TomTBT/pmc_open_access_xml · Datasets at Hugging Face

PMC10086387

36637433

[ "<title>Introduction</title>", "A question that we often get from laypeople and expert scientists alike is how sharks find their victim a mile away. The myths that sharks can detect blood and home in on their prey from large distances remain persistent despite efforts both in popular science (<uri xlink:href=\"https://www.youtube.com/watch?v=ugRc5jx80yg\">https://www.youtube.com/watch?v=ugRc5jx80yg</uri>) and in peer-reviewed work to dispel them (##UREF##25##Gardiner et al., 2012##; ##REF##20889825##Meredith and Kajiura, 2010##). Sharks do possess a nervous system that is exquisitely sensitive to chemicals in blood and can likely detect the blood of potential prey from a mile away. However, tracking resources based on their smell (odor tracking) is more challenging than just detecting an odor, because odor gradients are not preserved beyond the immediate vicinity of the odor source. The mechanisms of odor dispersal allow odors to be detected at long distances without providing directional cues because animals typically experience concentrated patches of odor followed by clean air (##BOX##0##Box 1##). This difference between detection and tracking is best quantified in the context of the champion smellers in the insect world – male moths. Males of many moth species can detect a single molecule of the female pheromone (##REF##3518584##Kaissling, 1986##). However, this exquisite sensitivity does not allow them to track down females from a kilometer away as suggested by earlier studies (##REF##5875169##Bossert and Wilson, 1963##; ##UREF##19##Collins and Potts, 1932##). Later work has demonstrated that it is hard for moths to locate females even 80 m away (##UREF##24##Elkinton et al., 1987##).", "Nevertheless, odor tracking is ubiquitous in the animal kingdom, albeit not over kilometers, and underpins many behaviors essential to an animal's survival. How does an animal go about finding the source of an odor in the absence of directional cues from odor concentration? The best source of directional information is wind direction. When wind direction is constant, flying upwind upon odor contact is an excellent strategy because the odor source is likely to be upwind. However, in the real world, wind direction is rarely constant (##REF##24413963##David et al., 1982##), which means that the present upwind direction and the direction of the odor source are not always the same (##UREF##15##Brady et al., 1989##).", "Thus, the problem confronting any animal performing odor tracking is how the sporadic detection of odor can be efficiently used to get closer to the source of the odor.", "Even under the best of circumstances, odor tracking itself only leads the animal to the vicinity of the source, and not directly to the source itself. There are various reasons for this. In the case of the moth, likely owing to the eddies under the tree, odor tracking cannot direct the insect to the source, just to the right tree (##UREF##18##Charlton and Cardé, 1990##); often not even that (##UREF##22##Doane, 1968##). Similarly, odor plumes emanating from a mammal can be as large as the entire animal, but a mosquito will still feed preferentially from specific body parts (##UREF##21##De Jong and Knols, 1996##). Long-range odor tracking is replaced by a different strategy – local search – near the source of the odor. For example, once odor tracking leads a male moth to the right tree, the moth flies vertically in the immediate vicinity of the tree, lands on the tree trunk and walks the last few centimeters to the female (##UREF##18##Charlton and Cardé, 1990##). Over short distances near the female, visual cues might play a role (##UREF##18##Charlton and Cardé, 1990##; ##UREF##21##De Jong and Knols, 1996##; ##UREF##22##Doane, 1968##). In some cases, such as flower feeding by moths, a conjunction between olfaction and vision is necessary for successful feeding (##UREF##54##Raguso and Willis, 2002##).", "So far, we have discussed the challenges of finding the location of an odor. Another equally difficult problem that animals must contend with is identifying an odor. The olfactory environment is complex and rich (##UREF##30##Herrmann, 2011##). Odors from the resource that an insect is seeking are mixed in with odors – sometimes closely related ones – from other sources. Insects must discriminate the odors from the resource from this complex mix (##REF##24970087##Riffell et al., 2014##). The behavior towards a given odor is also highly dependent on the state of the animal, such as feeding or mating status.", "In summary, odor modulation of locomotion is not a single behavior optimized to find the source of odor. Rather, it is a suite of behaviors that together ensure that animals can find and exploit resources critical to their survival (##FIG##0##Fig. 1##). Odor-guided locomotion requires exquisite sensitivity to multiple sensory systems, neural circuits to process and integrate sensory information, spatial memory, behavioral flexibility and the ability to act with incomplete information. Insects possess all these capabilities.", "In this Review, we will consider behavioral algorithms (see Glossary) underlying odor modulation of locomotion in insects and the neural circuits underpinning this behavior. We draw on research performed in various insects, but note that most of the work has done in moths, cockroaches and flies. This Review is divided into three sections. We start by reviewing behavioral algorithms that underpin different aspects of an insect's odor-tracking behavior, followed by a review of how the behavioral algorithm is implemented in the insect's brain. Finally, we review the neural circuits underlying odor identification and discuss future research avenues.\n\n" ]

[]

[ "<title>Conclusions and future work</title>", "Over the last few decades, much progress has been made in discovering the behavioral algorithms that underlie insects' behavioral response and their neural implementation. This progress provides a strong framework with which gaps in our knowledge can be approached.", "One deficit is the absence of the complete dataset required to understand olfactory behavior in nature: simultaneous tracking of the position of the animal along with the odor stimulus, wind direction and other sensory signals. With modern techniques to locate an insect's position (##REF##31266418##Knight et al., 2019##) and wireless electronics to sense the environment and measure electrical signals in real-time (##REF##23851198##Harrison et al., 2011##; ##REF##32601892##Pawson et al., 2020##; ##REF##23853229##Thomas et al., 2012##), it seems possible to study odor-guided locomotion in a natural environment, particularly in the context of large insects. These datasets, when combined with modern statistical methods (##REF##31600508##Datta et al., 2019##) for analyzing behavior and the relationship between neural responses and behavior, have the potential to not only illuminate odor-guided locomotion in detail, but also to contribute immensely to our understanding of the inner workings of the brain.", "Another rich area for future work is understanding the neural implementation of odor-guided behaviors in the brain. Here, recent progress in <italic toggle=\"yes\">Drosophila</italic> in generating genetic tools to probe specific neurons (##REF##33240047##Luan et al., 2020##), to activate and inactivate neurons (##REF##29618589##Simpson and Looger, 2018##), as well as large-scale datasets (##UREF##23##Dorkenwald et al., 2021##) that describe connectivity between neurons in the brain, enable progress in understanding the sensorimotor transformation at the level of single neurons. Finally, great strides have been made in introducing genetic tools in other insects (##REF##31494407##Mansourian et al., 2019##).", "In summary, we predict a productive future for a comparative approach to understanding odor-guided locomotion using large insects in field studies, through leveraging the power of genetic tools and neuroanatomy in <italic toggle=\"yes\">Drosophila</italic> and, finally, through the introduction of powerful genetic tools across other insect species." ]

[ "\n<bold>Competing interests</bold>\n", "The authors declare no competing or financial interests.", "<title>ABSTRACT</title>", "Odors released from mates and resources such as a host and food are often the first sensory signals that an animal can detect. Changes in locomotion in response to odors are an important mechanism by which animals access resources important to their survival. Odor-modulated changes in locomotion in insects constitute a whole suite of flexible behaviors that allow insects to close in on these resources from long distances and perform local searches to locate and subsequently assess them. Here, we review changes in odor-mediated locomotion across many insect species. We emphasize that changes in locomotion induced by odors are diverse. In particular, the olfactory stimulus is sporadic at long distances and becomes more continuous at short distances. This distance-dependent change in temporal profile produces a corresponding change in an insect's locomotory strategy. We also discuss the neural circuits underlying odor modulation of locomotion.", "<bold>Summary:</bold> This Review explores the behavioral and neural mechanisms that insects use to locate food and mates using their sense of smell." ]

