Macaques are a commonly used model for studying immunity to human viruses, including for studies of SARS-CoV-2 infection and vaccination. However, it is unknown whether macaque antibody responses resemble the response in humans. To answer this question, we employed a phage-based deep mutational scanning approach (Phage-DMS) to compare which linear epitopes are targeted on the SARS-CoV-2 Spike protein in convalescent humans, convalescent (re-infected) rhesus macaques, mRNA-vaccinated humans, and repRNA-vaccinated pigtail macaques. We also used Phage-DMS to determine antibody escape pathways within each epitope, enabling a granular comparison of antibody binding specificities at the locus level. Overall, we identified some common epitope targets in both macaques and humans, including in the fusion peptide (FP) and stem helix-heptad repeat 2 (SH-H) regions. Differences between groups included a response to epitopes in the N-terminal domain (NTD) and C-terminal domain (CTD) in vaccinated humans but not vaccinated macaques, as well as recognition of a CTD epitope and epitopes flanking the FP in convalescent macaques but not convalescent humans. There was also considerable variability in the escape pathways among individuals within each group. Sera from convalescent macaques showed the least variability in escape overall and converged on a common response with vaccinated humans in the SH-H epitope region, suggesting highly similar antibodies were elicited. Collectively, these findings suggest that the antibody response to SARS-CoV-2 in macaques shares many features with humans, but with substantial differences in the recognition of certain epitopes and considerable individual variability in antibody escape profiles, suggesting a diverse repertoire of antibodies that can respond to major epitopes in both humans and macaques. Differences in macaque species and exposure type may also contribute to these findings. Non-human primates, including macaques, are considered the best animal model for studying infectious diseases that infect humans. Vaccine candidates for SARS-CoV-2 are first tested in macaques to assess immune responses prior to advancing to human trials, and macaques are also used to model the human immune response to SARS-CoV-2 infection. However, there may be differences in how macaque and human antibodies recognize the SARS-CoV-2 entry protein, Spike. Here we characterized the locations on Spike that are recognized by antibodies from vaccinated or infected macaques and humans. We also made mutations to the viral sequence and assessed how these affected antibody binding, enabling a comparison of antibody binding requirements between macaques and humans at a very precise level. We found that macaques and humans share some responses, but also recognize distinct regions of Spike. We also found that in general, antibodies from different individuals had unique responses to viral mutations, regardless of species. These results will yield a better understanding of how macaque data can be used to inform human immunity to SARS-CoV-2. Macaca mulatta) [Macaca fascicularis) [Macaca nemestrina) [The COVID-19 pandemic has created a pressing need to understand immunity to SARS-CoV-2, both in the setting of vaccination and infection. This has prompted numerous studies in non-human primates (NHPs), which are considered the most relevant animal model for studying many infectious diseases of humans. Various NHP models have been employed to study the immunogenicity and protective efficacy of SARS-CoV-2 vaccine candidates, with most studies using macaque species including rhesus macaques (mulatta) , cynomolcularis) ,24–32, aestrina) ,33–35. Sestrina) . In the Coronaviruses such as SARS-CoV-2 enter host cells using their Spike glycoprotein, which is composed of trimeric S1 and S2 subunits. Receptor-binding S1 homotrimers protrude out from the surface of the virion like a crown, giving this family of viruses its name, while the fusion-mediating S2 trimers anchor the protein to the viral membrane. On S1, the receptor-binding domain (RBD) of SARS-CoV-2 Spike protein binds to angiotensin-converting enzyme 2 (ACE2) on host cells ,41. For Antibodies to SARS-CoV-2 Spike protein are especially interesting as a potential correlate of protection, as they have the capacity to block infection and kill infected cells –47. TherPrevious studies have used Phage-DMS , a tool In this study, we built on this foundation by using Phage-DMS to study the binding and escape profiles of antibodies in repRNA-vaccinated pigtail macaques and convalescent (re-infected) rhesus macaques in comparison to mRNA-vaccinated humans and convalescent humans. Our data reveal broad overlap in some major epitopes targeted by both macaques and humans, though neither vaccinated nor convalescent macaques perfectly model the human response. We also find considerable variability in individuals’ antibody escape pathways in most epitope regions in both macaques and humans. The broadest responses were seen in vaccinated humans and re-infected rhesus macaques, groups that also share more concordant escape profiles. These results have implications for the interpretation of COVID-19 macaque research studies.Four groups were included in this study: vaccinated pigtail macaques, vaccinated humans, convalescent (re-infected) rhesus macaques, and convalescent humans . The vacTo compare which regions of Spike protein are recognized by human and macaque antibodies, we examined the enrichment of wildtype peptides by antibodies from each individual . BroadlyIn general, we did not detect responses in the RBD because many epitopes in this region are known to be conformational, and Phage-DMS only has the power to detect epitopes that include linear sequences. Epitopes in the RBD have been extensively detailed elsewhere ,60. HoweTo quantify differences in the epitopes targeted by different groups, the enrichment of wildtype peptides was summed across each epitope region for every individual. Because the main research question is whether responses in macaques model those in humans, two comparisons were performed: vaccinated macaques vs. vaccinated humans and convalescent macaques vs. convalescent humans .In concordance with a qualitative assessment of the enrichment heatmap in Taken together, these findings indicate: 1) vaccinated humans were the only group to consistently recognize peptides from both the NTD and CTD-C’ epitope regions, which are in close physical proximity to one another ; 2) convTo assess differences in the