[ "<title>Behavioral algorithms underlying odor modulation of locomotion</title>", "Understanding behavioral algorithms underlying odor modulation of locomotion is a formidable challenge; researchers have met this challenge with a range of behavioral paradigms (##BOX##2##Box 2##). We describe behavioral algorithms at two different spatial scales. We start with describing medium-range navigation to a source of odor. In this regime, the animal has detected an odor but does not know the source location and seeks to find this source. Then, we review near-range navigation during which the insect has either narrowed down the source considerably or has already found it and is taking the last few steps to engage with the source.\n", "<title>Medium-range navigation towards an odor source</title>", "The presence of a resource is often first signaled by the detection of odor, i.e. the resource is smelt before it is seen or touched – and the animal's initial response is influenced by odor alone. As the animal approaches the source, its behavior is affected by multi-modal integration. The distance at which behavior becomes multimodal depends on the species and the environment. Moreover, the processes described here are not specific to a single mode of locomotion, as the effect of odors on behavior is similar during both flight and walking (see ‘Local search’ section for details).", "<title>The reflexive cast-and-surge program</title>", "In both walking and flying insects, there are two conserved motor programs that aid in medium-range navigation to an odor source: the reflexive cast-and-surge program and the internally driven counter-turning. The reflexive cast-and-surge program consists of upwind locomotion or odor-gated anemotaxis (see Glossary); many insects either show little directional preference or walk/fly downwind in the absence of odor, but will travel upwind in the presence of odor (##REF##30129438##Alvarez-Salvado et al., 2018##; ##REF##16857884##Budick and Dickinson, 2006##; ##REF##9463918##Willis and Arbas, 1998##; ##REF##15695764##Willis and Avondet, 2005##; ##REF##10667968##Wolf and Wehner, 2000##). Odor-gated anemotaxis (##REF##4826172##Kennedy and Marsh, 1974##) consists of a two-component motor program where both components are sensorimotor reflexes (see Glossary) (##FIG##0##Fig. 1##). The first component is surge, in which contact with an odor results in rapid upwind movement; surge can be phasic or tonic (##REF##16857884##Budick and Dickinson, 2006##) depending on the species (##FIG##0##Fig. 1##A). The second component, cast, which occurs upon loss of odor, results in a cessation of upwind progress and execution of turns. In many, but not all, insects, these turns gradually widen, and between each turn the insect travels perpendicular to the wind direction.", "The cast-and-surge strategy and its origin as a sensorimotor reflex was first proposed by Baker and colleagues (##UREF##1##Baker, 1990##) based on a clever deduction; they realized that responses to pheromone (##UREF##6##Baker and Haynes, 1987##) and odor encounter rate (##UREF##3##Baker and Haynes, 1989##) had similar frequency. Measurement of odor contact during free flight in two moth species (##UREF##41##Mafra-Neto and Cardé, 1994##; ##REF##11607476##Vickers and Baker, 1994##) showed that contact with female pheromone led to an upwind surge with a ∼200 ms delay that lasted approximately 500 ms and terminated in a cast. Since these pioneering studies, the cast-and-surge strategy has been demonstrated in other flying insects (##REF##21957112##Dekker and Cardé, 2011##; ##UREF##64##Thiery and Visser, 1986##; ##REF##24440395##van Breugel and Dickinson, 2014##), and also during walking in both cockroaches (##UREF##9##Bell and Tobin, 1981##) and in <italic toggle=\"yes\">Drosophila</italic> (##REF##30129438##Alvarez-Salvado et al., 2018##)<italic toggle=\"yes\">.</italic> An iterative cast-and-surge strategy will bring the insect closer to the source of odor and also explains the difference in behavior under different stimulus conditions. In laminar plumes (see Glossary), the moth turns frequently and flies crosswind because each surge takes the insect out of the odor, and contact is only made after the moth turns around (##UREF##41##Mafra-Neto and Cardé, 1994##). In turbulent plumes (see Glossary), where the contact with odors is intermittent, the moth's trajectory is straighter owing to the fact that each contact with the odor results in a surge that is barely extinguished before the next odor contact is made, resulting in another upwind surge (##UREF##41##Mafra-Neto and Cardé, 1994##; ##UREF##42##Mafra–Neto and Cardé, 1995##). Strikingly, when pheromones are pulsed at a high enough frequency, even the tracks in a ribbon plume become straight because each surge ends in another odor stimulation, leading to another surge and completely extinguishing turns (##UREF##43##Mafra-Neto and Cardé##, ##UREF##41##1994##, ##UREF##42##1995##, 1996).", "<title>The internally driven counter-turning</title>", "The internally driven counter-turning requires odor for its expression (‘gating’) but is not a direct response to odor encounters; odors play a permissive rather than an instructive role. This motor program also has two components that are roughly analogous to cast and surge but have different mechanisms (##UREF##5##Baker et al., 1984##; ##REF##4826172##Kennedy and Marsh, 1974##; ##REF##1779417##Willis and Arbas, 1991##; ##UREF##71##Wright, 1958##) (##FIG##0##Fig. 1##). Equivalent to surge but not resulting from a direct contact with odor, the insect has straight flight segments during which it maintains constant ground speed and orientation in relation to wind direction (##UREF##20##David and Kennedy, 1987##; ##UREF##29##Haynes and Baker, 1989##; ##UREF##44##Marsh et al., 1978##; ##UREF##67##Von Keyserlingk, 1984##; ##UREF##69##Willis and Baker, 1994##; ##UREF##70##Willis et al., 1991##), reflecting visually guided anemotaxis (see Glossary). These straight segments are interrupted by crosswind turns that occur at remarkably regular intervals (##UREF##20##David and Kennedy, 1987##; ##UREF##29##Haynes and Baker, 1989##; ##UREF##67##Von Keyserlingk, 1984##), suggesting that they are generated internally (##REF##1779417##Willis and Arbas, 1991##) rather than being a consequence of discrete odor encounters. Odors also modulate this program: an increase in the number of odor encounters results in decreased speed (##UREF##6##Baker and Haynes, 1987##; ##UREF##35##Kennedy, 1983##; ##UREF##44##Marsh et al., 1978##; ##UREF##69##Willis and Baker, 1994##). In some moths, the frequency of counter-turning also increases as the moth approaches the odor source (##UREF##35##Kennedy, 1983##; ##UREF##40##Kuenen and Baker, 1982##; ##REF##1779417##Willis and Arbas, 1991##). Because speed decreases and the frequency of counter-turning increases as the insect approaches the source of odor, the crosswind excursions become smaller, giving the impression that the insect is homing in on the odor source (##UREF##44##Marsh et al., 1978##). In contrast, decreasing odor encounters leads to wider casts (##UREF##20##David and Kennedy, 1987##).", "<title>The contribution of motor programs to finding an odor source</title>", "The cast-and-surge motor program and the internally generated counter-turning are similar and might appear to be just a single motor program. Some authors have made a distinction between them based on the characteristics of the cross-wind movement, which they classified as either zigzagging or casting, casting being movement perpendicular to wind direction without any upwind progress and zigzagging being movement with upwind progress (##UREF##36##Kennedy et al., 1981##; ##UREF##53##Preiss and Kramer, 1986##). These differences could be real and significant; however, it is difficult to convincingly distinguish between the different mechanisms without quantifying the relationship between sensory stimulus and each turn – an important avenue for future research. Previous studies have emphasized the reflexive aspects of the tracking behavior over the internally generated program (##UREF##6##Baker and Haynes, 1987##; ##UREF##4##Baker and Vickers, 1997##; ##REF##16857884##Budick and Dickinson, 2006##; ##REF##24440395##van Breugel and Dickinson, 2014##) because they have focused on turns immediately after an odor encounter. However, the most parsimonious interpretation of these studies is that the reflexive cast-and-surge strategy is superposed on top of the internally generated counter-turning, and both are necessary to explain an insect's overall behavior; experiments aimed at testing whether this interpretation is correct constitute a particularly fruitful line for future research.", "Having both reflexive and internally driven counterturning would make odor tracking more robust. Tracking an odor plume, particularly in flight, is difficult. A recent study found that flies can only stay within a predictable, cylindrical plume for 500 ms (##REF##24440395##van Breugel and Dickinson, 2014##). Similarly, sensory delays of 200 ms typically associated with cast-and-surge strategies imply that an animal is always reacting to the past and not the present. Errors and delays are not debilitating when the wind direction is constant, because turning would lead the insect back into the plume, as the insects exit the plume mostly because of misalignment with the upwind direction. However, in realistic plumes with variable wind direction and speed, turning back does not ensure odor encounter, and the likely existence of long intervals during which there is no odor contact makes an internally generated strategy necessary. A long-lasting strategy with frequent changes of direction is more likely to result in contact with odor because the insect will end up re-encountering the plume by chance. Slowing down as encounters become more frequent would increase the chance that insects would stay close to the plume; this slow down close to an odor source has been observed in flies (##REF##29146771##Saxena et al., 2018##).", "The idea that a reflexive strategy works well in predictable conditions and internally generated counter-turning performs better in a more unpredictable environment is supported by modeling studies (e.g. ##UREF##7##Belanger and Willis, 1996##).", "<title>Other mechanisms in medium-range odor tracking</title>", "Another important conclusion from the ##UREF##7##Belanger and Willis (1996)## study is that the known mechanisms of odor tracking did not come close to the performance of the actual moth, demonstrating that there are additional mechanisms at play. Precise control over odor stimulation, detailed analysis of an insect's tracks and generative models (see Glossary) to assess how well behavior is understood in walking <italic toggle=\"yes\">Drosophila</italic> have led to the discovery of these mechanisms. A recent study, which took advantage of optogenetic stimulation to create a precise pattern of olfactory stimulation, showed that activating a fly's olfactory system did not change the fly's propensity to turn while exiting an odorized area (##REF##32226007##Tao et al., 2020##). Rather, flies slowed down as they exited the odor plume, giving the impression that there is increased turning at the border; the turns made at the border of the odorized area were much larger. That study also found that there are kinematic changes associated with olfactory stimulation that cause the flies to slow down in the stimulus and increase its speed outside the stimulus region. Another recent study that replicated turbulent plumes with more precise stimulus control than in previous experiments demonstrated that the fly's behavior is much better modeled as stochastic than as a pure sensorimotor reflex (##REF##33140723##Demir et al., 2020##). Moreover, that study showed that odor encounters modulated the stop-to-walk transition, an important movement characteristic. In flies, a recent study also found that odors affect multiple aspects of locomotion (##REF##26439011##Jung et al., 2015##). Recent advances in machine vision and statistical techniques will help us to make progress in discovering mechanisms by which odors affect locomotion, and how the entire ensemble of mechanisms helps insects to approach the odor source.", "<title>Local search near the odor source and harvesting the resource</title>", "The mechanisms described above operate when the insect is far from the odor source. Often, the insect's behavior changes close to the source: a male moth reacting to female scent, after flying upwind and reaching the right tree, performs vertical flights to find the correct landing spot, lands on the tree and performs a local search by walking, and finally makes contact with the female (##UREF##18##Charlton and Cardé, 1990##). A similar behavioral transformation is observed – this time in flight – as the moth approaches a flowering plant (##UREF##54##Raguso and Willis, 2002##). This time, the moth hovers over the flower. Mosquitoes, too, change their behavior as they approach their host. Far from the odor source (>10 m), it is driven primarily by detection of CO2, and close to the odor source (<10 m), it is driven by a combination of vision and odor (##REF##26190071##Van Breugel et al., 2015##) before landing and searching. Sandflies land non-preferentially on their host – a mammal – but then move to a region with less hair, such as the ears or eyelid, to feed (##REF##3404541##Coleman and Edman, 1988##). Even for insects that just walk, the strategy changes as the animal approaches the odor source (##REF##10667968##Wolf and Wehner, 2000##). Regardless of whether the locomotion mode changes, there can be a behavioral switch. Both the nature of the behavioral change and where it occurs (how far from the odor source) depends on the species, environmental conditions, the density of available resources and other factors (##UREF##18##Charlton and Cardé, 1990##; ##REF##16272245##Wolf and Wehner, 2005##). In this section, we describe the behavior near the odor source; the insect's objective has changed from approaching the odor source to locating, assessing and utilizing the resource it signals.", "One change is that the insect's locomotion strategy is altered into a local search strategy, likely in response to the stimulus becoming more continuous and/or other sensory modalities, such as vision and taste, that are also present, representing a resource patch (see Glossary) (##FIG##0##Fig. 1##). Local search in insects was first discovered in blowflies, which change their locomotion to a local search after feeding on sugar, and this was thought to be initiated by resource utilization (##REF##17794437##Dethier, 1957##; ##UREF##47##Murdie and Hassell, 1973##; ##UREF##66##Vinson, 1977##). A similar local search pattern is also observed on encountering resource-specific cues such as food odors or sex pheromones (##REF##26439011##Jung et al., 2015##; ##UREF##56##Sabelis et al., 1984##).", "Just like medium-distance navigation to the odor source, local search is not a single motor program but a constellation of mechanisms that result in the animal being restricted to a given area. One mechanism is looping (or spiraling), which involves an increase in the animal's turn rate, with the animal maintaining a turn bias (see Glossary) in a single direction, resulting in looping trajectories that bring the animal back to the same location, essentially circling the resource (##REF##24420602##Beevers et al., 1981##; ##UREF##56##Sabelis et al., 1984##). Another mechanism is a decrease in run length or in the distance between each subsequent stop. This has been observed in bumblebees (##REF##28309608##Heinrich, 1979##) and honeybees in a patch of flowers (##UREF##58##Schmid-Hempel and Schmid-Hempel, 1986##), and in flies in response to odor alone (##REF##26439011##Jung et al., 2015##) (##FIG##0##Fig. 1##).", "A mechanism that has received particular attention is turning back into the resource patch when the patch border (see Glossary) is encountered. Unlike spiraling or changes in run length, turning back requires a sense of direction. Decreasing odor concentration can serve as a directional cue that can be sensed by simultaneously comparing concentration at two locations. Because olfactory receptors are present within the insect's antennae – elongated, jointed sense organs that are attached to the insect's head – comparison of odor concentrations across two locations to turn towards the side that experiences the higher concentration is possible (##UREF##13##Borst and Heisenberg, 1982##; ##REF##19576769##Duistermars et al., 2009##; ##UREF##45##Martin, 1965##). Odor concentration at two locations can also be measured sequentially by simply walking to different locations (##UREF##10##Bell and Tobin, 1982##; ##REF##25987729##Lockey and Willis, 2015##); this computation requires short-term memory. Moreover, insects successfully turn at the border using a large increase in turn amplitude even when the patch abruptly ends and there is little scope for them to evaluate concentration (##UREF##56##Sabelis et al., 1984##; ##UREF##68##Waage, 1978##). In <italic toggle=\"yes\">Drosophila</italic>, a large decrease in speed is coupled with an increase in turn amplitude (##REF##32226007##Tao et al., 2020##).", "The local search mechanisms can be elicited by other sensory modalities such as gustation (##UREF##46##Mayor et al., 1987##; ##UREF##52##Nelson, 1977##) or vision (##UREF##11##Bell et al., 1983##; ##REF##28311117##Lawrence, 1982##) alone, which suggests that local search mechanisms can utilize the sensory modality that provides the most salient stimulus. In contrast to changes in locomotor strategy, acceptance or rejection of a resource such as food, oviposition site or mate often requires a conjunction of multiple sensory modalities (##FIG##0##Fig. 1##). The synergism between vision and olfaction is important for locating the odor source and landing (##REF##12547939##Frye et al., 2003##; ##REF##29146771##Saxena et al., 2018##; ##REF##20472776##Stewart et al., 2010##; ##REF##26190071##Van Breugel et al., 2015##; ##REF##31327719##Vinauger et al., 2019##). Similar multimodal interactions are observed in oviposition (##UREF##28##Harris and Miller, 1982##; ##UREF##60##Spencer et al., 1999##), initiation of feeding (##UREF##54##Raguso and Willis, 2002##; ##REF##33475852##Wheelwright et al., 2021##) and courtship (##REF##19214231##Krstic et al., 2009##; ##REF##22645338##Pan et al., 2012##).", "As summarized in ##FIG##0##Fig. 1##, odor modulation of locomotion involves distance-dependent locomotor strategies. At each distance, a whole suite of changes in locomotion characterizes changes in behavior. As described above, different insects employ these strategies to different extents, and the details of a given strategy would also differ from insect to insect. Unraveling all the behavioral strategies employed, how these strategies are deployed based on current sensory conditions and how differences in behavior between insects reflect adaptation to their ecological niche are all important avenues for future research.", "<title>Neural mechanisms underlying odor modulation of locomotion</title>", "The behaviors described above require many computational abilities: one is to process and integrate information from different sensory modalities, called multimodal integration (see Glossary). Odor information is combined with wind direction and full-field visual signals, such as optic flow, to navigate towards the odor from large distances (##REF##18581182##Cardé and Willis, 2008##). Near the odor source, visual recognition of small objects is combined with other sensory cues to land on the object if the animal navigates to the odor in flight (##UREF##54##Raguso and Willis, 2002##). Gustatory, visual and mechanosensory information is combined with olfactory information to decide whether to accept or reject the resource. A second ability is memory – both spatial and episodic. Spatial memory is required to keep track of one's position in space to direct the next movement, whereas episodic memory is necessary to recall past odor encounters and make decisions based on odor history (##REF##27594700##Ache et al., 2016##; ##REF##30381430##Baker et al., 2018##; ##REF##29432454##Pang et al., 2018##). Finally, behavior depends on other circumstances, such as an animal's risk assessment and its own state and motivation. In the following sections, we will discuss these three abilities in insects, and how they aid or limit an insect's ability to locate and utilize resources. It is important to note that these neural circuits are conserved enough across insects (##REF##24559671##Ito et al., 2014##; ##REF##21963552##Martin et al., 2011##) that, despite some differences, the basic computation and logic are similar; therefore, in discussing the role of different circuits, we draw on research across insects.", "<title>Unimodal sensory processing of odors, wind and photons</title>", "The basic circuit that senses and processes olfactory information is described in ##BOX##3##Box 3##. Odors are detected by olfactory receptor neurons (ORNs; see Glossary); a large number of ORNs converge onto a single second-order neuron called a projection neuron (PN).\n", "<title>Convergence increases the sensitivity to odors</title>", "The sensitivity of individual ORNs and the convergence from ORNs to PNs allows insects to detect odors at low concentration with short latency. Estimates suggest that a single moth pheromone molecule can produce a change in firing rate in an ORN that is specific to pheromones (##REF##3518584##Kaissling, 1986##). Even when ORNs are not specific to a single odor, they can still be sensitive to odors (##REF##16615896##Hallem and Carlson, 2006##; ##REF##20435004##Olsen et al., 2010##). An insect's ability to detect odors is further enhanced through convergence from the ORNs to the PNs, which provides a mechanism for amplification (##REF##19684589##Kazama and Wilson, 2009##). In <italic toggle=\"yes\">Drosophila</italic>, 40 to 100 ORNs project to the same glomerulus (see Glossary); each ORN synapses on each uniglomerular PN (uPN) (##REF##19684589##Kazama and Wilson, 2009##), which results in an amplification of weak odor responses (##REF##17922008##Bhandawat et al., 2007##; ##REF##20435004##Olsen et al., 2010##). Convergence also shortens the latency to detect an odor, an important consideration when tracking odors in an ever-changing environment (##REF##26586183##Jeanne and Wilson, 2015##).", "There is additional circumstantial evidence that convergence is an important mechanism for increasing odor sensitivity (##REF##22153368##Hansson and Stensmyr, 2011##). In many insects, the antennae are highly branched to accommodate thousands of pheromone-sensitive sensilla (##REF##18620256##Keil, 1989##; ##REF##29429617##Nishino et al., 2018##), presumably to increase sensitivity. Moths also have a sexually dimorphic macroglomerular complex (##UREF##38##Koontz and Schneider, 1987##), a set of glomeruli that process sex pheromones, that is enlarged in males (##UREF##12##Boeckh and Boeckh, 1979##; ##REF##1598574##Hansson et al., 1992##); similar expansion is also observed in drosophilid flies (##UREF##37##Kondoh et al., 2003##). The increased glomerular size is likely related to an increase in ORN numbers, a phenomenon also observed for ORNs involved in the detection of other non-pheromonal volatiles. Two examples include the expansion of ORNs that detect a specific food source in the specialist <italic toggle=\"yes\">D. sechellia</italic> compared with the generalist <italic toggle=\"yes\">D. melanogaster</italic> (##REF##16401429##Dekker et al., 2006##), and in mosquitoes (##REF##19858490##Syed and Leal, 2009##).", "<title>Comparison of odor concentrations at different body parts</title>", "As discussed above, it is unlikely that an instantaneous concentration comparison between ORNs in different parts of the body such as the two antennae plays a large role in odor tracking over long distances. However, instant comparison appears to play a crucial role in trail tracking across the animal kingdom (##UREF##27##Hangartner, 1967##; ##REF##16456082##Rajan et al., 2006##; ##UREF##62##Takasaki et al., 2012##) and is involved in determining the borders of a resource patch (##UREF##8##Bell, 1985##). Concentration comparison can be crucial under conditions in which there are sharp odor gradients, but it does not appear to be the only mechanism (##REF##32226007##Tao et al., 2020##). There are several neural mechanisms that can extract and accentuate local concentration differences at the two antennae. In <italic toggle=\"yes\">Drosophila</italic>, where most ORNs project bilaterally, the PNs can differentiate between ipsilateral and contralateral ORNs, likely based on the different axon lengths of the ipsilateral and contralateral ORN axons, which result in a time difference between signals from the two antennae reaching PNs (##REF##23263180##Gaudry et al., 2013##). In both moths and cockroaches, a more elaborate architecture, whereby pheromone-related ORNs in different parts of the antennae project to small sub-regions of the glomerulus, exists to take advantage of different spatial patterns of odors (##REF##29429617##Nishino et al., 2018##). PN responses, too, were responsive to the location of the odor stimulus on the antennae. This topographical arrangement appears to be maintained in higher-order olfactory circuits and, in principle, can create a map of instantaneous pheromone concentrations. Whether an instantaneous map of the local distribution of pheromone concentration (or other odors) is created and how these instantaneous comparisons are employed in driving behavior is an important avenue for future investigation.", "<title>Contribution of mechanosensation and vision to odor-guided behaviour</title>", "We will only discuss mechanosensation and vision briefly as these modalities have been covered in greater detail in other reviews (##UREF##14##Borst et al., 2020##, ##REF##20225934##2010##; ##UREF##39##Krishnan and Sane, 2015##; ##REF##25032498##Silies et al., 2014##). We will first discuss mechanosensation (##FIG##1##Fig. 2##B). Detecting the direction of airflow is critical for long-range odor tracking as it provides important directional cues. Neurons in the antennal lobe can themselves be responsive to airflow through projections of mechanosensory hairs or the responses of ORNs to mechanosensory stimuli (##REF##7884038##Anton and Hansson, 1994##; ##REF##11195281##Galizia et al., 2000##; ##UREF##26##Han et al., 2005##). However, the specialized mechanoreceptors for detecting airflow are found in the Johnston's organ in insect antennae (##REF##17444491##Ai et al., 2007##; ##REF##19279630##Kamikouchi et al., 2009##, ##REF##16998934##2006##; ##UREF##59##Schneider, 1964##; ##REF##19279637##Yorozu et al., 2009##). These receptors are highly sensitive to airflow; <italic toggle=\"yes\">Drosophila</italic> can behaviorally respond to air speeds as low as 0.5 cm s−1, a flow rate that is well within speeds described as ‘calm’ by humans (##REF##19279637##Yorozu et al., 2009##). The information from the two antennae are combined to decode the direction of wind (##REF##30948249##Suver et al., 2019##). Flies pick a heading with respect to the direction of airflow and can respond to changes in direction with changes in heading (##REF##33377868##Currier et al., 2020##; ##REF##32681825##Okubo et al., 2020##). Nevertheless, work is needed to assess how well insects can disambiguate exogenous airflow from motion-generated airflow. It is also unknown how well insects can assess the mean wind direction in a natural environment with variable wind speed and direction.", "Next, we will discuss vision. Two kinds of visual information are important in odor-guided locomotion (##FIG##1##Fig. 2##C). The first kind is wide-field motion created by self-motion; as the animal moves, the world moves past it. This pattern of movement is critical for controlling speed and assessing whether one is going straight or turning and for stabilizing flight paths (##REF##25116140##Borst, 2014##; ##REF##23269913##Egelhaaf et al., 2012##; ##REF##21689925##Srinivasan, 2011##, ##UREF##61##2014##; ##UREF##63##Taylor and Krapp, 2007##). Wide-field information is carried by lobula plate tangential cells (LPTCs) (##FIG##1##Fig. 2##C). LPTCs project to multiple regions in the brain, including the superior slope, where visual and olfactory information is integrated to generate motor commands. The activity of the LPTCs themselves is modulated by odors (##REF##25619767##Wasserman et al., 2015##); LPTC responses are amplified in the presence of odors, which is likely important for a correct orientation into the wind during the surge. A second type of visual information critical to behavior is the detection of visual features in the environment, such as the long vertical shapes resembling a tree, or detecting a small object as a conspecific. Information about visual features is carried by another set of neurons called the lobula columnar neurons (LCs) (##FIG##1##Fig. 2##C). A comprehensive analysis in <italic toggle=\"yes\">Drosophila</italic> has revealed that there are 22 LCs that encode different visual features and likely play an important role in olfactory behavior (##REF##28029094##Wu et al., 2016##) that is directed at an object. LCs directly interact with motor pathways and mediate visuo-motor behaviors (##REF##32822613##Bidaye et al., 2020##; ##REF##33217639##Cheong et al., 2020##; ##REF##29943730##Namiki et al., 2018a##); LC inputs are also integrated with other inputs in the posterior part of the brain. Through mechanisms that are not well understood, neurons downstream of the LCs likely play an important role in integrating visual information about objects with their smell to drive behavior.", "Other regions important for odor-guided behavior such as the mushroom body and lateral horn also receive visual inputs (see below). Many different streams of visual information into the central complex, a region of the brain important for computing an insect's spatial orientation, are likely to exist because neurons in the central complex are responsive to different kinds of visual information including self-motion (##REF##34696823##Hulse et al., 2021##; ##REF##34912112##Lyu et al., 2022##; ##REF##28988858##Stone et al., 2017##). The neural pathways that carry visual information related to self-motion – wide-field visual information such as optic flow – into the central complex are currently unclear, but are under investigation.", "<title>Higher-order olfactory processing and multi-modal integration</title>", "PNs from the antennal lobe project to two higher-order processing centers, the mushroom body and the lateral horn (##REF##19737085##Galizia and Rössler, 2010##; ##REF##17072827##Kirschner et al., 2006##; ##REF##21963552##Martin et al., 2011##; ##REF##19706282##Masse et al., 2009##), although minor connections to other protocerebral regions (see Glossary) also exist (##REF##25535794##Aso et al., 2014b##; ##REF##22592823##Tanaka et al., 2012##). Both the mushroom body and lateral horn are centers for multi-modal integration and participate in an array of computations through their multimodal input and through connections to other higher brain centers (##FIG##0##Fig. 1##B).", "<title>Integration at the mushroom body</title>", "The major sensory input into the mushroom body in many insects is from PNs; in flies, only excitatory PNs provide input into the mushroom body, whereas the situation for other insects has not been investigated (##REF##32619485##Bates et al., 2020##). The mushroom body also receives inputs from other sensory modalities, encoding information about temperature (##REF##25739506##Frank et al., 2015##; ##REF##25739502##Liu et al., 2015##), humidity (##REF##32619476##Marin et al., 2020##), taste (##REF##25878268##Kirkhart and Scott, 2015##; ##REF##25981787##Masek et al., 2015##), visual stimuli (##REF##12210130##Ehmer and Gronenberg, 2002##; ##REF##10376745##Li and Strausfeld, 1999##) and mechanical stimuli (##REF##10376745##Li and Strausfeld, 1999##). The relative importance of these inputs depends on the taxa: cockroaches receive more mechanosensory input, whereas bees receive more visual input (##REF##23080415##Menzel, 2012##). These sensory inputs interact with the main local neurons of the mushroom body called the Kenyon cells in a region of the mushroom body called the calyx; the axons of the Kenyon cells project to the lobes, which are segmented into processing units. Each segment receives input from a subset of dopaminergic neurons and outputs to a subset of mushroom body output neurons (##REF##25535793##Aso et al., 2014a##; ##REF##19152379##Strausfeld et al., 2009##). The input–output relationship between Kenyon cells that carry input sensory information and mushroom body output neurons that carry output behavioral messages is modified by signals from dopaminergic neurons to affect learning (##REF##21963552##Martin et al., 2011##; ##REF##11166636##Menzel and Giurfa, 2001##; ##REF##32283995##Modi et al., 2020##). This neural architecture is perfect for associating odors with other events in the world.", "However, associating odors with events is not the only role of the mushroom body in odor-guided behavior. Both the dopaminergic neurons and the output neurons interact with premotor circuits and with output neurons from the lateral horn (##REF##25535793##Aso et al., 2014a##; ##REF##31112130##Dolan et al., 2019##; ##REF##34032214##Schlegel et al., 2021##), and are in the correct place in the circuit to perform sensorimotor transformations including those involved in odor-guided locomotion. When processing in mushroom body is blocked, either by chemical ablation or through genetic methods, it leads to elevated locomotor activity in flies (##REF##10454382##Martin et al., 1998##), crickets and grasshoppers (##UREF##32##Huber, 1974##). Activating individual mushroom body output neurons can produce attraction or repulsion to odors (##REF##25535794##Aso et al., 2014b##) and also promote upwind movement when activated (##REF##34983933##Matheson et al., 2022##). Similarly, manipulating dopaminergic signaling in the mushroom body of flies can affect movement on a trial-by-trial basis (##REF##31230716##Handler et al., 2019##; ##REF##34697455##Zolin et al., 2021##). It has been hypothesized that in a complex environment with multiple odor sources, the mushroom body can tie together inputs from PNs that are activated at the same time, allowing disambiguation of different olfactory stimuli (##UREF##2##Baker and Hansson, 2016##).", "<title>Sensory integration at the lateral horn</title>", "The circuit architecture of the lateral horn is strikingly different from that of mushroom body. The lateral horns in all insects studied thus far receive inputs from all PNs (##REF##19737085##Galizia and Rössler, 2010##); in flies, this includes the excitatory PNs that also project to the mushroom body and the inhibitory PNs (##REF##32619485##Bates et al., 2020##; ##REF##34032214##Schlegel et al., 2021##). The lateral horn also receives input from other sensory modalities, including gustation, mechanosensation, thermosensation and vision (##REF##33475851##Chakraborty and Sachse, 2021##), as well as from the mushroom body (##REF##31112130##Dolan et al., 2019##; ##REF##34032214##Schlegel et al., 2021##). Unlike the mushroom body, which is segmented into clear and well-defined processing units, the lateral horn is a diffuse neuropil (##REF##9057110##Sun et al., 1997##; ##REF##14556288##Yasuyama et al., 2003##), and the underlying computational logic is not obvious. The connectivity pattern between projection neurons, the intrinsic and output neurons of the lateral horn, is stereotyped enough that the same neurons (similar anatomy, connections and responses) can be identified across animals (##REF##32619485##Bates et al., 2020##; ##REF##23615618##Caron et al., 2013##; ##REF##29909998##Jeanne et al., 2018##; ##REF##17382886##Jefferis et al., 2007##; ##REF##34032214##Schlegel et al., 2021##). Based on this connectivity pattern, the lateral horn consists of ∼500 cell types in <italic toggle=\"yes\">Drosophila</italic> compared with only 15 types of Kenyon cells (##REF##34032214##Schlegel et al., 2021##). There are also more than 37 types of output neurons. Although there is some disagreement among different studies, neurons in the same morphological class have similar odor–response profiles (##REF##31112127##Frechter et al., 2019##; ##REF##29909998##Jeanne et al., 2018##), once again highlighting the stereotyped nature of the circuit.", "There is some evidence that the lateral horn can function as a site for computing odor valence, i.e. whether an odor is attractive or repulsive (##REF##25512254##Strutz et al., 2014##), or as a site for encoding odors based on chemical structure (##REF##31112127##Frechter et al., 2019##). However, there is hardly any consensus regarding the fundamental computations performed in the lateral horn. The lateral horn output neurons project to different regions of the protocerebrum, where they interact with outputs from the mushroom body and with premotor circuits (##REF##34032214##Schlegel et al., 2021##). Given that the lateral horn receives multisensory input from the mushroom body and downstream motor areas, it is unlikely that the lateral horn functions purely as a center for integration of olfactory input (##REF##33475851##Chakraborty and Sachse, 2021##; ##REF##21963552##Martin et al., 2011##; ##REF##34032214##Schlegel et al., 2021##). This conclusion is supported by a recent comprehensive analysis of the anatomy of the lateral horn in <italic toggle=\"yes\">Drosophila</italic>, which found that many lateral horn neurons receive more feedback input from motor areas than feedforward sensory inputs (##REF##34032214##Schlegel et al., 2021##).", "The lateral horn appears to play an important role in many innate behaviors driven by ecologically important stimuli. For example, the behavioral responses of <italic toggle=\"yes\">Drosophila</italic> to CO2, which is sensed by a single ORN class, appear to be completely mediated by the lateral horn (##REF##30653496##Varela et al., 2019##); the behavioral response to geosmin, an odor that signals harmful microbes, is another example (##UREF##33##Huoviala et al., 2020## preprint). In the context of a moth's behavioral response to pheromones, a region adjacent to lateral horn, often referred to as inferior lateral protocerebrum, is a site where inputs from monoglomerular PNs, multiglomerular PNs and inhibitory PNs are integrated (##REF##9318892##Anton et al., 1997##; ##REF##12588734##Kanzaki et al., 2003##; ##REF##18723543##Kárpáti et al., 2008##, ##REF##20044934##2010##; ##REF##30723902##Lee et al., 2019##). One hypothesis is that this integration is important to differentiate between individual pheromone components versus a blend. Alternatively, different kinetics of the neural response and different axonal lengths of these PNs might provide important information about the stimulus (##REF##30723902##Lee et al., 2019##). In most insects, the lateral horn is also a site for integration of information from the two antennae (##REF##22153368##Hansson and Stensmyr, 2011##). Finally, some of the integration of odor inputs with wind and visual input also occurs in the lateral horn (##UREF##2##Baker and Hansson, 2016##; ##REF##34032214##Schlegel et al., 2021##).", "In total, the mushroom body and the lateral horn are not just centers for olfactory integration; rather, they are highly recurrent circuits for sensorimotor transformation. How these two regions of the brain interact with downstream motor circuits to control behavior is an important avenue for future research.", "<title>Circuits integrating spatial information with sensory input to produce motor commands</title>", "The spatial context for orientation and navigation is computed in the central complex, which is a collection of central brain neuropils. Many recent reviews describe the computation performed in the central complex (##REF##30205054##Heinze et al., 2018##; ##REF##34696823##Hulse et al., 2021##; ##REF##24160424##Pfeiffer and Homberg, 2014##; ##REF##27269718##Turner-Evans and Jayaraman, 2016##; ##REF##27436729##Webb and Wystrach, 2016##). In brief, two of the central complex neuropils, the ellipsoid body and the protocerebral bridge, record the current heading. The central complex also receives direct information related to wind direction (##REF##33377868##Currier et al., 2020##; ##UREF##31##Homberg, 1994##; ##REF##34983933##Matheson et al., 2022##; ##REF##32681825##Okubo et al., 2020##; ##UREF##55##Ritzmann et al., 2008##), which allows it to reference internal representations to external directional stimuli such as wind direction; insects use the central complex to orient to airflow (##FIG##0##Fig. 1##B). Silencing fan-shaped body neurons – neurons within a sub-region of central complex – affects the ability of flies to make corrective turns with respect to the wind (##REF##33377868##Currier et al., 2020##).", "The lateral accessory lobe receives information regarding both orientation and pheromones (##REF##15593336##Seki et al., 2005##) through medial protocerebral neurons that, in turn, receive input from the lateral horn (##UREF##51##Namiki et al., 2014##). Many descending neurons (DNs) receive input from the lateral accessory lobe (##FIG##1##Fig. 2##D). These DNs, therefore, have much of the information needed to send navigation-related motor commands, and many are responsive to pheromones (##UREF##34##Kanzaki et al., 1994##). An interesting property of these neurons in the moth is that they are bistable; thus, they are referred to as flip-flop neurons (##UREF##34##Kanzaki et al., 1994##). Each state lasts up to 30 s, with state transitions being mediated by a new stimulus. Thus, these flip-flop neurons have the correct properties necessary to mediate an insect's behavior, including the internally generated counter-turns that are non-reflexive. Pheromone-sensitive DNs also originate from a region of the brain called the posterior slope. These DNs receive pheromone-related information directly from the medial protocerebrum. At least in the case of moth pheromones, these DNs have a phasic response to pheromones (##REF##29311619##Namiki et al., 2018b##) and are likely responsible for mediating stimulus-triggered responses such as the phasic surge response or the turn response to odor.", "Much remains to be discovered in terms of which DNs respond to odor stimuli and the relationship between DNs and behavior. Nevertheless, studies seeking to model plume tracking show that turns driven by the flip-flopping neurons can serve as a mechanism for odor tracking (##UREF##0##Adden et al., 2022##; ##REF##23385386##Ando et al., 2013##). In these two studies, outputs of flip-flopping neurons were used to guide turns; two mutually inhibiting flip-flop neurons drive turns on each side of the body. Such a simple system appears to replicate the moth's odor-tracking behavior.", "<title>Identification of odor and identity-dependent behavior</title>", "Thus far, our Review has focused on the neural mechanisms involved in locating the odor source. Another important problem is identifying the odor, a task for which the olfactory system is optimized (##BOX##3##Box 3##). Odor discrimination is essential for associative learning and has been covered in detail elsewhere (##REF##12415296##Laurent, 2002##; ##REF##19706282##Masse et al., 2009##; ##REF##19804753##Su et al., 2009##; ##REF##23841839##Wilson, 2013##).", "Odor discrimination is also important for instantaneous behavioral decisions. One theme that has emerged in this regard is that many ORNs are specialists and respond specifically to a single ecologically relevant odor. These odors are important for a range of odor-gated behaviors, such as courtship (##REF##18988843##Dickson, 2008##), aggregation, food avoidance and approach, aggression and choice of substrate for egg laying (##REF##27752072##Anderson, 2016##; ##REF##29940518##Aranha and Vasconcelos, 2018##). An important idea is that these specialist ORN classes function as a ‘labeled line’, where they signal to a few dedicated neurons at each processing stage to connect odors to specific behaviors. Recent electron microscopic reconstruction of the <italic toggle=\"yes\">Drosophila</italic> olfactory circuit shows that, particularly at the level of the lateral horn and beyond, the signals from the specialist ORN classes diverge to many downstream neurons (##UREF##33##Huoviala et al., 2020## preprint). This divergence makes sense because most ecologically important behaviors are both multimodal and plastic – properties that require extensive integration.", "Moth sex pheromones are also specialist odors. A major component of most moth pheromones, bombykal (##UREF##2##Baker and Hansson, 2016##), activates a single ORN type with high specificity. In many moth species, odor-tracking behavior is elicited by a specific blend of odors in the correct ratio rather than by a single compound (##REF##18452043##Baker, 2008##; ##REF##26462937##Berg et al., 2014##; ##UREF##50##Mustaparta, 1997##; ##UREF##65##Vickers et al., 1991##; ##REF##12184401##Vickers, 2002##), a characteristic that is important in ensuring that a male is tracking only its conspecific. One question is whether a moth waits for the exact blend or whether aspects of the behavior can be triggered by a non-optimal blend. Existing data suggest that even in moth species in which the full tracking program relies on the exact blend, this requirement is less stringent for certain aspects of the behavior, such as initiation of upwind flight (##REF##12184401##Vickers, 2002##). Moreover, addition of pheromone components from a closely related species affects some aspects of the tracking motor program (##UREF##50##Mustaparta, 1997##; ##REF##12184401##Vickers, 2002##; ##REF##26300751##Wu et al., 2015##) while leaving others intact. These data suggest that odor tracking is not organized as a unitary behavior; rather, it is a result of parallel sensorimotor loops that connect activity in known ORNs to aspects of the overall behavior.", "The question of whether odor modulation of locomotion is composed of independent sensorimotor loops was addressed in targeted experiments designed to ask how different combinations of active ORNs affect a fly's locomotion (##REF##26439011##Jung et al., 2015##). The authors created an arena in which a known combination of ORNs could be activated, and found that each ORN class only affects a subset of locomotor behaviors. These results are best interpreted as a sensory-motor transformation between active ORN classes and the eventual behavior. As an example, they found that activating just one ORN class – one containing the <italic toggle=\"yes\">Or42b</italic> receptor – affects the run duration. However, a combination of multiple active ORNs is essential to change the propensity to turn sharply. Thus, each combination of active ORN classes can be thought of as a sensory-motor feature that affects a particular aspect of locomotion, a conclusion supported by another recent study (##REF##34983933##Matheson et al., 2022##). The olfactory circuits – particularly those in the lateral horn – are tailor-made to make these sensory motor transformations." ]

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[ "<fig position=\"float\" id=\"JEB200261F1\"><label>Fig. 1.</label><caption><bold>Insects employ distance-dependent locomotor strategies.</bold> (A) An insect can sense odors a long distance away. Insects employ distance-dependent strategies to find the resource, assess it and accept it. Here, an insect flying to the odor source comes near it and changes its strategy to local search before assessing the resource. Some of the behavioral strategies are shown. (B) The behavioral strategy changes, in part, because the odor profile changes from patchy (gray shading) at long distances to continuous near the odor source. In response, the behavioral strategies are different as well. Far from the odor source, insects use long-range strategies. Two of these strategies are caste-and-surge and internally generated counter-turning. When using the caste-and-surge strategy, insects surge upwind on encountering an odor, and perform frequent turns perpendicular to wind direction after losing the odor. Internally generated counter-turning is similar to caste-and-surge; the main difference is that behavior is driven by an internal program and not by odor encounters. Odor detection activates this behavior. Closer to the odor source, the insect aims to stay close to the odor source through a variety of local search strategies. These changes in behavior – including correlated turning, increased turn angle, and short runs punctuated by changes in direction – have the effect of keeping the insect close to the odor source. Finally, insects assess the resource and choose to accept or reject it. This assessment depends on other modalities including vision, taste and touch.</caption></fig>", "<fig position=\"float\" id=\"JEB200261F2\"><label>Fig. 2.</label><caption><bold>Circuits underlying odor-guided locomotion.</bold> (A) Regions of the brain important for olfactory processing (green). Odors are detected by neurons in the antenna. These neurons project to the antennal lobe (AL). Projection neurons from the antennal lobe project to mushroom body (MB) and lateral horn (LH). (B) Airflow (in magenta) information is also important for odor-guided behavior. Airflow is detected by the Johnston organ (JO) neurons in the antenna; through various intermediate centers, such as antennal mechanosensory and motor centers (AMMC) and Wedge (abbreviated as WED), these neurons connect to central complex (CC) neuropils to allow insects to orient themselves with respect to airflow. (C) Visual (in blue) information is also important for odor-guided behavior. Two parallel streams of visual information – wide-field information, such as that arising from motion, and feature detectors – are important for odor-guided behavior. The lobula (Lo) and lobula plate (Lp) are important visual processing centers. (D) Flow of information underlying odor-guided locomotion. Neuropils in green, blue and magenta are largely unimodal sensory processing centers that process olfactory, visual and mechanosensory information. Many central brain regions (marked with striped color) such as MB, LH and the superior medial protocerebrum (SMP) play an important role in multi-modal integration through connections from multiple sensory systems and recurrent connections between each other. Motor commands originate from the lateral accessory lobe (LAL) and from the superior slope (SS). Motor regions are marked with a red border. Descending neurons (DNs) carry motor-related information from the brain to thoracic ganglia (##REF##34912112##Lyu et al., 2022##).</caption></fig>" ]

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[ "<boxed-text id=\"JEB200261B1\" position=\"float\"><caption><title>Box 1. The role of odor dispersal in odor tracking</title></caption>Odor dispersal, a topic covered in detail in other reviews (##UREF##16##Capelli et al., 2013##; ##UREF##17##Celani et al., 2014##; ##REF##24318851##Elkinton et al., 1984##; ##UREF##48##Murlis et al., 1992##; ##REF##18548311##Riffell et al., 2008##), is essential to understanding odor tracking. There are two mechanisms by which pheromones released by a small odor source (red dot in the figure) such as a female gypsy moth can disperse: diffusion, and advection and convection (top panel is a snapshot of odor distribution; gray patches represent odor concentration). Diffusion is a process in which the odor molecules move down a concentration gradient. Diffusion rates are so low that it can be discounted as a mechanism for odor dispersal beyond a few centimeters from the odor source (##REF##18548311##Riffell et al., 2008##). Much of the dispersal occurs through advection and convection, processes by which a mass of air moves owing to spatial differences in air density, pressure and temperature, carrying odor molecules with it. This mode of dispersal has two consequences for odor tracking. First, odors move in packets such that local odor concentration is above the detection threshold for long distances from the odor source (bottom panel); this makes odors the first source of information about a resource. Second, the distribution of odor packets in space might be informative about the location of the odor source (##REF##29990365##Boie et al., 2018##), but is not a strong predictor of source location in a dynamic environment. Therefore, odors provide information about objects from afar without providing a roadmap to the object that other senses such as vision might. Near the odor source, the odor pulses that an animal experiences are no longer transient; they become continuous (note the consistently gray region adjacent to the odor in the top panel). In one set of measurements, at 4 m from the source, the stimulus was present 75% of the time, while being present only 20% of the time at 40 m (##REF##30381430##Baker et al., 2018##); similar observations have been made by others (##UREF##49##Murlis et al., 2000##). This change drives the change in behavior observed as the insects come close to the odor.</boxed-text>", "<boxed-text id=\"JEB200261B0\" position=\"float\"><caption><title>Glossary</title></caption><bold>Behavioral algorithm</bold>A set of rules for selecting an appropriate action or sequence of actions from a set of pre-established behaviors to accomplish a given task.<bold>Generative model</bold>A model that can generate new data. Here, it means a behavioral model that generates new locomotion trajectories that can be compared with actual data to assess whether model trajectories are consistent with empirical data.<bold>Glomerulus</bold>A clustering of nerve endings. Here, it refers to the region within the antennal lobe where olfactory receptor neurons that express the same olfactory receptor project into.<bold>Laminar plume</bold>Airflow moves smoothly in a regular path, producing a continuous ribbon of odor filament projecting from the source location.<bold>Multimodal integration</bold>Integration of information from different sensory modalities.<bold>Neuropil</bold>An area within the nervous system where there is a high density of synapses but relatively few cell bodies.<bold>Odor-gated anemotaxis</bold>Turning upwind when a salient odor is encountered.<bold>Olfactory receptor neurons (ORNs)</bold>Receptors housed in specialized hairs called sensilla within the antennae that are activated in response to airborne odorants.<bold>Patch border</bold>The point between an odor plume and odorless space where the concentration of odor is sufficient to pass a detection threshold.<bold>Protocerebral</bold>Pertaining to the protocerebrum, a prominent neural structure within the insect brain that contains important neuropils such as the mushroom body and central complex.<bold>Resource patch</bold>Resources are not distributed randomly. They are distributed in clusters called patches. Sensory stimuli including odor, tastants or visual stimuli can signal a resource patch.<bold>Sensorimotor reflexes</bold>The modulation or initiation of behaviors in response to a specific sensory cue.<bold>Turbulent plume</bold>Fluctuating, irregular airflow causes odor filaments to be dispersed amongst intermittent pockets of odorless space.<bold>Turn bias</bold>The propensity to turn in the same direction, say, clockwise.<bold>Visually guided anemotaxis</bold>Maintaining a fixed trajectory with respect to the wind direction using visual cues for steering. This behavior is important during flight.</boxed-text>", "<boxed-text id=\"JEB200261B2\" position=\"float\"><caption><title>Box 2. Studying odor-guided locomotion in the lab</title></caption>Odor-guided locomotion is a challenging problem as the complexity and diversity of the odor landscape experienced by insects in nature is difficult to replicate in the lab. Furthermore, even in simplified laboratory experiments, it is difficult to quantify when the animal encountered an odor, making it difficult to evaluate the animal's underlying strategy. Inferring strategy from an animal's circuitous walking or flight paths is itself a daunting problem. Despite these challenges, much progress has been made in understanding the behavioral algorithms at play during odor-modulated locomotion by performing experiments in simpler behavioral arenas that, with some exceptions, fall into three types. In the first type, insects navigate towards an odor source in a laminar plume. These experiments are performed in a wind tunnel at low wind speeds such that there is a small cylinder of odorized region within the tunnel. The second type of experiment uses similar methods, but with turbulent rather than laminar plumes. These turbulent plumes still do not capture the complexity of real-world plumes because the wind direction is also constant, and much of the spatial scales of turbulence observed in nature are too large to be observed within a wind tunnel. Finally, the third type of experiments is conducted in still air without any wind.</boxed-text>", "<boxed-text id=\"JEB200261B3\" position=\"float\"><caption><title>Box 3. Circuits for olfactory processing in insects</title></caption>Odor detection occurs in the olfactory receptor neurons (ORNs) present in the antennae and palps. Each ORN expresses one or a few odorant receptors (ORs); the number of receptors range from just 10 in some lice (##REF##22153368##Hansson and Stensmyr, 2011##; ##REF##20566863##Kirkness et al., 2010##) to a few hundred in bees (##REF##14608037##Robertson et al., 2003##). The ORs expressed in each ORN determine its odor response profile. In many (##UREF##57##Schachtner et al., 2005##) but not all insects, ORNs expressing a given receptor (different ORN classes in the figure) project to a single glomerulus, where they interact with second-order neurons called projection neurons (PNs). Approximately half of the PNs in <italic toggle=\"yes\">Drosophila</italic> are themselves uniglomerular (uPN), and the other half are multiglomerular (mPN). A large majority of uPNs use acetylcholine, the major excitatory neurotransmitter in invertebrates; a minority use GABA as their neurotransmitter. The division of mPNs into excitatory versus inhibitory is more equal. The outputs of the antennal lobe are four different channels of information: excitatory (in magenta in the figure) and inhibitory uPNs (in green in the figure) as well as excitatory and inhibitory mPNs (##REF##32619485##Bates et al., 2020##). The presence of these parallel pathways from the antennal lobe to higher brain centers is conserved across insect orders, but there are also important differences (##REF##19737085##Galizia and Rössler, 2010##).</boxed-text>" ]

[]

[ "<fn-group><title>Footnotes</title><fn fn-type=\"financial-disclosure\">\n<bold>Funding</bold>\nThis research was supported by the National Institute on Deafness and Other Communication Disorders (RO1DC015827 to V.B.), the National Institute of Neurological Disorders and Stroke (RO1NS097881 to V.B.) and a National Science Foundation CAREER award (IOS-1652647 to V.B.). Deposited in PMC for release after 12 months.</fn></fn-group>" ]

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Insect Physiol."], "volume": ["34"], "fpage": ["231"], "lpage": ["316"], "pub-id": ["10.1016/S0065-2806(07)34005-8"]}, {"surname": ["Thiery", "Visser"], "given-names": ["D.", "J."], "year": ["1986"], "article-title": ["Masking of host plant odour in the olfactory orientation of the Colorado potato beetle"], "italic": ["Entomol. Exp. Appl."], "volume": ["41"], "fpage": ["165"], "lpage": ["172"], "pub-id": ["10.1111/j.1570-7458.1986.tb00524.x"]}, {"surname": ["Vickers", "Christensen", "Mustaparta", "Baker"], "given-names": ["N.", "T.", "H.", "T."], "year": ["1991"], "article-title": ["Chemical communication in heliothine moths"], "italic": ["J. Comp. Physiol. A"], "volume": ["169"], "fpage": ["275"], "lpage": ["280"], "pub-id": ["10.1007/BF00206991"]}, {"surname": ["Vinson"], "given-names": ["S. B"], "year": ["1977"], "italic": ["Behavioral chemicals in the augmentation of natural enemies. In Biological Control by Augmentation of Natural Enemies"], "fpage": ["237"], "lpage": ["279"], "publisher-name": ["Springer"]}, {"surname": ["Von Keyserlingk"], "given-names": ["H"], "year": ["1984"], "article-title": ["Close range orientation of flying Lepidoptera to pheromone sources in a laboratory wind tunnel and the field."], "italic": ["Meded. Fac. Landbouwwet. Rijksuniv. Gent"], "volume": ["49"], "fpage": ["683"], "lpage": ["689"]}, {"surname": ["Waage"], "given-names": ["J. K."], "year": ["1978"], "article-title": ["Arrestment responses of the parasitoid, "], "italic": ["Nemeritis canescens", "Plodia interpunctella", "Physiol. Entomol."], "volume": ["3"], "fpage": ["135"], "lpage": ["146"], "pub-id": ["10.1111/j.1365-3032.1978.tb00143.x"]}, {"surname": ["Willis", "Baker"], "given-names": ["M.", "T. C."], "year": ["1994"], "article-title": ["Behaviour of flying oriental fruit moth males during approach to sex pheromone sources"], "italic": ["Physiol. Entomol."], "volume": ["19"], "fpage": ["61"], "lpage": ["69"], "pub-id": ["10.1111/j.1365-3032.1994.tb01075.x"]}, {"surname": ["Willis", "Murlis", "Card\u00e9"], "given-names": ["M. A.", "J.", "R. T."], "year": ["1991"], "article-title": ["Pheromone\u2013mediated upwind flight of male gypsy moths, "], "italic": ["Lymantria dispar", "Physiol. Entomol."], "volume": ["16"], "fpage": ["507"], "lpage": ["521"], "pub-id": ["10.1111/j.1365-3032.1991.tb00590.x"]}, {"surname": ["Wright"], "given-names": ["R."], "year": ["1958"], "article-title": ["The olfactory guidance of flying insects"], "italic": ["Can. Entomol."], "volume": ["90"], "fpage": ["81"], "lpage": ["89"], "pub-id": ["10.4039/Ent9081-2"]}]

{ "acronym": [], "definition": [] }

215

CC BY

no

2024-01-13 23:40:13

J Exp Biol. 2023 Jan 13; 226(1):jeb200261

oa_package/e2/e4/PMC10086387.tar.gz

PMC10228894

37253842

[ "<title>Introduction</title>", "The spread of the novel coronavirus forced healthcare systems globally to optimize resource allocation and re-deploy healthcare personnel to save lives [##REF##34995028##1##, ##REF##32191675##2##]. As a result, the increased needs of patient care eclipsed other hospital priorities and often impeded medical doctors’ training [##REF##35148040##3##]. Early studies explored the impact of the COVID-19 pandemic on residents’ training [##UREF##0##4##–##UREF##1##7##]. Residents’ education in surgical specialties in particular is found to be more severely impacted by the pandemic [##REF##32278932##8##–##REF##32740604##17##]. Recent studies have revealed substantial learning losses among plastic surgery residents in specific regions [##REF##33119537##18##–##REF##32458042##22##]. In this study, we investigate the impact of the COVID-19 pandemic on learning outcomes of plastic surgery residents at the country and continent level worldwide.", "Our cross-sectional survey study goes beyond previous research in four important ways. First, we target plastic surgery residents across the world, while previous studies have explored the impact of COVID-19 at a national or a regional level. Our analysis makes comparisons between countries and provides benchmarks for national strategies of learning recovery. Second, we investigate the impact of the pandemic directly on expected learning outcomes, overcoming the challenge of identifying the intermediate relation between learning inputs, such as surgeries and seminars, and outputs, such as surgical competence. Third, the data collection for our study began two years after the pandemic had started, giving us the opportunity to capture a rather complete picture of the impact of COVID-19 on plastic surgery residents across the world. According the CDC, prior pandemics lasted 1–2 years on average [##UREF##7##23##]. Fourth, our analysis provides two contextual benchmarks of pandemic-related impact: each country’s COVID-19 disease burden and its income level. These benchmarks allow us to investigate how the pandemic severity and resource availability may contribute to the magnitude of resident learning losses during COVID-19." ]

[ "<title>Methodology</title>", "We combined data from multiple sources. First, we collected survey responses from plastic surgery residents across the world regarding their learning inputs and outputs prior to and during the COVID-19 pandemic. Second, we obtained data on each country’s COVID-19 cases and COVID-19-related deaths per million through 2021 from the Institute for Health Metrics and Evaluation (IHME) [##UREF##8##24##]. The severity of the COVID-19 pandemic in each country, measured by COVID-19 cases and deaths, may be associated with the operational pressure on local healthcare systems and any interruptions in resident training. Third, we retrieved each country’s classification in economic development from the World Bank [##UREF##9##25##]. The financial context in each country is likely to influence the operational resilience of healthcare systems and consequently the level of disruption in resident training during COVID-19.", "<title>Survey Data</title>", "We developed a survey to capture demographics, reported changes in surgeries and seminars attended prior to and during the COVID-19 pandemic, and expected impact on surgical skill and scientific knowledge at the end of the training program due to the pandemic of plastic surgery residents across the world. The survey was administered automatically through an online link in English between January 10th and February 6th, 2022.", "We followed Aucejo et al. in directly asking individuals for their expected learning outcomes with and without COVID-19 [##REF##32873994##26##]. The responses allowed us to directly calculate the resident-level subjective treatment effect. Our approach builds on an established literature that uses subjective expectations on education outcomes to understand decision making under uncertainty [##UREF##10##27##–##UREF##12##29##]. The validity of our methodology relies on the assumption that residents have well-formed expectations regarding their learning outcomes in both in a reality with the COVID-19 pandemic and in a version of reality without the pandemic. This study was approved by the Institutional Review Board at Stanford University and followed the STROBE reporting guidelines [##REF##17938396##30##].", "We identify key learning inputs for plastic surgery residents: surgeries participated/scrubbed in and seminars attended. Residents were asked to report the number of surgeries and seminars per week or month before and during the COVID-19 pandemic. For each learning input, we calculated the percentage change in the number of surgeries and seminars attended per week, respectively, between prior to and during the pandemic. This information allowed us to understand the severity of the pandemic-related disruption in training inputs across the world. We focused on two main learning outcomes: surgical skill and scientific knowledge. We explicitly asked residents whether the impact of the pandemic on their surgical skill and scientific knowledge has been <italic>significantly negative</italic>, <italic>slightly negative</italic>, <italic>zero</italic>, <italic>slightly positive</italic>, or <italic>significantly positive</italic> relative to residents who completed their training prior to the COVID-19 pandemic. For each leaning outcome, we created binary variable that takes the value one when the respondent replied slightly or significantly less/negative impact.", "With the help of the International Society for Aesthetic Plastic Surgery (ISAPS) we reached plastic surgery residents around the world. ISAPS is the leading professional body for board-certified plastic surgeons with a network of residents in more than 100 countries. The survey link was disseminated by 63 associations of plastic surgeons, including ISAPS, to their resident members via email and social media. The survey questions and the dissemination strategy are reported in Supplementary Appendix. All residents in plastic surgery programs in training when the pandemic started in early 2020 were eligible to participate in the survey. From administrative sources, we inferred that ISAPS had 1314 plastic surgery members in 2022. If half of them were in training during the pandemic, the maximum potential sample we could have would be around 657 residents." ]

[ "<title>Results</title>", "<title>Demographics</title>", "A total of 664 plastic surgery residents responded to the survey request. Two hundred and fifteen residents did not complete the survey.<xref ref-type=\"fn\" rid=\"Fn1\">1</xref> Eleven responses were excluded from respondents who were not in training during the pandemic. Six duplicate responses were dropped. Twenty countries with single responses were excluded. The analytic sample included 412 respondents from 47 countries. Table ##TAB##0##1## presents summary statistics of characteristics of participants and their training settings.", "Females represent 42% of respondents. The majority (61%) of participants are non-Hispanic white. Sixty-three percent have prior general surgery experience of up to 2 years, while 42% of residents have had plastic surgery training prior to their residency. Our sampled residents are roughly equally distributed in PGY 1 through 5+. Nearly 60% of participants work in university-affiliated hospitals, followed by community (18.7%) and tertiary (13.1%) healthcare centers. More than 90% of respondents worked in a hospital that treated COVID-19 patients, while roughly 46% of those were redeployed to COVID-19 wards.", "<title>Learning Inputs</title>", "Figure ##FIG##0##1## shows the average number of surgeries and seminars plastic surgery residents attended per week prior to and during the COVID-19 pandemic. The average number of surgeries declined from 10.01 to 5.61 per week, a 44% decrease. At the same time, the number of seminars decreased from 1.36 to 0.93 per week, an 18% decline. ", "Table ##TAB##1##2## shows the percentage change in surgeries and seminars attended between prior to and during the pandemic by respondents in each country (also shown in Figure ##FIG##1##2##). Respondents from every country except for the Dominican Republic report a decrease in the number of surgeries they scrubbed in. Trainees from the Dominican Republic report that they participated in 10% more surgeries during the COVID-19 pandemic. Residents from Moldova, Japan, and the UK report the least decrease in their operation room time: − 10%, − 12%, and − 12%, respectively. On the other extreme, residents from Canada, Uruguay, Kenya, and Slovakia report that their surgical training decreased more than 70%.", "The median percentage change during the pandemic is four and seven percentage points closer to zero than the corresponding averages in surgeries (− 48% versus − 44%) and seminars (− 25% versus − 18%), respectively. This suggests that a limited number of countries had negligible or even positive percentage change in learning inputs during the pandemic, while the preponderance of nations experienced substantial declines in resident training inputs.", "<title>Learning Outputs</title>", "Figure ##FIG##2##3## plots the survey responses regarding the impact of COVID-19 on residents’ skill and scientific knowledge. We find that 74% of the residents report a slightly negative (43.69%) or significantly negative (30.10%) impact on their surgical skill. In contrast, the scientific knowledge of plastic surgery residents was relatively preserved with 43.45% claiming slight or significant losses in their scientific knowledge attributed to the pandemic.", "Table ##TAB##2##3## shows the percentage of respondents in each country reporting slightly or significantly negative impact on their surgical skill and scientific knowledge due to the pandemic (also shown in Figure ##FIG##3##4##). Residents in the Dominican Republic, Russia, the USA, and Taiwan are the least likely to report surgical skill losses due to COVID-19. On the other extreme, residents from Turkey, the Netherlands, Mexico, Italy, Albania, Brazil, Czech Republic, and Denmark were the most likely to report a negative COVID-19 impact on their surgical dexterity. Respondents from Morocco were the only ones who did not report any surgical skill loss. Our analysis reveals a portion of countries in which residents cruised through the pandemic with their scientific knowledge intact. Six countries, Belgium, Canada, Morocco, the Netherlands, Pakistan, and Syria, had zero percent of trainees reporting scientific knowledge loss due to COVID-19. At the same time, Ethiopia, Venezuela, Serbia, and Egypt were the most heavily impacted with respect to the scientific knowledge of their trainees.", "We find that the median losses level is four and one percentage point higher than the corresponding averages in surgeries (82% versus 78%) and scientific knowledge (41% versus 40%), respectively. This suggests that a limited number of countries had limited or negligible impact of COVID-19 on residents’ surgical skill, while the preponderance of nations reported sizable losses in surgical skill.", "We investigate the statistical association between changes in surgeries and seminars during COVID-19 and the share of respondents reporting declined surgical skill (<italic>ρ</italic> = <italic>−</italic>0<italic>.</italic>511 with <italic>p <</italic> 0<italic>.</italic>001 for surgeries; <italic>ρ</italic> = <italic>−</italic>0<italic>.</italic>079 with <italic>p</italic> = 0<italic>.</italic>600 for seminars) and decreased scientific knowledge (<italic>ρ</italic> = <italic>−</italic>0<italic>.</italic>118 with <italic>p</italic> = 0<italic>.</italic>428 for surgeries; <italic>ρ</italic> = <italic>−</italic>0<italic>.</italic>274 with <italic>p</italic> = 0<italic>.</italic>006 for seminars). It is important to note the significant correlations between corresponding input and output (i.e., surgeries scrubbed in with surgical skill). We find weak cross-correlations between inputs and outputs (i.e., surgeries attended with scientific knowledge and seminars attended with surgical skill). This suggests limited substitutability of inputs in training. In other words, it may be challenging to make up for lost operating room time with more seminars.", "<title>COVID-19 Pandemic Burden</title>", "The COVID-19 burden of disease may reflect pressures to each country’s healthcare system and is likely to have influenced disruptions in resident training during the pandemic. Tables ##TAB##1##2## and ##TAB##2##3## report the pandemic-related burden of disease in each country next to the corresponding pandemic-related change in learning inputs and outputs, respectively.", "We investigate the association between pandemic-induced changes in plastic surgery residents’ learning inputs and outputs, and the number of COVID-19 cases and COVID-19-related deaths through the end of 2021. We find substantial correlations between COVID-19 prevalence per country until the end of 2021 and the percentage change in the number of surgeries residents scrubbed in during COVID-19 (<italic>ρ</italic> = <italic>−</italic> 0<italic>.</italic>188 with <italic>p</italic> = 0<italic>.</italic>206 for cases; <italic>ρ</italic> = 0<italic>.</italic>202 with <italic>p</italic> = 0<italic>.</italic>174 for deaths). We find limited association between COVID-19 prevalence and the percentage change in seminars residents attended during the pandemic in each country (<italic>ρ</italic> = <italic>−</italic>0<italic>.</italic>056 with <italic>p</italic> = 0<italic>.</italic>709 for cases; <italic>ρ</italic> = 0<italic>.</italic>077 with <italic>p</italic> = 0<italic>.</italic>605 for deaths). Turning to learning outputs, we find sizable correlations between COVID-19 prevalence per country until the end of 2021 and the share of respondents reporting declined surgical skill (<italic>ρ</italic> = 0<italic>.</italic>207 with <italic>p</italic> = 0<italic>.</italic>163 for cases; <italic>ρ</italic> = 0<italic>.</italic>2840 with <italic>p</italic> = 0<italic>.</italic>053 for deaths). The correlation of COVID-19 disease burden and reported decreased scientific knowledge is found to be relatively weak (<italic>ρ</italic> = <italic>− </italic>0<italic>.</italic>094 with <italic>p</italic> = 0<italic>.</italic>530 for cases; <italic>ρ</italic> = 0<italic>.</italic>120 with <italic>p</italic> = 0<italic>.</italic>422 for deaths).", "<title>Regional Analysis</title>", "Tables ##TAB##3##4## and ##TAB##4##5## show the COVID-19 impact on learning inputs (outputs) by geographical region. South America reports the largest decline in surgeries and surgical skill during the pandemic compared with other regions. Trainees in Europe, Asia, and Africa follow in terms of surgical skill losses. Plastic surgery residents in North America and Oceania seem to be the least affected by the pandemic. Even at the regional level, we observe smaller losses in areas with lower input declines (e.g., residents from North America report attending 28% more seminars during the pandemic and only 20% of them claim a negative COVID-19 impact on their scientific background). ", "<title>World Bank Income Classification</title>", "Table ##TAB##5##6## shows the percentage change in surgeries and seminars by respondents from countries in each World Bank income-level classification. We find that the decline in reported surgeries was roughly 40%, on average, across all four income classifications, suggesting a ubiquitous impact of COVID-19 on operating volume across countries regardless of their income. In contrast, we find varying levels of COVID-19 impact on the seminars trainees attended. Residents from low-income countries reported the most dramatic negative impact on seminars (i.e., more than 60% decrease). Table ##TAB##6##7## shows the percentage of respondents reporting negative impact on their surgical skill and scientific knowledge due to the pandemic in countries in each World Bank income-level classification. Residents from low-income countries stand out, as 100% of them report a negative impact in their surgical skill and half of them report a decline in their scientific knowledge as a result of COVID-19." ]

[ "<title>Discussion</title>", "Our results show that the preponderance of plastic surgery residents across the world expects their surgical skill and scientific knowledge to be lower compared with previous cohorts due to the pandemic. Residents are much more likely to report surgical skill losses than scientific knowledge losses (i.e., 73.79% versus 43.45%). At the same time, pockets of residents may have experienced positive consequences from COVID-19 on personal and professional dimensions, such as trauma and emergency case care [##UREF##4##14##, ##REF##32740604##17##]. Roughly one-third (31.31%) of residents reported no COVID-19 impact on their scientific knowledge, while more than one-fifth (20.87%) experienced a positive impact from the pandemic. Some residents might have been able to invest time in self-study or research during the pandemic, preserving their knowledge capital from depreciation [##REF##33119537##18##, ##REF##33862581##31##–##REF##36804840##33##].", "Digital resources, such as video recordings of operations, webinars, and teleconferences, might have also benefited residents’ scientific knowledge [##REF##33119537##18##, ##REF##33973935##34##]. Remedial measures that target surgical skill may be more needed than those targeting scientific knowledge. Potential remedial strategies include a surgical skills laboratory or simulating surgical procedures on practice models [##REF##32826733##35##, ##REF##36688516##36##].", "Our results corroborate previous studies on the COVID-19 impact on the surgical logged hours of residents across surgical specialties at the national and regional level [##REF##34432706##6##, ##UREF##14##37##, ##REF##32729892##38##]. This suggests that our findings on learning outputs of plastic surgery residents may constitute a benchmark for the pandemic-related learning losses of residents more broadly [##REF##32426529##39##–##REF##32739443##44##].", "Our finding that surgical skill is primarily driven by the surgeries residents scrub in and less so by seminars provides clear guidance regarding the necessary remedial strategies. Program directors, health policy makers, and health system administrators can leverage our findings into designing recovery plans that provide residents with operation room exposure to help mitigate their pandemic-induced learning losses [##REF##28357046##45##].", "Our results support the need for flexible training models, competency-based advancement, and regular assessment of trainees. This training approach may be best suited to mitigate crisis-driven training deficits [##REF##34995028##1##]. National associations such as the American Board of Plastic Surgery and the Accreditation Council for Graduate Medical Education can lead the efforts to design effective recovery plans for plastic surgery residents [##REF##24701285##46##].", "Our study brings forth two contextual benchmarks of the impact of COVID-19 on the plastic surgery residents in each country: the COVID-19-related disease burden and the income level. Countries with substantial learning losses and a COVID-19 caseload close to the average (e.g., Mexico) may experience low system resilience more generally [##REF##34878546##47##].<xref ref-type=\"fn\" rid=\"Fn2\">2</xref> These countries may need to invest in fortifying their healthcare system (e.g., through resource redundancy) and in the learning recovery of their residents to ensure they become effective health professionals.", "Each country’s income level may be correlated with resource availability that would make healthcare systems and training programs more resilient to crises and more likely to bounce back after a crisis. Residents in high-income countries like Japan, the UK, South Korea, and New Zealand, who suffered lower loss levels, may have better access to tools that would help them recover compared with their counterparts in less affluent countries.", "This study has certain limitations. First, our focus on plastic surgery trainees limits the statistical power of our analyses. Future studies can potentially reach residents across multiple specialties. Second, our convenient non-random sample of participants represents a broad geographic distribution of resident experiences during the pandemic. At the same time, the severity of pandemic may influence the likelihood of plastic surgery residents from specific countries to participate in the study. Future studies might be able to capture the pandemic-related training disruptions of plastic surgery residents in countries or communities not reachable at this time.", "Third, our self-reported measures of learning inputs and anticipated outputs may contain recall and expectation bias. Further research could explore the availability of administrative data on log books and board examinations scores to measure training inputs and outputs and obtain more accurate estimates of the COVID-19 impact.", "At the same time, this study can serve as a blueprint for future research on resident training inputs and outputs, especially following system-wide shocks. The COVID-19 pandemic impaired resident training across the world. Future epidemics, natural phenomena associated with climate change, geopolitical instabilities also pose a significant threat to resident training in the future [##REF##36328042##48##–##REF##34800397##50##]. Our study contributes to a broader understanding of the resilience of each country’s resident training programs to crises. Our approach to quantifying resident learning inputs and expected outputs is general and can be applied in more contexts and specialties. Our measures of learning outputs, in particular, speak to current proposals to develop a “Surgical Preparedness Index” (SPI) to assess, monitor, and improve the resilience of training programs and healthcare systems across the world [##REF##36328042##48##]." ]

[]

[ "<title>Background</title>", "The COVID-19 pandemic has upended graduate medical education globally. We investigated the COVID-19 impact on learning inputs and expected learning outputs of plastic surgery residents across the world.", "<title>Methods</title>", "We administered an online survey capturing training inputs before and during the pandemic and retrieved residents’ expected learning outputs compared with residents who completed their training before COVID. The questionnaire reached residents across the world through the mobilization of national and international societies of plastic surgeons.", "<title>Results</title>", "The analysis included 412 plastic surgery residents from 47 countries. The results revealed a 44% decline (ranging from − 79 to 10% across countries) and an 18% decline (ranging from − 76 to across 151% countries) in surgeries and seminars, respectively, per week. Moreover, 74% (ranging from 0 to 100% across countries) and 43% (ranging from 0 to 100% across countries) of residents expected a negative COVID-19 impact on their surgical skill and scientific knowledge, respectively. We found strong correlations only between corresponding input and output: surgeries scrubbed in with surgical skill (<italic>ρ</italic> = <italic>−</italic>0<italic>.</italic>511 with <italic>p < </italic>0<italic>.</italic>001) and seminars attended with scientific knowledge (<italic>ρ</italic> = − 0<italic>.</italic>274 with <italic>p</italic> = 0<italic>.</italic>006).", "<title>Conclusions</title>", "Our ranking of countries based on their COVID-19 impacts provides benchmarks for national strategies of learning recovery. Remedial measures that target surgical skill may be more needed than those targeting scientific knowledge. Our finding of limited substitutability of inputs in training suggests that it may be challenging to make up for lost operating room time with more seminars. Our results support the need for flexible training models and competency-based advancement.", "<title>Level of evidence V</title>", "This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.springer.com/00266\">http://www.springer.com/00266</ext-link>.", "<title>Supplementary Information</title>", "The online version contains supplementary material available at 10.1007/s00266-023-03389-w.", "<title>Keywords</title>", "Open access funding provided by HEAL-Link Greece." ]

[ "<title>Supplementary Information</title>", "Below is the link to the electronic supplementary material." ]

[ "<title>Author’s Contribution</title>", "SG has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. SG, GK helped in study concept and design, drafting of manuscript, critical revision of the manuscript for important intellectual content, and study supervision. GK, SG, PK, MW contributed to survey dissemination. GK acquired the data. SG was involved in statistical analysis and interpretation of data.", "<title>Funding</title>", "Open access funding provided by HEAL-Link Greece.", "<title>Declarations</title>", "<title>Conflict of interest</title>", "The authors declare that they have no conflicts of interest to disclose.", "<title>Human or Animal Rights</title>", "This article does not contain any procedures with human participants or animals.", "<title>Informed Consent</title>", "This study obtained informed respondent consent.", "<title>Ethical Approval</title>", "This study was approved by the Institutional Review Board at Stanford University (protocol #63918)." ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption>Reported Surgeries and Seminars before and during COVID-19. <italic>Notes:</italic> This figure shows the average number of surgeries (left two columns) and seminars (two right columns) before and during to COVID-19</caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption>Reported Changes in Learning Inputs during COVID-19 by Country Panel A: Surgeries Participated/Scrubbed in. Panel B: Seminars Attended. <italic>Notes:</italic> This map shows the percentage change between prior to and during the COVID-19 pandemic of surgeries residents participated/scrubbed in (Panel A) and seminars attended (Panel B) by country. Darker shades reflect more negatively impacted countries.</caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption>Reported Change in Surgical Skill and Scientific Knowledge of Plastic Surgery Residents due to COVID-19. <italic>Notes:</italic> These figures show the percentage of respondents reporting changes in their surgical skill (left) and scientific knowledge (right) due to COVID-19.</caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption>Reported loss in learning outputs due to COVID-19 by country. Panel A: surgical skill. Panel B: scientific knowledge</caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption>Resident characteristics</caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Mean</th></tr></thead><tbody><tr><td align=\"left\">Female sex (%)</td><td align=\"left\">41.7</td></tr><tr><td align=\"left\" colspan=\"2\"><italic>Race (%)</italic></td></tr><tr><td align=\"left\">White</td><td align=\"left\">61.4</td></tr><tr><td align=\"left\">Asian</td><td align=\"left\">25.0</td></tr><tr><td align=\"left\">Multi-racial</td><td align=\"left\">5.6</td></tr><tr><td align=\"left\">Black</td><td align=\"left\">2.4</td></tr><tr><td align=\"left\">Other</td><td align=\"left\">5.6</td></tr><tr><td align=\"left\">Mean age (yr)</td><td align=\"left\">32.1</td></tr><tr><td align=\"left\">Have dependents (%)</td><td align=\"left\">31.0</td></tr><tr><td align=\"left\">International medical school graduate (%)</td><td align=\"left\">16.3</td></tr><tr><td align=\"left\">Prior general surgery training up to 2 years (%)</td><td align=\"left\">63.0</td></tr><tr><td align=\"left\">Prior plastic surgery training (%)</td><td align=\"left\">42.1</td></tr><tr><td align=\"left\">Mean training duration (yr)</td><td align=\"left\">4.7</td></tr><tr><td align=\"left\" colspan=\"2\"><italic>Year of training (%)</italic></td></tr><tr><td align=\"left\">PGY-1</td><td align=\"left\">17.0</td></tr><tr><td align=\"left\">PGY-2</td><td align=\"left\">21.6</td></tr><tr><td align=\"left\">PGY-3</td><td align=\"left\">21.4</td></tr><tr><td align=\"left\">PGY-4</td><td align=\"left\">20.4</td></tr><tr><td align=\"left\">PGY-5+<italic>Hospital type (%)</italic></td><td align=\"left\">19.7</td></tr><tr><td align=\"left\">University</td><td align=\"left\">59.5</td></tr><tr><td align=\"left\">Community</td><td align=\"left\">18.7</td></tr><tr><td align=\"left\">Tertiary</td><td align=\"left\">13.1</td></tr><tr><td align=\"left\">Private</td><td align=\"left\">5.8</td></tr><tr><td align=\"left\">Military</td><td align=\"left\">2.9</td></tr><tr><td align=\"left\">Residents in hospitals treating COVID-19 patients (1=yes)</td><td align=\"left\">91.3</td></tr><tr><td align=\"left\">Residents redeployed to COVID-19 wards (1=yes)</td><td align=\"left\">45.9</td></tr><tr><td align=\"left\"><italic>N</italic></td><td align=\"left\">412</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption>Changes in surgeries and seminars attended during COVID-19 by country</caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\">Country</th><th align=\"left\" colspan=\"4\">Reported change in</th><th align=\"left\" rowspan=\"3\">N</th><th align=\"left\" rowspan=\"3\">COVID-19 cases (per million)</th><th align=\"left\" rowspan=\"3\">COVID-19 deaths (per million)</th><th align=\"left\" rowspan=\"3\">Income</th></tr><tr><th align=\"left\" colspan=\"2\">Surgeries</th><th align=\"left\" colspan=\"2\">Seminars</th></tr><tr><th align=\"left\">Rank</th><th align=\"left\">%</th><th align=\"left\">Rank</th><th align=\"left\">%</th></tr></thead><tbody><tr><td align=\"left\">Dominican Rep.</td><td align=\"left\">1</td><td align=\"left\">10</td><td align=\"left\">25</td><td align=\"left\">− 26</td><td align=\"left\">10</td><td align=\"left\">37,295</td><td align=\"left\">378</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Moldova</td><td align=\"left\">2</td><td align=\"left\">− 10</td><td align=\"left\">40</td><td align=\"left\">− 53</td><td align=\"left\">5</td><td align=\"left\">114,927</td><td align=\"left\">3139</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Japan</td><td align=\"left\">3</td><td align=\"left\">− 12</td><td align=\"left\">22</td><td align=\"left\">− 22</td><td align=\"left\">9</td><td align=\"left\">13,984</td><td align=\"left\">148</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">UK</td><td align=\"left\">4</td><td align=\"left\">− 12</td><td align=\"left\">10</td><td align=\"left\">0</td><td align=\"left\">5</td><td align=\"left\">191,647</td><td align=\"left\">2627</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Indonesia</td><td align=\"left\">5</td><td align=\"left\">− 13</td><td align=\"left\">34</td><td align=\"left\">− 50</td><td align=\"left\">2</td><td align=\"left\">15,473</td><td align=\"left\">523</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">South Korea</td><td align=\"left\">6</td><td align=\"left\">− 16</td><td align=\"left\">29</td><td align=\"left\">− 35</td><td align=\"left\">12</td><td align=\"left\">12,260</td><td align=\"left\">109</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Finland</td><td align=\"left\">7</td><td align=\"left\">− 18</td><td align=\"left\">19</td><td align=\"left\">− 21</td><td align=\"left\">4</td><td align=\"left\">48,880</td><td align=\"left\">309</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">New Zealand</td><td align=\"left\">8</td><td align=\"left\">− 19</td><td align=\"left\">46</td><td align=\"left\">− 76</td><td align=\"left\">4</td><td align=\"left\">2,723</td><td align=\"left\">9</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Russia</td><td align=\"left\">9</td><td align=\"left\">− 22</td><td align=\"left\">23</td><td align=\"left\">− 25</td><td align=\"left\">9</td><td align=\"left\">71,316</td><td align=\"left\">2092</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">USA</td><td align=\"left\">10</td><td align=\"left\">− 23</td><td align=\"left\">14</td><td align=\"left\">− 1</td><td align=\"left\">26</td><td align=\"left\">162,301</td><td align=\"left\">2441</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Austria</td><td align=\"left\">11</td><td align=\"left\">− 24</td><td align=\"left\">43</td><td align=\"left\">− 67</td><td align=\"left\">3</td><td align=\"left\">142,155</td><td align=\"left\">1,881</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Norway</td><td align=\"left\">12</td><td align=\"left\">− 26</td><td align=\"left\">17</td><td align=\"left\">− 16</td><td align=\"left\">8</td><td align=\"left\">72,550</td><td align=\"left\">240</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Taiwan</td><td align=\"left\">13</td><td align=\"left\">− 27</td><td align=\"left\">26</td><td align=\"left\">− 26</td><td align=\"left\">44</td><td align=\"left\">713</td><td align=\"left\">36</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Sweden</td><td align=\"left\">14</td><td align=\"left\">− 29</td><td align=\"left\">34</td><td align=\"left\">− 50</td><td align=\"left\">4</td><td align=\"left\">124,632</td><td align=\"left\">1451</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Germany</td><td align=\"left\">15</td><td align=\"left\">− 31</td><td align=\"left\">41</td><td align=\"left\">− 57</td><td align=\"left\">38</td><td align=\"left\">85,767</td><td align=\"left\">1343</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Syria</td><td align=\"left\">16</td><td align=\"left\">− 33</td><td align=\"left\">34</td><td align=\"left\">− 50</td><td align=\"left\">2</td><td align=\"left\">2,272</td><td align=\"left\">131</td><td align=\"left\">LI</td></tr><tr><td align=\"left\">Morocco</td><td align=\"left\">17</td><td align=\"left\">− 38</td><td align=\"left\">9</td><td align=\"left\">8</td><td align=\"left\">2</td><td align=\"left\">25,711</td><td align=\"left\">396</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Turkey</td><td align=\"left\">18</td><td align=\"left\">− 39</td><td align=\"left\">27</td><td align=\"left\">− 33</td><td align=\"left\">5</td><td align=\"left\">111,113</td><td align=\"left\">965</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Colombia</td><td align=\"left\">19</td><td align=\"left\">− 42</td><td align=\"left\">4</td><td align=\"left\">40</td><td align=\"left\">11</td><td align=\"left\">99,422</td><td align=\"left\">2505</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">South Africa</td><td align=\"left\">20</td><td align=\"left\">− 45</td><td align=\"left\">44</td><td align=\"left\">− 69</td><td align=\"left\">2</td><td align=\"left\">57,740</td><td align=\"left\">1522</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Bulgaria</td><td align=\"left\">21</td><td align=\"left\">− 47</td><td align=\"left\">18</td><td align=\"left\">− 17</td><td align=\"left\">6</td><td align=\"left\">110,161</td><td align=\"left\">4564</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Czechia</td><td align=\"left\">22</td><td align=\"left\">− 47</td><td align=\"left\">32</td><td align=\"left\">− 44</td><td align=\"left\">4</td><td align=\"left\">235,919</td><td align=\"left\">3443</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">India</td><td align=\"left\">23</td><td align=\"left\">− 47</td><td align=\"left\">3</td><td align=\"left\">96</td><td align=\"left\">17</td><td align=\"left\">24,599</td><td align=\"left\">340</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Serbia</td><td align=\"left\">24</td><td align=\"left\">− 48</td><td align=\"left\">20</td><td align=\"left\">− 21</td><td align=\"left\">7</td><td align=\"left\">189,090</td><td align=\"left\">1850</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Philippines</td><td align=\"left\">25</td><td align=\"left\">− 48</td><td align=\"left\">5</td><td align=\"left\">30</td><td align=\"left\">5</td><td align=\"left\">24,611</td><td align=\"left\">446</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Argentina</td><td align=\"left\">26</td><td align=\"left\">− 49</td><td align=\"left\">1</td><td align=\"left\">151</td><td align=\"left\">16</td><td align=\"left\">124,245</td><td align=\"left\">2575</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Ethiopia</td><td align=\"left\">27</td><td align=\"left\">− 50</td><td align=\"left\">45</td><td align=\"left\">− 75</td><td align=\"left\">2</td><td align=\"left\">3,407</td><td align=\"left\">56</td><td align=\"left\">LI</td></tr><tr><td align=\"left\">Peru</td><td align=\"left\">27</td><td align=\"left\">− 50</td><td align=\"left\">10</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">67,455</td><td align=\"left\">5953</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Belgium</td><td align=\"left\">29</td><td align=\"left\">− 50</td><td align=\"left\">10</td><td align=\"left\">0</td><td align=\"left\">4</td><td align=\"left\">180,624</td><td align=\"left\">2431</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Pakistan</td><td align=\"left\">30</td><td align=\"left\">− 54</td><td align=\"left\">30</td><td align=\"left\">− 38</td><td align=\"left\">2</td><td align=\"left\">5,495</td><td align=\"left\">123</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Poland</td><td align=\"left\">31</td><td align=\"left\">− 55</td><td align=\"left\">34</td><td align=\"left\">− 50</td><td align=\"left\">2</td><td align=\"left\">103,073</td><td align=\"left\">2435</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Albania</td><td align=\"left\">32</td><td align=\"left\">− 57</td><td align=\"left\">6</td><td align=\"left\">25</td><td align=\"left\">4</td><td align=\"left\">73,962</td><td align=\"left\">1132</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Mexico</td><td align=\"left\">33</td><td align=\"left\">− 57</td><td align=\"left\">8</td><td align=\"left\">14</td><td align=\"left\">8</td><td align=\"left\">31,213</td><td align=\"left\">2348</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Greece</td><td align=\"left\">34</td><td align=\"left\">− 57</td><td align=\"left\">42</td><td align=\"left\">− 63</td><td align=\"left\">23</td><td align=\"left\">116,597</td><td align=\"left\">2002</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Venezuela</td><td align=\"left\">35</td><td align=\"left\">− 59</td><td align=\"left\">31</td><td align=\"left\">− 42</td><td align=\"left\">8</td><td align=\"left\">15,711</td><td align=\"left\">188</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Denmark</td><td align=\"left\">36</td><td align=\"left\">− 61</td><td align=\"left\">33</td><td align=\"left\">− 46</td><td align=\"left\">9</td><td align=\"left\">136,410</td><td align=\"left\">555</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Italy</td><td align=\"left\">37</td><td align=\"left\">− 62</td><td align=\"left\">16</td><td align=\"left\">− 9</td><td align=\"left\">33</td><td align=\"left\">103,759</td><td align=\"left\">2327</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Spain</td><td align=\"left\">38</td><td align=\"left\">− 64</td><td align=\"left\">47</td><td align=\"left\">− 88</td><td align=\"left\">7</td><td align=\"left\">132,358</td><td align=\"left\">1880</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Brazil</td><td align=\"left\">39</td><td align=\"left\">− 65</td><td align=\"left\">15</td><td align=\"left\">− 8</td><td align=\"left\">3</td><td align=\"left\">103,532</td><td align=\"left\">2876</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Paraguay</td><td align=\"left\">40</td><td align=\"left\">− 66</td><td align=\"left\">6</td><td align=\"left\">25</td><td align=\"left\">2</td><td align=\"left\">68,739</td><td align=\"left\">2452</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Netherlands</td><td align=\"left\">41</td><td align=\"left\">− 68</td><td align=\"left\">21</td><td align=\"left\">− 22</td><td align=\"left\">4</td><td align=\"left\">179,544</td><td align=\"left\">1196</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Romania</td><td align=\"left\">42</td><td align=\"left\">− 69</td><td align=\"left\">27</td><td align=\"left\">− 33</td><td align=\"left\">15</td><td align=\"left\">92,012</td><td align=\"left\">2989</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Egypt</td><td align=\"left\">43</td><td align=\"left\">− 71</td><td align=\"left\">23</td><td align=\"left\">− 25</td><td align=\"left\">16</td><td align=\"left\">3,474</td><td align=\"left\">196</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Canada</td><td align=\"left\">44</td><td align=\"left\">− 71</td><td align=\"left\">2</td><td align=\"left\">125</td><td align=\"left\">2</td><td align=\"left\">57,828</td><td align=\"left\">788</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Uruguay</td><td align=\"left\">45</td><td align=\"left\">− 72</td><td align=\"left\">34</td><td align=\"left\">− 50</td><td align=\"left\">2</td><td align=\"left\">120,773</td><td align=\"left\">1803</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Kenya</td><td align=\"left\">46</td><td align=\"left\">− 75</td><td align=\"left\">10</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">5461</td><td align=\"left\">100</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Slovakia</td><td align=\"left\">47</td><td align=\"left\">− 79</td><td align=\"left\">34</td><td align=\"left\">− 50</td><td align=\"left\">2</td><td align=\"left\">242,951</td><td align=\"left\">2948</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Average</td><td align=\"left\"/><td align=\"left\">− 44</td><td align=\"left\"/><td align=\"left\">− 18</td><td align=\"left\"/><td align=\"left\">83,955</td><td align=\"left\">1537</td><td align=\"left\"/></tr><tr><td align=\"left\">Median</td><td align=\"left\"/><td align=\"left\">− 48</td><td align=\"left\"/><td align=\"left\">− 25</td><td align=\"left\"/><td align=\"left\">73,962</td><td align=\"left\">1451</td><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption>Losses in surgical skills and scientific knowledge due to COVID-19 by country</caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Country</th><th align=\"left\">Rank</th><th align=\"left\">%</th><th align=\"left\">Rank</th><th align=\"left\">%</th><th align=\"left\">N</th><th align=\"left\">COVID-19 cases (per million)</th><th align=\"left\">COVID-19 deaths (per million)</th><th align=\"left\">Income</th></tr></thead><tbody><tr><td align=\"left\">Morocco</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">25,711</td><td align=\"left\">396</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Dominican Rep.</td><td align=\"left\">2</td><td align=\"left\">10</td><td align=\"left\">7</td><td align=\"left\">10</td><td align=\"left\">10</td><td align=\"left\">37,295</td><td align=\"left\">378</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Russia</td><td align=\"left\">3</td><td align=\"left\">44</td><td align=\"left\">11</td><td align=\"left\">22</td><td align=\"left\">9</td><td align=\"left\">71,316</td><td align=\"left\">2092</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">USA</td><td align=\"left\">4</td><td align=\"left\">46</td><td align=\"left\">9</td><td align=\"left\">19</td><td align=\"left\">26</td><td align=\"left\">162,301</td><td align=\"left\">2441</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Finland</td><td align=\"left\">5</td><td align=\"left\">50</td><td align=\"left\">13</td><td align=\"left\">25</td><td align=\"left\">4</td><td align=\"left\">48,880</td><td align=\"left\">309</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Taiwan</td><td align=\"left\">5</td><td align=\"left\">50</td><td align=\"left\">21</td><td align=\"left\">36</td><td align=\"left\">44</td><td align=\"left\">713</td><td align=\"left\">36</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">South Korea</td><td align=\"left\">5</td><td align=\"left\">50</td><td align=\"left\">25</td><td align=\"left\">42</td><td align=\"left\">12</td><td align=\"left\">12,260</td><td align=\"left\">109</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Kenya</td><td align=\"left\">5</td><td align=\"left\">50</td><td align=\"left\">27</td><td align=\"left\">50</td><td align=\"left\">2</td><td align=\"left\">5461</td><td align=\"left\">100</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">New Zealand</td><td align=\"left\">5</td><td align=\"left\">50</td><td align=\"left\">27</td><td align=\"left\">50</td><td align=\"left\">4</td><td align=\"left\">2723</td><td align=\"left\">9</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Serbia</td><td align=\"left\">10</td><td align=\"left\">57</td><td align=\"left\">45</td><td align=\"left\">86</td><td align=\"left\">7</td><td align=\"left\">189,090</td><td align=\"left\">1850</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Argentina</td><td align=\"left\">11</td><td align=\"left\">63</td><td align=\"left\">13</td><td align=\"left\">25</td><td align=\"left\">16</td><td align=\"left\">124,245</td><td align=\"left\">2575</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Austria</td><td align=\"left\">12</td><td align=\"left\">67</td><td align=\"left\">18</td><td align=\"left\">33</td><td align=\"left\">3</td><td align=\"left\">142,155</td><td align=\"left\">1881</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Japan</td><td align=\"left\">12</td><td align=\"left\">67</td><td align=\"left\">26</td><td align=\"left\">44</td><td align=\"left\">9</td><td align=\"left\">13,984</td><td align=\"left\">148</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Spain</td><td align=\"left\">14</td><td align=\"left\">71</td><td align=\"left\">8</td><td align=\"left\">14</td><td align=\"left\">7</td><td align=\"left\">132,358</td><td align=\"left\">1880</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Colombia</td><td align=\"left\">15</td><td align=\"left\">73</td><td align=\"left\">37</td><td align=\"left\">55</td><td align=\"left\">11</td><td align=\"left\">99,422</td><td align=\"left\">2505</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Belgium</td><td align=\"left\">16</td><td align=\"left\">75</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">4</td><td align=\"left\">180,624</td><td align=\"left\">2431</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Albania</td><td align=\"left\">16</td><td align=\"left\">75</td><td align=\"left\">13</td><td align=\"left\">25</td><td align=\"left\">4</td><td align=\"left\">73,962</td><td align=\"left\">1132</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Sweden</td><td align=\"left\">16</td><td align=\"left\">75</td><td align=\"left\">13</td><td align=\"left\">25</td><td align=\"left\">4</td><td align=\"left\">124,632</td><td align=\"left\">1451</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Venezuela</td><td align=\"left\">16</td><td align=\"left\">75</td><td align=\"left\">46</td><td align=\"left\">88</td><td align=\"left\">8</td><td align=\"left\">15,711</td><td align=\"left\">188</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Philippines</td><td align=\"left\">20</td><td align=\"left\">80</td><td align=\"left\">10</td><td align=\"left\">20</td><td align=\"left\">5</td><td align=\"left\">24,611</td><td align=\"left\">446</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">UK</td><td align=\"left\">20</td><td align=\"left\">80</td><td align=\"left\">23</td><td align=\"left\">40</td><td align=\"left\">5</td><td align=\"left\">191,647</td><td align=\"left\">2627</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Moldova</td><td align=\"left\">20</td><td align=\"left\">80</td><td align=\"left\">38</td><td align=\"left\">60</td><td align=\"left\">5</td><td align=\"left\">114,927</td><td align=\"left\">3139</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Egypt</td><td align=\"left\">23</td><td align=\"left\">81</td><td align=\"left\">44</td><td align=\"left\">81</td><td align=\"left\">16</td><td align=\"left\">3474</td><td align=\"left\">196</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">India</td><td align=\"left\">24</td><td align=\"left\">82</td><td align=\"left\">24</td><td align=\"left\">41</td><td align=\"left\">17</td><td align=\"left\">24,599</td><td align=\"left\">340</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Greece</td><td align=\"left\">25</td><td align=\"left\">83</td><td align=\"left\">43</td><td align=\"left\">70</td><td align=\"left\">23</td><td align=\"left\">116,597</td><td align=\"left\">2002</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Bulgaria</td><td align=\"left\">26</td><td align=\"left\">83</td><td align=\"left\">41</td><td align=\"left\">67</td><td align=\"left\">6</td><td align=\"left\">110,161</td><td align=\"left\">4564</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Romania</td><td align=\"left\">27</td><td align=\"left\">87</td><td align=\"left\">41</td><td align=\"left\">67</td><td align=\"left\">15</td><td align=\"left\">92,012</td><td align=\"left\">2989</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Germany</td><td align=\"left\">28</td><td align=\"left\">87</td><td align=\"left\">40</td><td align=\"left\">66</td><td align=\"left\">38</td><td align=\"left\">85,767</td><td align=\"left\">1343</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Norway</td><td align=\"left\">29</td><td align=\"left\">88</td><td align=\"left\">22</td><td align=\"left\">38</td><td align=\"left\">8</td><td align=\"left\">72,550</td><td align=\"left\">240</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Canada</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">57,828</td><td align=\"left\">788</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Netherlands</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">4</td><td align=\"left\">179,544</td><td align=\"left\">1196</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Pakistan</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">5495</td><td align=\"left\">123</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Syria</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">2</td><td align=\"left\">2272</td><td align=\"left\">131</td><td align=\"left\">LI</td></tr><tr><td align=\"left\">Denmark</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">11</td><td align=\"left\">22</td><td align=\"left\">9</td><td align=\"left\">136,410</td><td align=\"left\">555</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Czechia</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">13</td><td align=\"left\">25</td><td align=\"left\">4</td><td align=\"left\">235,919</td><td align=\"left\">3443</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Brazil</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">18</td><td align=\"left\">33</td><td align=\"left\">3</td><td align=\"left\">103,532</td><td align=\"left\">2876</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Italy</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">18</td><td align=\"left\">33</td><td align=\"left\">33</td><td align=\"left\">103,759</td><td align=\"left\">2327</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Indonesia</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">27</td><td align=\"left\">50</td><td align=\"left\">2</td><td align=\"left\">15,473</td><td align=\"left\">523</td><td align=\"left\">LMI</td></tr><tr><td align=\"left\">Mexico</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">27</td><td align=\"left\">50</td><td align=\"left\">8</td><td align=\"left\">31,213</td><td align=\"left\">2348</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Paraguay</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">27</td><td align=\"left\">50</td><td align=\"left\">2</td><td align=\"left\">68,739</td><td align=\"left\">2452</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Peru</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">27</td><td align=\"left\">50</td><td align=\"left\">2</td><td align=\"left\">67,455</td><td align=\"left\">5953</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Poland</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">27</td><td align=\"left\">50</td><td align=\"left\">2</td><td align=\"left\">103,073</td><td align=\"left\">2435</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Slovakia</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">27</td><td align=\"left\">50</td><td align=\"left\">2</td><td align=\"left\">242,951</td><td align=\"left\">2948</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">South Africa</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">27</td><td align=\"left\">50</td><td align=\"left\">2</td><td align=\"left\">57,740</td><td align=\"left\">1522</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Uruguay</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">27</td><td align=\"left\">50</td><td align=\"left\">2</td><td align=\"left\">120,773</td><td align=\"left\">1803</td><td align=\"left\">HI</td></tr><tr><td align=\"left\">Turkey</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">38</td><td align=\"left\">60</td><td align=\"left\">5</td><td align=\"left\">111,113</td><td align=\"left\">965</td><td align=\"left\">UMI</td></tr><tr><td align=\"left\">Ethiopia</td><td align=\"left\">30</td><td align=\"left\">100</td><td align=\"left\">47</td><td align=\"left\">100</td><td align=\"left\">2</td><td align=\"left\">3407</td><td align=\"left\">56</td><td align=\"left\">LI</td></tr><tr><td align=\"left\">Average</td><td align=\"left\"/><td align=\"left\">78</td><td align=\"left\"/><td align=\"left\">40</td><td align=\"left\"/><td align=\"left\">83,955</td><td align=\"left\">1537</td><td align=\"left\"/></tr><tr><td align=\"left\">Median</td><td align=\"left\"/><td align=\"left\">82</td><td align=\"left\"/><td align=\"left\">41</td><td align=\"left\"/><td align=\"left\">73,962</td><td align=\"left\">1451</td><td align=\"left\"/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption>Changes in surgeries and seminars attended during COVID-19 by region</caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Region</th><th align=\"left\" colspan=\"2\">Reported change in</th><th align=\"left\" rowspan=\"2\">N</th><th align=\"left\" rowspan=\"2\">Countries</th><th align=\"left\" rowspan=\"2\">COVID-19 cases (per million)</th><th align=\"left\" rowspan=\"2\">COVID-19 deaths (per million)</th></tr><tr><th align=\"left\">Surgeries (%)</th><th align=\"left\">Seminars</th></tr></thead><tbody><tr><td align=\"left\">Africa</td><td align=\"left\">− 56</td><td align=\"left\">− 32</td><td align=\"left\">24</td><td align=\"left\">5</td><td align=\"left\">19,159</td><td align=\"left\">454</td></tr><tr><td align=\"left\">Asia</td><td align=\"left\">− 31</td><td align=\"left\">− 15</td><td align=\"left\">107</td><td align=\"left\">10</td><td align=\"left\">28,184</td><td align=\"left\">491</td></tr><tr><td align=\"left\">Europe</td><td align=\"left\">− 46</td><td align=\"left\">− 34</td><td align=\"left\">187</td><td align=\"left\">20</td><td align=\"left\">133,851</td><td align=\"left\">2037</td></tr><tr><td align=\"left\">North America</td><td align=\"left\">− 35</td><td align=\"left\">28</td><td align=\"left\">46</td><td align=\"left\">4</td><td align=\"left\">72,159</td><td align=\"left\">1489</td></tr><tr><td align=\"left\">Oceania</td><td align=\"left\">− 19</td><td align=\"left\">− 76</td><td align=\"left\">4</td><td align=\"left\">1</td><td align=\"left\">2723</td><td align=\"left\">9</td></tr><tr><td align=\"left\">South America</td><td align=\"left\">− 57</td><td align=\"left\">17</td><td align=\"left\">44</td><td align=\"left\">7</td><td align=\"left\">85,697</td><td align=\"left\">2622</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption>Losses in surgical skills and scientific knowledge due to COVID-19 by region</caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\">Region</th><th align=\"left\" colspan=\"2\">Reported loss in</th><th align=\"left\" rowspan=\"3\">N</th><th align=\"left\" rowspan=\"3\">Countries</th><th align=\"left\" rowspan=\"3\">COVID-19 cases (per million)</th><th align=\"left\" rowspan=\"3\">COVID-19 deaths (per million)</th></tr><tr><th align=\"left\">Surgical skill</th><th align=\"left\">Scientific knowledge</th></tr><tr><th align=\"left\">%</th><th align=\"left\">%</th></tr></thead><tbody><tr><td align=\"left\">Africa</td><td align=\"left\">66</td><td align=\"left\">56</td><td align=\"left\">24</td><td align=\"left\">5</td><td align=\"left\">19,159</td><td align=\"left\">454</td></tr><tr><td align=\"left\">Asia</td><td align=\"left\">77</td><td align=\"left\">32</td><td align=\"left\">107</td><td align=\"left\">10</td><td align=\"left\">28,184</td><td align=\"left\">491</td></tr><tr><td align=\"left\">Europe</td><td align=\"left\">83</td><td align=\"left\">40</td><td align=\"left\">187</td><td align=\"left\">20</td><td align=\"left\">133,851</td><td align=\"left\">2037</td></tr><tr><td align=\"left\">North America</td><td align=\"left\">64</td><td align=\"left\">20</td><td align=\"left\">46</td><td align=\"left\">4</td><td align=\"left\">72,159</td><td align=\"left\">1489</td></tr><tr><td align=\"left\">Oceania</td><td align=\"left\">50</td><td align=\"left\">50</td><td align=\"left\">4</td><td align=\"left\">1</td><td align=\"left\">2723</td><td align=\"left\">9</td></tr><tr><td align=\"left\">South America</td><td align=\"left\">87</td><td align=\"left\">50</td><td align=\"left\">44</td><td align=\"left\">7</td><td align=\"left\">85,697</td><td align=\"left\">2622</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab6\"><label>Table 6</label><caption>Changes in surgeries and seminars attended during COVID-19 by income Level</caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\">Income level</th><th align=\"left\" colspan=\"2\">Reported change in</th><th align=\"left\" rowspan=\"3\">N</th><th align=\"left\" rowspan=\"3\">Countries</th><th align=\"left\" rowspan=\"3\">COVID-19 cases (per million)</th><th align=\"left\" rowspan=\"3\">COVID-19 deaths (per million)</th></tr><tr><th align=\"left\">Surgeries</th><th align=\"left\">Seminars</th></tr><tr><th align=\"left\">%</th><th align=\"left\">%</th></tr></thead><tbody><tr><td align=\"left\">High Income (HI)</td><td align=\"left\">− 43</td><td align=\"left\">− 30</td><td align=\"left\">264</td><td align=\"left\">23</td><td align=\"left\">111,281</td><td align=\"left\">1539</td></tr><tr><td align=\"left\">Upper Middle Income (UMI)</td><td align=\"left\">− 45</td><td align=\"left\">1</td><td align=\"left\">93</td><td align=\"left\">14</td><td align=\"left\">82,928</td><td align=\"left\">2243</td></tr><tr><td align=\"left\">Lower Middle Income (LMI)</td><td align=\"left\">− 44</td><td align=\"left\">− 4</td><td align=\"left\">51</td><td align=\"left\">8</td><td align=\"left\">27,469</td><td align=\"left\">658</td></tr><tr><td align=\"left\">Low Income (LI)</td><td align=\"left\">− 42</td><td align=\"left\">− 63</td><td align=\"left\">4</td><td align=\"left\">2</td><td align=\"left\">2,840</td><td align=\"left\">94</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab7\"><label>Table 7</label><caption>Losses in surgical skills and scientific knowledge due to COVID-19 by income level</caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\">Income level</th><th align=\"left\" colspan=\"2\">Reported loss in</th><th align=\"left\" rowspan=\"3\">N</th><th align=\"left\" rowspan=\"3\">Countries</th><th align=\"left\" rowspan=\"3\">COVID-19 cases (per million)</th><th align=\"left\" rowspan=\"3\">COVID-19 deaths (per million)</th></tr><tr><th align=\"left\">Surgical skill</th><th align=\"left\">Scientific knowledge</th></tr><tr><th align=\"left\">%</th><th align=\"left\">%</th></tr></thead><tbody><tr><td align=\"left\">High Income (HI)</td><td align=\"left\">79</td><td align=\"left\">35</td><td align=\"left\">264</td><td align=\"left\">23</td><td align=\"left\">111,281</td><td align=\"left\">1539</td></tr><tr><td align=\"left\">Upper Middle Income (UMI)</td><td align=\"left\">77</td><td align=\"left\">48</td><td align=\"left\">93</td><td align=\"left\">14</td><td align=\"left\">82,928</td><td align=\"left\">2243</td></tr><tr><td align=\"left\">Lower Middle Income (LMI)</td><td align=\"left\">72</td><td align=\"left\">38</td><td align=\"left\">51</td><td align=\"left\">8</td><td align=\"left\">27,469</td><td align=\"left\">658</td></tr><tr><td align=\"left\">Low Income (LI)</td><td align=\"left\">100</td><td align=\"left\">50</td><td align=\"left\">4</td><td align=\"left\">2</td><td align=\"left\">2840</td><td align=\"left\">94</td></tr></tbody></table></table-wrap>" ]

[]

[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]

[ "<table-wrap-foot>This table reports mean values of respondent characteristics<italic>PGY:</italic> postgraduate year</table-wrap-foot>", "<table-wrap-foot>This table shows the average percentage change in the number of surgeries participated/scrubbed in and the number of seminars attended between prior to and during the COVID-19 pandemic in each country. Countries with fewer than two respondents are excluded. Ranking of countries was based on percentages with up to five decimal points. Shown percentages are rounded to full percentage points. Countries are sorted based on reported change in surgeries residents participated/scrubbed in. Each country’s COVID-19 cases and COVID-19-related deaths per million through 2021 were obtained from the Institute for Health Metrics and Evaluation (IHME) [##UREF##8##24##]. Income classification comes from the World Bank [##UREF##9##25##]. The average and median are obtained across countries</table-wrap-foot>", "<table-wrap-foot>This table shows the percentage of respondents in each country reporting slightly or significantly negative change in surgical skill or scientific knowledge due to the COVID-19 pandemic. Countries with fewer than two respondents are excluded. Ranking of countries was based on percentages with up to five decimal points. Shown percentages are rounded to full percentage points. Countries are sorted based on reported surgical skill loss. Each country’s COVID-19 cases and COVID-19-related deaths per million through 2021 were obtained from the Institute for Health Metrics and Evaluation (IHME) [##UREF##8##24##]. Income classification comes from the World Bank [##UREF##9##25##]. The average and median are obtained across countries.</table-wrap-foot>", "<table-wrap-foot>This table shows the average percentage change in the number of surgeries participated/scrubbed in and the number of seminars attended between prior to and during the COVID-19 pandemic in each region. Countries with fewer than two respondents are excluded. Each country’s COVID-19 cases and COVID-19-related deaths per million through 2021 were obtained from the Institute for Health Metrics and Evaluation (IHME) [##UREF##8##24##].</table-wrap-foot>", "<table-wrap-foot>This table shows the percentage of respondents in each region reporting slightly or significantly negative change in surgical skill or scientific knowledge due to the COVID-19 pandemic. Countries with fewer than two respondents are excluded. Each country’s COVID-19 cases and COVID-19-related deaths per million through 2021 were obtained from the Institute for Health Metrics and Evaluation (IHME) [##UREF##8##24##].</table-wrap-foot>", "<table-wrap-foot>This table shows the average percentage change in the number of surgeries participated/scrubbed in and the number of seminars attended between prior to and during the COVID-19 pandemic in countries in each income level. Countries with fewer than two respondents are excluded. Each country’s COVID-19 cases and COVID-19-related deaths per million through 2021 were obtained from the Institute for Health Metrics and Evaluation (IHME) [##UREF##8##24##]. Income classification comes from the World Bank [##UREF##9##25##].</table-wrap-foot>", "<table-wrap-foot>This table shows the percentage of respondents from countries in each income level reporting slightly or significantly negative change in surgical skill or scientific knowledge due to the COVID-19 pandemic. Countries with fewer than two respondents are excluded. Each country’s COVID-19 cases and COVID-19-related deaths per million through 2021 were obtained from the Institute for Health Metrics and Evaluation (IHME) [##UREF##8##24##]. Income classification comes from the World Bank [##UREF##9##25##].</table-wrap-foot>", "<fn-group><fn id=\"Fn1\"><label>1</label>The average survey completion rate for those was less than 30%.</fn><fn id=\"Fn2\"><label>2</label>A low death toll combined with high learning losses in a country may be indicative of sacrificed training to save lives.</fn><fn><bold>Publisher's Note</bold>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</fn></fn-group>" ]

[ "<graphic xlink:href=\"266_2023_3389_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"266_2023_3389_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"266_2023_3389_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"266_2023_3389_Fig4_HTML\" id=\"MO4\"/>" ]

[ "<media xlink:href=\"266_2023_3389_MOESM1_ESM.docx\"><caption>Supplementary file1 (DOCX 372 KB)</caption></media>" ]

[{"label": ["4."], "surname": ["Bambakidis", "Tomei"], "given-names": ["NC", "KL"], "article-title": ["Impact of COVID-19 on neurosurgery resident training and education"], "source": ["J Neurosurg"], "year": ["2020"], "volume": ["133"], "issue": ["1"], "fpage": ["10"], "lpage": ["11"]}, {"label": ["7."], "mixed-citation": ["CR Stewart, SR Lipner (2021) Experiences of resident dermatologists during the COVID-19 pandemic: a cross-sectional survey. Dermatol Ther"]}, {"label": ["11."], "surname": ["de Berker", "Bressington", "Mayo", "Rose", "Honeyman"], "given-names": ["HT", "MJ", "IM", "A", "C"], "article-title": ["Surgical training during the COVID-19 pandemic: challenges and opportunities for junior trainees"], "source": ["J Plast Recostr Aesthet Surg"], "year": ["2021"], "volume": ["74"], "issue": ["3"], "fpage": ["644"], "lpage": ["710"]}, {"label": ["13."], "surname": ["Filobbos", "Sepehripour", "Kumar"], "given-names": ["G", "S", "S"], "article-title": ["Dissection of plastic surgery national selection"], "source": ["B R Coll Surg Engl"], "year": ["2018"], "volume": ["100"], "issue": ["3"], "fpage": ["152"], "lpage": ["154"]}, {"label": ["14."], "surname": ["Leng", "Challoner", "Hausien", "Filobbos", "Baden"], "given-names": ["C", "T", "O", "G", "J"], "article-title": ["From chaos to a new norm: the birmingham experience of restructuring the largest plastics department in the UK in response to the COVID-19 pandemic"], "source": ["J Plast Recostr Aesthet Surg"], "year": ["2020"], "volume": ["73"], "issue": ["12"], "fpage": ["2136"], "lpage": ["2141"]}, {"label": ["15."], "surname": ["Armstrong", "Jeevaratnam", "Murphy", "Pasha", "Tough", "Conway-Jones", "Mifsud", "Tucker"], "given-names": ["A", "J", "G", "M", "A", "R", "RW", "S"], "article-title": ["A plastic surgery service response to COVID-19 in one of the largest teaching hospitals in Europe"], "source": ["J Plast Recostr Aesthet Surg"], "year": ["2020"], "volume": ["73"], "issue": ["6"], "fpage": ["1174"], "lpage": ["1205"]}, {"label": ["19."], "mixed-citation": ["Ayush K Kapila, Michela Schettino, Yasser Farid, Socorro Ortiz, Moustapha Hamdi (2020) The impact of coronavirus disease 2019 on plastic surgery training: the resident perspective. "], "italic": ["Plast Reconstr Surg Glob Open"]}, {"label": ["23."], "mixed-citation": ["CDC Past Pandemics (2022) Center for Disease Control and Preventions, 2022. Accessed from 2 Oct 2022"]}, {"label": ["24."], "mixed-citation": ["WHO-Global-Initiative-for-Emergency-and-Essential-Surgical-Care (2023) Accessed from 27 Jan 2023"]}, {"label": ["25."], "mixed-citation": ["World Bank (2022) Country level income. The World Bank Group. "], "ext-link": ["https://data-helpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups"]}, {"label": ["27."], "mixed-citation": ["Giustinelli P, MD Shapiro (2019) SEATE: Subjective ex ante treatment effect of health on retirement. Technical report, National Bureau of Economic Research"]}, {"label": ["28."], "surname": ["Peter Arcidiacono", "Hotz", "Maurel", "Romano"], "given-names": ["V", "J", "A", "T"], "article-title": ["Ex ante returns and occupational choice"], "source": ["J Polit Econ"], "year": ["2020"], "volume": ["128"], "issue": ["12"], "fpage": ["4475"], "lpage": ["4522"]}, {"label": ["29."], "surname": ["Wiswall", "Zafar"], "given-names": ["M", "B"], "article-title": ["Human capital investments and expectations about career and family"], "source": ["J Polit Econ"], "year": ["2021"], "volume": ["129"], "issue": ["5"], "fpage": ["1361"], "lpage": ["1424"]}, {"label": ["32."], "surname": ["Karamitros", "Goulas"], "given-names": ["G", "S"], "article-title": ["Human capital and productivity in plastic surgery research across nations"], "source": ["Aesthet Plast Surg"], "year": ["2022"], "pub-id": ["10.1007/s00266-022-03223-9"]}, {"label": ["37."], "surname": ["Paskal", "Jaremkow", "Lyszczak", "Paskal", "Wojcik", "Jakub", "Paul"], "given-names": ["AM", "P-L", "PM", "W", "K", "O-L", "MA"], "article-title": ["Impact of COVID-19 pandemic on plastic surgery training in Europe"], "source": ["J Plast Recostr Aesthet Surg"], "year": ["2021"], "volume": ["75"], "fpage": ["1696"], "lpage": ["1703"]}]

{ "acronym": [], "definition": [] }

50

CC BY

no

2024-01-13 23:35:02

Aesthetic Plast Surg. 2023 May 30; 47(6):2889-2901

oa_package/f1/51/PMC10228894.tar.gz

PMC10246083

37292481

[]

[ "Pediatric tumors of the central nervous system are the most common cause of\ncancer-related death in children. The five-year survival rate for high-grade\ngliomas in children is less than 20\\%. Due to their rarity, the diagnosis of\nthese entities is often delayed, their treatment is mainly based on historic\ntreatment concepts, and clinical trials require multi-institutional\ncollaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a\nlandmark community benchmark event with a successful history of 12 years of\nresource creation for the segmentation and analysis of adult glioma. Here we\npresent the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which\nrepresents the first BraTS challenge focused on pediatric brain tumors with\ndata acquired across multiple international consortia dedicated to pediatric\nneuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on\nbenchmarking the development of volumentric segmentation algorithms for\npediatric brain glioma through standardized quantitative performance evaluation\nmetrics utilized across the BraTS 2023 cluster of challenges. Models gaining\nknowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training\ndata will be evaluated on separate validation and unseen test mpMRI dataof\nhigh-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023\nchallenge brings together clinicians and AI/imaging scientists to lead to\nfaster development of automated segmentation techniques that could benefit\nclinical trials, and ultimately the care of children with brain tumors." ]

[]

{ "acronym": [], "definition": [] }

0

CC BY

no

2024-01-13 23:35:03

ArXiv. 2024 Jan 4;:arXiv:2305.17033v4

oa_package/01/95/PMC10246083.tar.gz

PMC10312904

37396601

[]

[ "Computed tomography (CT) reconstructs volumetric images using X-ray\nprojection data acquired from multiple angles around an object. For low-dose or\nsparse-view scans, the classic image reconstruction algorithms often produce\nsevere noise and artifacts. To address this issue, we develop an iterative\nimage reconstruction method based on maximum a posteriori (MAP) estimation. In\nthe Bayesian framework, the gradient of logarithmic probability density\ndistribution of the image, i.e., the score function, plays a crucial role,\ncontributing to the process of image reconstruction. By leveraging Gaussian\nmixture model, we derive a novel score matching formula to establish a mixture\nscore function (MSF) through deep learning. The MSF-based iterative\nreconstruction algorithm significantly improves image reconstruction quality.\nThe convergence of the MSF iterative reconstruction algorithm is first proven\nthrough mathematical analysis. Then, the performance of the MSF reconstruction\nmethod is evaluated on both public medical image datasets and clinical raw CT\ndataset. Our results show that our proposed method outperforms state-of-the-art\nreconstruction methods in terms of accuracy and generalizability." ]

[]

{ "acronym": [], "definition": [] }

0

CC BY

no

2024-01-13 23:35:04

ArXiv. 2024 Jan 9;:arXiv:2306.08610v6

oa_package/4e/c5/PMC10312904.tar.gz

PMC10349424

37452371

[ "<title>Introduction</title>", "Pseudorabies virus (PRV), the etiological agent of Aujeszky's disease, belongs to the subfamily Alphaherpesvirinae of the family Herpesviridae [##REF##35318331##1##–##REF##34201049##3##]. The natural hosts and carriers of PRV are pigs. The clinical manifestations of PRV infection include fatal encephalitis, respiratory distress, stunted growth and development in growing and fattening pigs, reproductive failure in sows, and 100% mortality in newborn piglets [##UREF##1##4##]. PRV infection in piglets can result in neurological symptoms and a mortality rate of up to 100%. Studies have reported the presence of severe neurological symptoms in various nonnatural hosts with PRV infection [##REF##21908347##5##–##REF##15542647##7##]. PRV is a neurotropic virus that can infect the central nervous system (CNS) of animals via retrograde axonal transport, leading to nonsuppurative encephalitis, characterized by an increase in the number of microglia and infiltrating peripheral leukocytes and severe neuron loss in the brain [##REF##16148307##8##, ##REF##35970581##9##].", "Microglia are the primary resident macrophages in the CNS and exhibit high dynamics and mobility [##REF##31299280##10##, ##REF##28959464##11##]. They play a key role in maintaining brain balance during early brain development and throughout life [##REF##29196460##12##–##REF##30660620##14##]. Microglia are activated in response to nerve injury, infection, or neurodegeneration, resulting in changes in their cell morphology and obvious proliferation [##REF##27959620##15##]. A study has reported that microglia are powerful producers of type I interferons, proinflammatory cytokines (such as interleukin [##REF##31892541##16##]-1β, IL-6, and tumor necrosis factor [TNF]-α), and chemokines (such as chemokine ligand 2 [CCL2], monocyte chemoattractant protein-1 [MCP-1], chemokine (C-X3-C motif) ligand 1 [CX3CL1]) in response to viral infection [##UREF##3##17##]. Cytokines are a class of protein molecules that promote the occurrence and development of inflammation [##REF##33264547##18##, ##REF##35015252##19##]. Moreover, microglia are recruited to eliminate infected neurons via phagocytosis to facilitate brain immunity [##REF##30027450##20##]. Triggering receptor expressed on myeloid cells 2 (TREM2) is a type I transmembrane protein that is primarily expressed on microglia. Increasing evidence suggests the importance of TREM2 signaling in the regulation of microglial phagocytosis and the production of inflammatory cytokines. Furthermore, many studies have reported that several disease-associated TREM2 mutations decrease TREM2 distribution on the cell surface and impair microglial phagocytosis [##REF##27143430##21##–##REF##26337043##23##].", "In the present study, we established a PRV infection model in Balb/C mice to elucidate the role of microglial activation in neuroinflammation." ]

[ "<title>Material and methods</title>", "<title>Virus</title>", "Virulent PRV was donated by Wu Bin’s laboratory at Huazhong Agricultural University.", "<title>Animals</title>", "Pig materials were obtained from Wu Bin’s laboratory at Huazhong Agricultural University. Six-week-old female Balb/C mice were purchased from the Laboratory Animal Center of Huazhong Agricultural University (Wuhan, China). All mice were randomly divided into three groups, with six mice in each group: control group, intranasally infected with 20 µL of Dulbecco's modified Eagle medium, 10 µL per nostril; PRV infection group, intranasally infected with 20 µL of PRV viral suspension (1 × 104 TCID50), 10 µL per nostril; and Ki20227 treatment group, Ki20227 (20 mg/kg/day) was orally administered for 14 consecutive days, followed by intranasal infection with 20 µL of PRV viral suspension (1 × 104 TCID50), 10 µL per nostril. The clinical manifestations of the mice were observed and their weight was measured at the same time every day. At the end of the experiment, mouse brain tissues were collected for tissue sectioning and RNA and protein extraction. The study protocols were performed according to the Committee for Protection, Supervision, and Control of Experiments on Animals Guidelines of Huazhong Agricultural University (approval number: HZAUMO-2023–0080).", "<title>Cell culture</title>", "Mouse microglia (BV2) were purchased from Warner Bio Co., Ltd. (Wuhan, China). BV2 cells were seeded into a 6-well plate and cultured for 24 h until 60%–80% confluency. Serum-free medium was added and 1 MOI PRV was inoculated. Cell samples were collected after culturing for 24 h.", "<title>Detection of blood–brain barrier integrity and permeability</title>", "Mice were injected with 1% Evans blue (10 mL/kg) via the tail vein and allowed to freely move for 24 h. After mice were anesthetized with 0.5% pentobarbital sodium, they were perfused with 20 mL of pre-cooled phosphate-buffered saline (PBS) via the heart. Finally, brain tissues were isolated to observe the precipitation of Evans blue.", "<title>Hematoxylin and eosin (HE) staining</title>", "After mice were euthanized, brain tissues were immediately removed and fixed with 4% paraformaldehyde for more than 24 h. Then, tissues were dehydrated using a series of procedures, embedded in paraffin blocks, cut into 3-µm sections, and stained with HE.", "<title>Immunohistochemical staining</title>", "Brain tissue sections were deparaffinized with xylene and a gradient of alcohol and water. Then, heat retrieval was performed using sodium citrate buffer (pH 6) for 15 min. After blocking with a blocking reagent in the dark at room temperature for 10 min, the sections were incubated with goat serum at room temperature for 30 min. Then, the sections were incubated with the primary antibody overnight at 4 °C, followed by incubation with a biotinylated secondary antibody at room temperature for 30 min. Finally, immunohistochemical staining was performed using solid 3,3-diaminobenzidine.", "<title>Immunofluorescence analysis</title>", "Paraffin-embedded brain tissue sections were deparaffinized with xylene and a gradient of alcohol and water. Then, heat retrieval was performed with sodium citrate buffer (pH 6) for 15 min. Sections were then blocked with goat serum at room temperature for 30 min, incubated with primary antibody overnight at 4 °C, and incubated with fluorescent secondary antibody at room temperature for 1 h. Thereafter, sections were stained with 4′,6-diamidino-2-phenylindole (DAPI) for 10 min. For cultured cells, they were fixed with 4% paraformaldehyde for 30 min, permeabilized with 1% Triton X-100 for 10 min, blocked with goat serum for 30 min, incubated with primary antibody overnight at 4 °C, and then incubated with fluorescent secondary antibody at room temperature for 1 h. Finally, cells were stained with DAPI for 10 min. The samples were observed and photographed under a fluorescence microscope.", "<title>Quantitative real-time polymerase chain reaction (qRT-PCR) analysis</title>", "Total RNA was extracted from mouse brain tissues or cells using Trizol. cDNA was synthesized using a reverse transcription kit. The SYBRGreen reagent and StepOne real-time system were used for qRT-PCR. Table ##TAB##0##1## presents the primers used for qRT-PCR. The cycling conditions were as follows: 3 min at 95 °C, followed by 40 cycles of 10 s at 95 °C, 30 s at 54 °C, and 30 s at 72 °C. Data analysis was performed using the 2−ΔΔCt method.", "<title>Enzyme-linked immunosorbent assay (ELISA)</title>", "Inflammatory cytokine levels in the cell supernatant were measured using ELISA. The specific steps were conducted according to the manufacturer’s instructions.", "<title>Western blotting</title>", "Brain tissues or cultured cells were lysed with cold RIPA lysis buffer and centrifuged. The supernatant was collected. Protein concentration was determined using a BCA protein assay kit. Samples were boiled with 5 × loading buffer, separated via SDS–PAGE, transferred onto a polyvinylidene difluoride (PVDF) membrane, blocked with 5% skim milk for 2 h, and then incubated with the corresponding primary antibody overnight at 4 °C. The primary antibody used was Iba-1 (1:1000). The next day, the membrane was incubated with the secondary antibody and visualized using an ECL detection reagent in a chemiluminescence imaging system. Images were saved for analysis.", "<title>Statistical analysis</title>", "All data are represented as mean ± SD. Statistical analysis was performed using GraphPad Prism 8.0 software. One-way analysis of variance and Dunnett’s multiple comparison test were performed. A <italic>P</italic>-value of < 0.05 was considered statistically significant." ]

[ "<title>Results</title>", "<title>PRV infection in pigs induced microglial activation and neuroinflammation.</title>", "The rectal temperature of PRV-infected pigs was increased. Furthermore, they exhibited severe respiratory symptoms. Three pigs died: two on day 7 and one on day 10 post-infection. One pig had difficulty eating on day 4 post-infection [##UREF##5##24##]. Histological examination revealed typical pathological changes in viral encephalitis in brain tissues, including neuronal degeneration and necrosis (green arrow), glial nodule formation via astrocyte proliferation (yellow arrow), and a typical “perivascular cuffing” phenomenon characterized by a large number of infiltrating inflammatory cells surrounding the blood vessels (red arrow), which were visible throughout the brain (Fig. ##FIG##0##1##A). Immunohistochemical staining revealed that these inflammatory cells were mainly lymphocytes, with a high number of T lymphocytes (Fig. ##FIG##0##1##C). Iba-1 is an immunomarker for microglia. Immunohistochemical staining revealed that after PRV infection, microglia in the brain were significantly activated, with enlarged cell bodies and retracted processes, resembling amoeboid shapes (Fig. ##FIG##0##1##B). Western blotting revealed a significant increase in Iba-1 in the brain tissues of PRV-infected pigs (<italic>P</italic> < 0.01) (Fig. ##FIG##0##1##D). Furthermore, qRT-PCR analysis revealed that the mRNA expression of the proinflammatory cytokines TNF-α (<italic>P</italic> < 0.01), IL-6 (<italic>P</italic> < 0.05), and IL-1β (<italic>P</italic> < 0.001); anti-inflammatory cytokines IL-4 (<italic>P</italic> < 0.001), IL-10 (<italic>P</italic> < 0.001), and IL-13 (<italic>P</italic> < 0.001); and chemokines CCL2 (<italic>P</italic> < 0.001), CX3CL1 (<italic>P</italic> < 0.05), and MCP-1 (<italic>P</italic> < 0.001) was significantly increased in the brain of PRV-infected pigs (Fig. ##FIG##0##1##E).", "<title>PRV infection in mice induces microglial activation and triggers neuroinflammation</title>", "We established a mouse PRV infection model and detected and analyzed immune indicators at different stages of the immune responses to elucidate the main immune response characteristics in the brain tissues of PRV-infected mice. PRV-infected mice exhibited decreased appetite, weight loss, and mild scratches on their faces on day 3. Furthermore, they refused to eat or drink, with continued weight loss, severe scratches on their faces, and significant neurological symptoms, and began to die on day 5. All mice died on day 7 (Fig. ##FIG##1##2##F). Histological observations revealed that the brains of PRV-infected mice exhibited obvious pathological changes related to viral encephalitis: neuronal degeneration and necrosis; visible nuclear condensation and rupture; enhanced acidophile of the cytoplasm; phagocytosis of dead neurons by macrophages, forming a “satellite phenomenon” (red arrow); glial cell proliferation, forming glial nodules (green arrow); and a large number of infiltrating lymphocytes and neutrophils around the blood vessels, forming a “vascular cuff” phenomenon (yellow arrow) (Fig. ##FIG##1##2##A). Nissl staining revealed that in the PRV infection group, the gaps became wider between the neurons, neurons became smaller, and staining became darker (Fig. ##FIG##1##2##C). Immunohistochemistry and western blotting were performed to detect microglia in the brain. The number of Iba-1-positive cells increased from day 3 in the PRV infection group (<italic>P</italic> < 0.001), exhibiting a “deformed parasite” state with enlarged cell bodies and decreased branches. On days 4 and 5 post-infection, the branches of microglia were further shortened and thickened and cell bodies were enlarged (Fig. ##FIG##1##2##B). Western blotting revealed that Iba-1 levels were increased (Fig. ##FIG##1##2##D). Immunohistochemistry and qPCR were performed to detect changes in the mRNA expression of cytokines in the brains of PRV-infected mice. On day 3, the expression of the proinflammatory cytokines TNF-α (<italic>P</italic> < 0.01), IL-6 (<italic>P</italic> < 0.01), and IL-1β (<italic>P</italic> < 0.05); anti-inflammatory cytokines IL-4 (<italic>P</italic> < 0.05), IL-10 (<italic>P</italic> < 0.05), and IL-13 (<italic>P</italic> < 0.05); and chemokines CCL2 (<italic>P</italic> < 0.05), CX3CL1 (<italic>P</italic> < 0.05), and MCP-1 (<italic>P</italic> < 0.05) were significantly upregulated; however, the increase in IL-4 and IL-13 expression was relatively low (Fig. ##FIG##1##2##E).", "<title>Ki20227 decreased PRV-induced mouse neuroinflammation by depleting microglia</title>", "Previous studies have reported that Ki20227 can target and decrease microglia in the CNS by inhibiting colony-stimulating factor 1 receptor (CSF-1R) [##UREF##6##25##]. In this study, mice were orally administered PBS or Ki20227 (20 mg/kg/day) for 14 consecutive days before PRV infection to deplete microglia in the brain (Fig. ##FIG##2##3##A). The Ki20227-treated group exhibited clinical symptoms on day 2 post-infection, and all mice died by day 6. On the other hand, the PRV-infected control group exhibited clinical symptoms on day 3 post-infection, and all mice died by day 7. qPCR analysis of brain tissues revealed that PRV viral DNA expression was significantly higher in the Ki20227-treated group than in the PRV-infected control group (<italic>P</italic> < 0.01) on day 3 post-infection; however, the difference was not significant on day 6 (Fig. ##FIG##2##3##D). Immunohistochemical staining revealed that the efficiency of microglial depletion in the brain of Ki20227-treated mice was approximately 80% (<italic>P</italic> < 0.001) (Fig. ##FIG##2##3##A). Notably, the remaining microglia in the Ki20227-treated group were activated on day 3 post-infection, with rapid proliferation and activation of microglia on day 5 (Fig. ##FIG##2##3##C). To elucidate the role of microglia in PRV-induced nonpurulent encephalitis, qPCR was performed to detect the mRNA expression of cytokines in the brain. Ki20227 treatment significantly inhibited the expression of the proinflammatory cytokines TNF-α (<italic>P</italic> < 0.01), IL-6 (<italic>P</italic> < 0.01), and IL-1β (<italic>P</italic> < 0.05) on day 3 post-infection; however, no significant difference was observed in the expression of the Ki20227-treated and PRV-infected control groups on days 4 and 5. On the other hand, the expression of the chemokines CCL2 (<italic>P</italic> < 0.05), CX3CL1 (<italic>P</italic> < 0.05), and MCP-1 (<italic>P</italic> < 0.05) was significantly increased from day 3 post-infection. However, the expression of the anti-inflammatory cytokines IL-4 (<italic>P</italic> < 0.05), IL-10 (<italic>P</italic> < 0.05), and IL-13 (<italic>P</italic> < 0.05) was not significantly affected (Fig. ##FIG##2##3##E).", "There is often a positive correlation between immune cell infiltration and inflammatory reactions (##REF##31892541##16##). To further confirm the proinflammatory role of microglia, we observed the effect of microglial depletion on cell infiltration induced by PRV infection in brain tissues. Immunohistochemical staining revealed that the number of CD3- (T lymphocyte marker) positive cells (<italic>P</italic> < 0.01) and CD20- (B lymphocyte marker) positive cells (<italic>P</italic> < 0.001) was significantly increased in the brain tissues of PRV-infected control mice, mainly distributed around the blood vessels. Furthermore, compared with PRV-infected control mice, the number of CD3-positive cells (<italic>P</italic> < 0.05) and CD20-positive cells (<italic>P</italic> < 0.05) was significantly increased in the brain tissues of infected mice treated with Ki20227 (Fig. ##FIG##3##4##A). Under normal conditions, no peripheral immune cells, such as lymphocytes and monocytes, are present in the brain. However, peripheral immune cells will enter the brain parenchyma with blood after damage to the blood–brain barrier. By injecting Evans blue solution via the tail vein, we observed changes in the permeability of the blood–brain barrier of PRV-infected mice. Furthermore, on day 4 post-infection, Evans blue began to get deposited in the olfactory bulb in the brain tissues of PRV-infected control mice. Nevertheless, the distribution of Evans blue did not significantly increase on day 5 post-infection. In the brain tissues of infected mice treated with Ki20227, Evans blue began to get deposited in the olfactory bulb on day 3 and was distributed throughout the brain tissue on day 5 (Fig. ##FIG##3##4##B). Immunohistochemical staining was performed to detect the expression of matrix metalloproteinases (MMP2 and MMP9) in the brain; the expression was significantly increased from day 3 post-infection (<italic>P</italic> < 0.01).", "<title>BV2 cells engulfed and processed PRV</title>", "A previous study has reported that PRV replication significantly increases after treating microglia with Ki20227 [##REF##32908485##26##]. Therefore, PRV was inoculated into BV2 cells. The refractive index of BV2 cells was increased, cells became round, and cell protrusions disappeared (Fig. ##FIG##4##5##A). Immunofluorescence analysis of PRV-gE protein levels in BV2 cells revealed that the positive signal of PRV-gE appeared in the cytoplasm at 2 h but disappeared at 24 h (Fig. ##FIG##4##5##D). Subsequently, we collected the cells and supernatants of PRV-infected BV2 cells at different time points (2, 4, 8, 12, 24, and 48 h) and inoculated them into PK-15 cells to determine the growth curve of the virus. PRV virus did not proliferate in BV2 cells (Fig. ##FIG##4##5##B). The engulfment ability of TREM2 protein in microglia is closely related. Immunofluorescence analysis revealed that PRV caused a significant increase in TREM2 protein levels in BV2 cells (Fig. ##FIG##4##5##C). Western blotting revealed the same trend in PRV-infected BV2 cells for 24 h.", "<title>PRV induces BV2 cell activation</title>", "Immunofluorescence analysis was performed to detect BV2 cell activation. The number of Iba-1-positive cells was very low at 2 h; however, it began to increase at 12 h and was significantly increased at 24 h (Fig. ##FIG##5##6##A). Western blotting revealed the same trend in PRV-infected BV2 cells (Fig. ##FIG##5##6##B). ELISA was performed to detect inflammatory factors in the cell supernatant. The levels of TNF-α, IL-1β, and IL-6 were significantly increased (Fig. ##FIG##5##6##C)." ]

[ "<title>Discussion</title>", "The CNS has been regarded as an immune privilege for a long time because it cannot generate an immune response against allogeneic grafts [##REF##32357509##27##]. Nevertheless, new evidence has prompted a re-evaluation of the CNS as not being an immune-privileged site but rather an “immunologically quiescent” one [##UREF##7##28##]. PRV is a well-known virus that can invade the nervous system. PRV infection in pigs is characterized by acute neurological symptoms and the possibility of developing nervous system disorders [##REF##28873002##29##]. Several studies have verified that PRV-infected mice typically exhibit neurological infection of the CNS, accompanied by severe central nervous symptoms and a high mortality rate [##REF##30258005##30##]. However, systematic studies on PRV-induced neuroinflammation are lacking. In the present study, we elucidated the neuroinflammation caused by PRV infection in pigs and used a PRV-infected mouse model to elucidate the role of activated microglia in neuroinflammation by altering the status and number of microglia.", "We observed that PRV can cause severe neurological inflammation in both pigs and mice. Interestingly, the number of “vascular cuffs” in pig brain tissues was higher than that in mice brain tissues; furthermore, the number of infiltrating peripheral immune cells around the blood vessels was higher. This may be associated with the different courses of the two animals after infection. Mice died acutely after PRV infection with a course of only 7 days [##REF##32244386##31##], whereas adult pigs had a longer course of PRV infection, providing more time for peripheral immune cells to react.", "Damage to the blood–brain barrier is a prerequisite for the entry of peripheral immune cells into the injured site. West Nile virus infects the cells in the blood–brain barrier, resulting in considerable endothelium impairment, potent neuroinflammation, and immune cell recruitment [##UREF##8##32##]. Furthermore, after herpes simplex virus 1 infection, changes in blood–brain barrier integrity and permeability can lead to increased movement of viruses, immune cells, and/or cytokines into the brain parenchyma, resulting in more severe neuroinflammation and further brain damage [##REF##30456443##33##, ##UREF##9##34##]. MMPs are encoded by multiple genes and belong to the zinc-dependent endopeptidase family. They are involved in the remodeling of the extracellular matrix. MMPs can cleave tight junction proteins, directly affecting the integrity of the blood–brain barrier [##REF##15711567##35##, ##REF##16624562##36##]. As a result, immunohistochemical staining was performed to assess the expression of MMP-2 and MMP-9. In this study, the expression of MMP-2 and MMP-9 was increased in the brain of pigs and mice, indicating that PRV infection damaged the blood–brain barrier.", "After entering the brain tissue, PRV destroys the stability of the nervous system and rapidly activates microglia. Microglia secrete chemokines such as CCL2, CX3CL1, and MCP-1 and induce the infiltration of peripheral immune cells. This is a mutually reinforcing positive feedback regulation process. The infiltration of peripheral immune cells is essential for the immune responses of the CNS and plays a vital role in CNS infections [##REF##26550694##37##]. An inflammatory response is generally caused by various inflammatory cytokines [##REF##27600281##38##]. These peripheral immune cells then release inflammatory components. We observed the expression of the proinflammatory cytokines TNF-α, IL-6, and IL-1β; anti-inflammatory cytokines IL-4 (<italic>P</italic> < 0.05), IL-10, and IL-13; and chemokines CCL2, CX3CL1, and MCP-1, which were significantly increased in the brain tissue of pigs and mice. Furthermore, the expression of these cytokines was significantly increased in mouse brain tissues after the breakdown of the blood–brain barrier.", "CSF-1R, a transmembrane tyrosine kinase, is involved in the proliferation and differentiation of microglia in the brain [##REF##27659107##39##]. Ki20227, a CSF-1R antagonist, is widely used to deplete microglia in the CNS [##UREF##6##25##, ##REF##25497089##40##]. In the present study, Ki20227 was administered to elucidate the role of microglial activation in PRV infection. We observed that PRV replication was relatively rapid in the brain tissues of Ki20227-treated mice treated in the early stages of infection; however, as the remaining microglia in the brain were activated, viral replication weakened. Studies have reported that microglia can engulf and degrade prions, thereby decreasing viral infection in the CNS [##REF##18066056##41##, ##REF##27185853##42##]. This is closely associated with the phagocytic ability of microglia against viruses, which was confirmed in subsequent cell experiments. These findings suggest that damage to the CNS is not the key factor for moderate microglial activation but may be related to PRV replication during the early stage of infection. Meanwhile, in Ki20227-treated mice, the blood–brain barrier was damaged earlier and more severely with more peripheral cell infiltration and a larger infiltration area. This may be related to the failure of microglia to interfere with early PRV replication.", "In summary, we have provided evidence that microglia play an important role in PRV-induced neuroinflammation. Microglia can engulf and process early PRV replication and secrete cytokines to cope with local injury and chemotactic peripheral immune cell infiltration." ]

[]

[ "Pseudorabies virus (PRV) can infect multiple hosts and lead to fatal encephalitis. There is a significant increase in the number of microglia in the brain of animals infected with PRV. However, whether and how microglia contribute to central nervous system damage in PRV infection remain unknown. In the present study, we elucidated that PRV infection can cause more severe inflammatory cell infiltration, thicker and more numerous vessel sleeve walls, and more severe inflammatory responses in the brains of natural hosts (pigs) than in those of nonnatural hosts (mice). In a mice infection model, activated microglia restricted viral replication in the early stage of infection. Acute neuroinflammation caused by microglia hyperactivation at late-stage of infection. Furthermore, in vitro experiments revealed that microglia restricted viral replication and decreased viral infectivity. This may be associated with the phagocytic ability of microglia because we observed a significant increase in the expression of the membrane receptor TREM2 in microglia, which is closely related to phagocytosis, we observed that depletion of microglia exacerbated neurological symptoms, blood–brain barrier breakdown, and peripheral lymphocyte infiltration. Taken together, we revealed the dual role of microglia in protecting the host and neurons from PRV infection.", "<title>Keywords</title>" ]

[]

[ "<title>Author contributions</title>", "Conceptualization: XS, GC. Data curation: XJ, XL. Formal analysis: LL, JY. Funding acquisition: GC. Investigation: XS, LW, ZL, HF, CZ. Methodology: XS, XJ. Supervision: GC, WZ, CG, XH. Writing—original draft preparation: XS, GC. Writing—review & editing: GC.", "<title>Funding</title>", "This work was supported by the Fundamental Research Funds for the Central Universities (NO. 140422008), the Fundamental Research Funds for the Central Universities (NO. 2662017PY008), the National Key Research and Development Program of China (NO. 2018YF0500805).", "<title>Availability of data and materials</title>", "The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "All mice used in this study were treated humanely during the experiment and euthanized at the end of the experiment. Animal care and use protocols were reviewed and approved by the experimental animal monitoring committee of Huazhong Agricultural University (Permit Number: HZAUMO-2023-0080).", "<title>Consent for publication</title>", "Not applicable.", "<title>Competing interests</title>", "The research was conducted in the absence of any commercial or fnancial relationships that could be construed as a potential confict of interest." ]

[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption>PRV infection induces microglial activation and neuroinflammation in pigs. Pig materials were obtained from Wu Bin’s laboratory at Huazhong Agricultural University. <bold>A</bold> HE staining showing histological changes in pig brains. Red arrow, vascular cuffs; green arrow, injured neurons surrounded by glial cells; and yellow arrow, glial nodules. <bold>B</bold> Immunohistochemical staining of Iba-1 in pig brain tissues. <bold>C</bold> Immunohistochemical staining of CD3 and CD20 in pig brain tissues. <bold>D</bold> Western blotting of Iba-1 and β-actin in pig brain tissues. <bold>E</bold> qRT-PCR showing cytokine expression (n = 3)</caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption>PRV infection induces microglial activation and neuroinflammation in mice. BALB/c mice were intranasally challenged with PRV (1 × 104 TCID50). <bold>A</bold> HE staining showing histological changes in mice brains. Red arrow, vascular cuffs; green arrow, glial nodules; and yellow arrow, injured neurons surrounded by glial cells. <bold>B</bold> Immunohistochemical staining of Iba-1 in mice brain tissues. Iba1-positive cells were quantified in the right panels. Each column represents the mean ± SD for 10 fields in each group. Bars, 20 mm. <bold>C</bold> Nissl staining of mice brain tissues. <bold>D</bold> Western blotting of Iba-1 and β-actin in mice brain tissues. <bold>E</bold> qRT-PCR showing cytokine expression (n = 6). <bold>F</bold> Clinical manifestations of PRV-infected mice</caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption>Ki20227 depletes microglia and exacerbates early neuroinflammation in mice. <bold>A</bold> Procedure of Ki20227 administration and PRV infection. <bold>B</bold> Microglial depletion was evaluated via immunostaining of Iba1. Iba1-positive cells were quantified in the right panels. Each column represents the mean ± SD for 10 fields in each group. Bars, 20 mm. <bold>C</bold> Immunohistochemical staining of Iba-1 in mice brain tissues. Iba1-positive cells were quantified in the right panels. Each column represents the mean ± SD for 10 fields in each group. Bars, 20 mm. <bold>D</bold> Clinical manifestations of the mice. <bold>E</bold> qRT-PCR showing cytokine expression (n = 6)</caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption>PRV infection promotes blood–brain barrier disruption and lymphocyte infiltration. <bold>A</bold> Immunohistochemical staining of CD3 and CD20 in mice brain tissues. Positive cells were quantified in the right panels. Each column represents the mean ± SD for 10 fields in each group. Bars, 20 mm. <bold>B</bold> Brain tissues stained with Evans blue. <bold>C</bold> Immunohistochemical staining of MMP2 and MMP9 in mice brain tissues. Positive cells were quantified in the right panels. Each column represents the mean ± SD for 10 fields in each group. Bars, 20 mm</caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption>BV2 cells engulfed and processed PRV. <bold>A</bold> Optical inverted microscope showing the morphology of BV2 cells. <bold>B</bold> One-step growth curve. <bold>C</bold> Western blotting of TERM2 and β-actin in BV2 cells. <bold>D</bold> Immunofluorescence analysis of PRV-gE in BV2 cells. <bold>E</bold> Immunofluorescence analysis of TREM2 in BV2 cells</caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><bold>A</bold> Immunofluorescence analysis of Iba-1 in BV2 cells. <bold>B</bold> Western blotting of Iba-1 and β-actin in BV2 cells. <bold>C</bold> ELISA showing cytokine secretion (n = 3)</caption></fig>" ]

[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption>Primers for quantitative real-time polymerase chain reaction</caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">β-actin Forward:</th><th align=\"left\">CACTGCCGCATCCTCTTCCTCCC</th></tr></thead><tbody><tr><td align=\"left\">β-actin Reverse:</td><td align=\"left\">CAATAGTGATGACCTGGCCGT</td></tr><tr><td align=\"left\">TNF-α Forward:</td><td align=\"left\">TGGCCTCCCTCTCATCAGTT</td></tr><tr><td align=\"left\">TNF-α Reverse:</td><td align=\"left\">TTGAGATCCATGCCGTTGGC</td></tr><tr><td align=\"left\">IL-6 Forward:</td><td align=\"left\">CTTCTTGGGACTGATGCTGG</td></tr><tr><td align=\"left\">IL-6 Reverse:</td><td align=\"left\">CTGGCTTTGTCTTTCTTGTT</td></tr><tr><td align=\"left\">IL-1β Forward:</td><td align=\"left\">ATGAAAGACGGCACACCCAC</td></tr><tr><td align=\"left\">IL-1β Reverse:</td><td align=\"left\">GCTTGTGCTCTGCTTGTGAG</td></tr><tr><td align=\"left\">IL-4β Forward</td><td align=\"left\">ACGGAGATGGATGTGCCAAAC</td></tr><tr><td align=\"left\">IL-4β Reverse:</td><td align=\"left\">AGCACCTTGGAAGCCCTACAGA</td></tr><tr><td align=\"left\">IL-10β Forward:</td><td align=\"left\">CCTGGTAGAAGTGATGCCCC</td></tr><tr><td align=\"left\">IL-10β Reverse:</td><td align=\"left\">GATGCCGGGTGGTTCAATTT</td></tr><tr><td align=\"left\">IL-13β Forward:</td><td align=\"left\">TGACCAAGTTCTCTTCGTTGACAA</td></tr><tr><td align=\"left\">IL-13β Reverse:</td><td align=\"left\">CACAGCCAGTCCTCTTACTTCAC</td></tr><tr><td align=\"left\">CCL2 Forward:</td><td align=\"left\">TGCCCTAAGGTCTTCAGCAC</td></tr><tr><td align=\"left\">CCL2 Reverse:</td><td align=\"left\">ACTGTCACACTGGTCACTCC</td></tr><tr><td align=\"left\">CX3CL1 Forward:</td><td align=\"left\">GGCTACTGGCTTTCCTTGGT</td></tr><tr><td align=\"left\">CX3CL1 Reverse:</td><td align=\"left\">TAGCGGAGGCCTTCTACCAT</td></tr></tbody></table></table-wrap>" ]

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[ "<fn-group><fn><bold>Publisher's Note</bold>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</fn></fn-group>" ]

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[]

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{ "acronym": [], "definition": [] }

42

CC BY

no

2024-01-13 23:36:46

Virol J. 2023 Jul 14; 20:151

oa_package/71/f6/PMC10349424.tar.gz

PMC10350086

37461424

[]

[ "Feature bagging is a well-established ensembling method which aims to reduce\nprediction variance by combining predictions of many estimators trained on\nsubsets or projections of features. Here, we develop a theory of\nfeature-bagging in noisy least-squares ridge ensembles and simplify the\nresulting learning curves in the special case of equicorrelated data. Using\nanalytical learning curves, we demonstrate that subsampling shifts the\ndouble-descent peak of a linear predictor. This leads us to introduce\nheterogeneous feature ensembling, with estimators built on varying numbers of\nfeature dimensions, as a computationally efficient method to mitigate\ndouble-descent. Then, we compare the performance of a feature-subsampling\nensemble to a single linear predictor, describing a trade-off between noise\namplification due to subsampling and noise reduction due to ensembling. Our\nqualitative insights carry over to linear classifiers applied to image\nclassification tasks with realistic datasets constructed using a\nstate-of-the-art deep learning feature map.", "NeurIPS 2023 Camera-Ready. Contains significant updates from the\noriginal submission" ]

[]

{ "acronym": [], "definition": [] }

0

CC BY-SA

no

2024-01-13 23:35:03

ArXiv. 2024 Jan 9;:arXiv:2307.03176v3

oa_package/da/84/PMC10350086.tar.gz

PMC10351774

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1840 Aug; 2(2):105-130

oa_package/9b/24/PMC10351774.tar.gz

PMC10351798

0

[]

[ "<graphic xlink:href=\"westjmsurg141085-0068\" id=\"sp1\" content-type=\"scanned-page\" xlink:role=\"307\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141085-0069\" id=\"sp2\" content-type=\"scanned-page\" xlink:role=\"308\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141085-0070\" id=\"sp3\" content-type=\"scanned-page\" xlink:role=\"309\" position=\"float\"/>" ]

[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1840 Oct; 2(4):307-309

oa_package/3d/b8/PMC10351798.tar.gz

PMC10351837

0

[]

[ "<graphic xlink:href=\"westjmsurg141082-0071\" id=\"sp1\" content-type=\"scanned-page\" xlink:role=\"67\" position=\"float\"/>" ]

[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1840 Jul; 2(1):67

oa_package/20/c4/PMC10351837.tar.gz

PMC10351838

0

[]

[ "<graphic xlink:href=\"westjmsurg141082-0064\" id=\"sp1\" content-type=\"scanned-page\" xlink:role=\"60\" position=\"float\"/>" ]

[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1840 Jul; 2(1):60

oa_package/eb/34/PMC10351838.tar.gz

PMC10351847

0

[]

[ "<graphic xlink:href=\"westjmsurg141082-0039\" id=\"sp1\" content-type=\"scanned-page\" xlink:role=\"35\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0040\" id=\"sp2\" content-type=\"scanned-page\" xlink:role=\"36\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0041\" id=\"sp3\" content-type=\"scanned-page\" xlink:role=\"37\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0042\" id=\"sp4\" content-type=\"scanned-page\" xlink:role=\"38\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0043\" id=\"sp5\" content-type=\"scanned-page\" xlink:role=\"39\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0044\" id=\"sp6\" content-type=\"scanned-page\" xlink:role=\"40\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0045\" id=\"sp7\" content-type=\"scanned-page\" xlink:role=\"41\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0046\" id=\"sp8\" content-type=\"scanned-page\" xlink:role=\"42\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0047\" id=\"sp9\" content-type=\"scanned-page\" xlink:role=\"43\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0048\" id=\"sp10\" content-type=\"scanned-page\" xlink:role=\"44\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0049\" id=\"sp11\" content-type=\"scanned-page\" xlink:role=\"45\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0050\" id=\"sp12\" content-type=\"scanned-page\" xlink:role=\"46\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0051\" id=\"sp13\" content-type=\"scanned-page\" xlink:role=\"47\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0052\" id=\"sp14\" content-type=\"scanned-page\" xlink:role=\"48\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0053\" id=\"sp15\" content-type=\"scanned-page\" xlink:role=\"49\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0054\" id=\"sp16\" content-type=\"scanned-page\" xlink:role=\"50\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0055\" id=\"sp17\" content-type=\"scanned-page\" xlink:role=\"51\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0056\" id=\"sp18\" content-type=\"scanned-page\" xlink:role=\"52\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0057\" id=\"sp19\" content-type=\"scanned-page\" xlink:role=\"53\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0058\" id=\"sp20\" content-type=\"scanned-page\" xlink:role=\"54\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0059\" id=\"sp21\" content-type=\"scanned-page\" xlink:role=\"55\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg141082-0060\" id=\"sp22\" content-type=\"scanned-page\" xlink:role=\"56\" position=\"float\"/>" ]

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{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1840 Jul; 2(1):35-56

oa_package/da/a2/PMC10351847.tar.gz

PMC10351852

0

[]

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{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1840 Jul; 2(1):61-64

oa_package/16/6a/PMC10351852.tar.gz

PMC10355518

0

[]

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{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1840 Jan; 1(1):79-80

oa_package/9b/e1/PMC10355518.tar.gz

PMC10355541

0

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[ "<graphic xlink:href=\"westjmsurg141245-0076\" id=\"sp1\" content-type=\"scanned-page\" xlink:role=\"72\" position=\"float\"/>" ]

[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1841 Jul; 4(1):72

oa_package/f8/e1/PMC10355541.tar.gz

PMC10355559

0

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2024-01-13 00:02:18

West J Med Surg. 1841 Sep; 4(3):225-226

oa_package/22/c9/PMC10355559.tar.gz

PMC10355568

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2024-01-13 00:02:18

West J Med Surg. 1841 Sep; 4(3):216-223

oa_package/e4/c1/PMC10355568.tar.gz

PMC10355582

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2024-01-13 00:02:18

West J Med Surg. 1841 Oct; 4(4):313

oa_package/c3/d6/PMC10355582.tar.gz

PMC10355620

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2024-01-13 00:02:18

West J Med Surg. 1841 Aug; 4(2):111-124

oa_package/fd/a7/PMC10355620.tar.gz

PMC10355769

0

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2024-01-13 00:02:18

West J Med Surg. 1840 Feb-May; 1(2-5):366-368

oa_package/41/75/PMC10355769.tar.gz

PMC10360500

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2024-01-13 00:02:18

West J Med Surg. 1841 Feb; 3(2):145-146

oa_package/ff/e5/PMC10360500.tar.gz

PMC10360502

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2024-01-13 00:02:18

West J Med Surg. 1841 Feb; 3(2):146-147

oa_package/a8/f9/PMC10360502.tar.gz

PMC10360529

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2024-01-13 00:02:18

West J Med Surg. 1841 Jan; 3(1):63

oa_package/f3/2b/PMC10360529.tar.gz

PMC10360549

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2024-01-13 00:02:18

West J Med Surg. 1841 May; 3(5):395-396

oa_package/ea/47/PMC10360549.tar.gz

PMC10360556

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2024-01-13 00:02:18

West J Med Surg. 1841 May; 3(5):383

oa_package/50/94/PMC10360556.tar.gz

PMC10360560

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2024-01-13 00:02:18

West J Med Surg. 1841 May; 3(5):384-385

oa_package/fe/90/PMC10360560.tar.gz

PMC10360562

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2024-01-13 00:02:18

West J Med Surg. 1841 May; 3(5):393-395

oa_package/27/08/PMC10360562.tar.gz

PMC10360622

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2024-01-13 00:02:18

West J Med Surg. 1842 Jan; 5(1):64-69

oa_package/b9/66/PMC10360622.tar.gz

PMC10360635

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 Feb; 5(2):157

oa_package/64/72/PMC10360635.tar.gz

PMC10360640

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2024-01-13 00:02:18

West J Med Surg. 1842 Feb; 5(2):159

oa_package/ec/28/PMC10360640.tar.gz

PMC10360686

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2024-01-13 00:02:18

West J Med Surg. 1842 Apr; 5(4):314-316

oa_package/80/24/PMC10360686.tar.gz

PMC10360698

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 Apr; 5(4):293

oa_package/33/b5/PMC10360698.tar.gz

PMC10360717

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[]

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no

2024-01-13 00:02:18

West J Med Surg. 1842 May; 5(5):400

oa_package/20/25/PMC10360717.tar.gz

PMC10360729

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 May; 5(5):369

oa_package/f6/71/PMC10360729.tar.gz

PMC10360748

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2024-01-13 00:02:18

West J Med Surg. 1842 May; 5(5):393-394

oa_package/51/2a/PMC10360748.tar.gz

PMC10360756

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 Jul; 6(1):65-67

oa_package/84/89/PMC10360756.tar.gz

PMC10360788

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 Mar; 5(3):225

oa_package/f7/20/PMC10360788.tar.gz

PMC10360794

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 Mar; 5(3):224-225

oa_package/c1/1b/PMC10360794.tar.gz

PMC10360797

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 Mar; 5(3):233

oa_package/52/ca/PMC10360797.tar.gz

PMC10360804

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 Mar; 5(3):223-224

oa_package/ef/cc/PMC10360804.tar.gz

PMC10360815

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 Aug; 6(2):145-146

oa_package/eb/c7/PMC10360815.tar.gz

PMC10360838

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 Oct; 6(4):313-319

oa_package/6e/8b/PMC10360838.tar.gz

PMC10360852

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[]

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2024-01-13 00:02:18

West J Med Surg. 1842 Nov; 6(5):395-397

oa_package/97/16/PMC10360852.tar.gz

PMC10360878

0

[]

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CC0

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2024-01-13 00:02:18

West J Med Surg. 1842 Dec; 6(6):426-438

oa_package/24/b1/PMC10360878.tar.gz

PMC10360903

0

[]

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CC0

no

2024-01-13 00:02:18

West J Med Surg. 1842 Sep; 6(3):193-194

oa_package/70/69/PMC10360903.tar.gz

PMC10367697

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CC0

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2024-01-13 00:02:18

West J Med Surg. 1843 Jul; 8(1):48-54

oa_package/df/db/PMC10367697.tar.gz

PMC10367706

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2024-01-13 00:02:18

West J Med Surg. 1843 Jul; 8(1):79

oa_package/7b/e1/PMC10367706.tar.gz

PMC10367759

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[]

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2024-01-13 00:02:18

West J Med Surg. 1844 Jan; 1(1):74-75

oa_package/41/4f/PMC10367759.tar.gz

PMC10367776

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0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1844 Apr; 1(4):285-313

oa_package/01/60/PMC10367776.tar.gz

PMC10367826

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1844 Mar; 1(3):193-219

oa_package/d4/2b/PMC10367826.tar.gz

PMC10367890

0

[]

[ "<graphic xlink:href=\"westjmsurg138990-0095\" id=\"sp1\" content-type=\"scanned-page\" xlink:role=\"187\" position=\"float\"/>" ]

[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:18

West J Med Surg. 1844 Feb; 1(2):187

oa_package/fd/0d/PMC10367890.tar.gz

PMC10379468

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Aug; 2(2):163-174

oa_package/4c/05/PMC10379468.tar.gz

PMC10379472

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Aug; 2(2):174-179

oa_package/65/a9/PMC10379472.tar.gz

PMC10379500

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Sep; 2(3):270-279

oa_package/7b/b9/PMC10379500.tar.gz

PMC10379509

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Dec; 2(6):545-548

oa_package/49/9e/PMC10379509.tar.gz

PMC10379527

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Dec; 2(6):537-544

oa_package/11/1c/PMC10379527.tar.gz

PMC10379568

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Jul; 2(1):1-25

oa_package/bd/5c/PMC10379568.tar.gz

PMC10379685

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Nov; 2(5):456-462

oa_package/0c/58/PMC10379685.tar.gz

PMC10379703

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Nov; 2(5):463-464

oa_package/0e/ba/PMC10379703.tar.gz

PMC10379710

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Nov; 2(5):445-451

oa_package/3d/20/PMC10379710.tar.gz

PMC10379719

0

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0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Nov; 2(5):373-402

oa_package/62/4b/PMC10379719.tar.gz

PMC10379750

0

[]

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0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Oct; 2(4):360-366

oa_package/1d/29/PMC10379750.tar.gz

PMC10379770

0

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{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1844 Oct; 2(4):354-360

oa_package/b6/7e/PMC10379770.tar.gz

PMC10379873

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1846 Jan; 5(1):52-53

oa_package/df/bc/PMC10379873.tar.gz

PMC10379907

0

[]

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{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1846 May; 5(5):369-386

oa_package/f1/0c/PMC10379907.tar.gz

PMC10379953

0

[]

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{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1846 Apr; 5(4):315-343

oa_package/a5/6b/PMC10379953.tar.gz

PMC10380027

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1846 Jun; 5(6):461-481

oa_package/80/5a/PMC10380027.tar.gz

PMC10380100

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1846 Feb; 5(2):100-112

oa_package/3f/0b/PMC10380100.tar.gz

PMC10380117

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1846 Feb; 5(2):160-161

oa_package/bc/ab/PMC10380117.tar.gz

PMC10380121

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1846 Feb; 5(2):172-176

oa_package/b9/da/PMC10380121.tar.gz

PMC10380147

0

[]

[ "<graphic xlink:href=\"westjmsurg139345-0077\" id=\"sp1\" content-type=\"scanned-page\" xlink:role=\"257\" position=\"float\"/>" ]

[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1846 Mar; 5(3):257

oa_package/32/8b/PMC10380147.tar.gz

PMC10380150

0

[]

[ "<graphic xlink:href=\"westjmsurg139345-0097\" id=\"sp1\" content-type=\"scanned-page\" xlink:role=\"nil1\" position=\"float\"/>" ]

[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1846 Apr; 5(4):nil1

oa_package/d4/47/PMC10380150.tar.gz

PMC10388952

0

[]

[ "<graphic xlink:href=\"westjmsurg139341-0086\" id=\"sp1\" content-type=\"scanned-page\" xlink:role=\"450\" position=\"float\"/>", "<graphic xlink:href=\"westjmsurg139341-0087\" id=\"sp2\" content-type=\"scanned-page\" xlink:role=\"451\" position=\"float\"/>" ]

[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1845 Nov; 4(5):450-451

oa_package/28/20/PMC10388952.tar.gz

PMC10388968

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1845 Nov; 4(5):396-401

oa_package/d5/13/PMC10388968.tar.gz

PMC10389845

0

[]

[ "<graphic xlink:href=\"westjmsurg154226-0076\" id=\"sp1\" content-type=\"scanned-page\" xlink:role=\"532\" position=\"float\"/>" ]

[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1845 Jun; 3(6):532

oa_package/cc/63/PMC10389845.tar.gz

PMC10389857

0

[]

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[]

{ "acronym": [], "definition": [] }

0

CC0

no

2024-01-13 00:02:17

West J Med Surg. 1845 Jun; 3(6):491-494

oa_package/15/a8/PMC10389857.tar.gz

PMC10389903

0

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2024-01-13 00:02:17

West J Med Surg. 1845 Feb; 3(2):172

oa_package/e7/b9/PMC10389903.tar.gz

PMC10389905

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2024-01-13 00:02:17

West J Med Surg. 1845 Feb; 3(2):175-178

oa_package/74/d3/PMC10389905.tar.gz

PMC10389932

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2024-01-13 00:02:17

West J Med Surg. 1845 May; 3(5):448-449

oa_package/30/95/PMC10389932.tar.gz

PMC10389984

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2024-01-13 00:02:17

West J Med Surg. 1845 Apr; 3(4):362-363

oa_package/ce/29/PMC10389984.tar.gz

PMC10396157

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2024-01-13 00:02:17

West J Med Surg. 1846 Nov; 6(5):441-442

oa_package/10/97/PMC10396157.tar.gz

PMC10396163

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2024-01-13 00:02:17

West J Med Surg. 1846 Aug; 6(2):180

oa_package/c8/ab/PMC10396163.tar.gz

PMC10396200

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2024-01-13 00:02:17

West J Med Surg. 1846 Sep; 6(3):259-260

oa_package/b1/b0/PMC10396200.tar.gz

PMC10396242

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2024-01-13 00:02:17

West J Med Surg. 1846 Dec; 6(6):523-525

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PMC10399351

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2024-01-13 00:02:17

West J Med Surg. 1848 Jun; 1(6):533-534

oa_package/87/4e/PMC10399351.tar.gz

PMC10399422

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2024-01-13 00:02:17

West J Med Surg. 1848 May; 1(5):453

oa_package/12/a2/PMC10399422.tar.gz

PMC10399429

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2024-01-13 00:02:17

West J Med Surg. 1848 May; 1(5):404-440

oa_package/e4/44/PMC10399429.tar.gz

PMC10402477

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2024-01-13 00:02:17

West J Med Surg. 1849 Sep; 4(3):252-254

oa_package/de/0c/PMC10402477.tar.gz

PMC10402478

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CC0

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2024-01-13 00:02:17

West J Med Surg. 1849 Sep; 4(3):271-273

oa_package/61/8a/PMC10402478.tar.gz

PMC10402521

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[]

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2024-01-13 00:02:17

West J Med Surg. 1849 Nov; 4(5):438-441

oa_package/49/8a/PMC10402521.tar.gz

PMC10402546

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2024-01-13 00:02:17

West J Med Surg. 1849 Dec; 4(6):527-529

oa_package/60/0a/PMC10402546.tar.gz

PMC10402549

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[]

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2024-01-13 00:02:16

West J Med Surg. 1849 Dec; 4(6):524-525

oa_package/61/d7/PMC10402549.tar.gz

PMC10402558

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2024-01-13 00:02:17

West J Med Surg. 1849 Dec; 4(6):547

oa_package/4d/d5/PMC10402558.tar.gz

PMC10402560

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CC0

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2024-01-13 00:02:16

West J Med Surg. 1849 Dec; 4(6):484-492

oa_package/66/08/PMC10402560.tar.gz

PMC10402588

0

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CC0

no

2024-01-13 00:02:17

West J Med Surg. 1849 Aug; 4(2):162b

oa_package/67/44/PMC10402588.tar.gz

PMC10402593

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2024-01-13 00:02:17

West J Med Surg. 1849 Aug; 4(2):162a

oa_package/97/75/PMC10402593.tar.gz

PMC10409012

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[]

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2024-01-13 00:02:17

West J Med Surg. 1849 Jan; 3(1):55-56

oa_package/29/0f/PMC10409012.tar.gz

PMC10409089

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2024-01-13 00:02:17

West J Med Surg. 1849 Mar; 3(3):185-193

oa_package/82/00/PMC10409089.tar.gz

PMC10409115

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2024-01-13 00:02:17

West J Med Surg. 1849 Jun; 3(6):549

oa_package/56/74/PMC10409115.tar.gz

PMC10409139

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2024-01-13 00:02:17

West J Med Surg. 1849 Jun; 3(6):487-495

oa_package/b7/d5/PMC10409139.tar.gz

PMC10409228

0

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2024-01-13 00:02:17

West J Med Surg. 1849 Feb; 3(2):93-104

oa_package/28/16/PMC10409228.tar.gz