binding characteristics of human and macaque antibodies on a more granular level, we next examined the mutations in Spike that reduced antibody binding in each epitope region of interest. Because the antibody escape pathways for vaccinated humans have been described previously , we did Vaccinated macaques, vaccinated humans, and convalescent macaques recognized peptides in the CTD-N’ (AA 526–593), whereas convalescent humans generally did not . Within To summarize the trends observed in the individual findings, we calculated summed differential selection values for each individual at each site and generated boxplots by group . In addiTo test the similarity of escape profiles across the CTD-N’ epitope region, escape similarity scores were aggregated across the region and computed both within and between groups. These are shown as boxplots, with each point representing a pairwise comparison between individual samples . For exaEscape profiles were examined in the FP epitope region (AA 805–835) for the three groups that showed significant wildtype enrichment in this area: vaccinated humans, convalescent macaques, and convalescent humans. As in the CTD-N’, overall there was variability in individual escape profiles, though the convalescent macaques showed a more consistent pattern of escape than other groups . Within All four groups consistently recognized peptides spanning the SH-H epitope region (AA 1135–1170). Major sites of escape were located between AA 1145–1158 for all groups . The indThe pairwise comparison between participant 352  and M21 (a vaccinated human) generated an escape similarity score closest to the median for all comparisons between these groups. Logo plots for these individuals are shown in In addition to the epitope regions described above, the convalescent macaques strongly recognized the pre-FP and post-FP, which were not targeted by human antibody responses . Escape It was notable that the vaccinated humans and convalescent macaques showed the most similarity in escape profiles across all epitope regions, most strikingly in the SH-H. Thus, we also asked whether they showed similarity in the epitopes they targeted by comparing the enrichment of wildtype peptides in these groups in each epitope region . VaccinaIn this study, we aimed to assess whether the antibody binding specificities to SARS-CoV-2 Spike in macaques are a useful model for the human response. Our results indicate important similarities between macaques and humans; for example, both have antibodies that recognize major epitopes in the CTD, FP, and SH-H. However, many differences are also apparent, with some groups showing responses to unique epitopes, such as two physically proximal epitopes in the NTD and CTD that are recognized by antibodies from vaccinated humans but not macaques. Additionally, epitope regions flanking the FP were recognized by antibodies from convalescent macaques, while antibodies from convalescent humans did not recognize the flanking regions but showed a strong response within the FP itself. We found considerable diversity in the pathways of escape between individuals, and this was not specific to either macaques or humans, suggesting a diverse repertoire of antibodies that can respond to the major epitopes in both groups. Overall, these results suggest that macaques and humans share recognition of certain major epitopes. The differences that exist could be due to species (macaque vs. human), but could also be influenced by differences in the specific type and number of exposures to antigen in each group.Other studies have characterized human monoclonal antibodies against some of the epitopes we report here, many of them with neutralizing or other activities. As previously reported by our group , we founPrevious work elucidated that pathways of antibody escape to SARS-CoV-2 Spike protein can be quite variable in convalescent humans, with vaccination inducing a more consistent response . In the While our focus was on understanding how macaques and humans respond to a similar exposure , we also noted similarities in response between re-infected macaques and vaccinated humans. These groups both exhibited the broadest recognition across Spike, although the epitope regions they targeted were somewhat different. As described above, these groups also had highly similar antibody escape profiles in the SH-H. The vaccinated humans and re-infected macaques both received two exposures to high doses of antigen. It is plausible that re-exposure directed initially diverse antibodies to converge on a more focused response in both scenarios. While it is known that vaccination and infection induce distinct humoral responses against Spike ,84,85, oThis study had several limitations. Because the Phage-DMS library displays peptides 31AA in length, discontinuous or conformational epitopes are not readily detected using this method. Additionally, epitopes that may normally be glycosylated are exposed for antibody binding in Phage-DMS. There also are known germline-encoded differences in the properties of immunoglobulin subclasses and Fc receptors between macaques and humans, leading to differences in antibody function that cannot be assayed using Phage-DMS . AdditioOur findings suggest that while vaccinated and convalescent macaques and humans share recognition of some major epitopes, each group has a unique antibody binding profile. Antibody escape profiles suggest a diversity of individual responses to most epitopes. Important avenues for future study include comparing macaque and human responses to the RBD and evaluating species differences in antibody function. Continued investigation of immunogenic epitopes in conserved regions of Spike is also warranted to inform the development of immunity that is more robust in the face of viral escape.For the vaccinated pigtail macaques, all procedures were approved by the University of Washington’s Institutional Animal Care and Use Committee (IACUC) (IACUC #4266–14). For the convalescent rhesus macaques, all procedures were performed in accordance with Animal Study Proposal RML 2020-046-E approved by the IACUC of Rocky Mountain Laboratories . For the vaccinated humans, all participants provided written informed consent. Because samples were de-identified, this study was approved by the Fred Hutchinson Cancer Research Center Institutional Review Board as non-human subjects research. For the convalescent humans, electronic informed consent was obtained for all participants. This research was approved by the University of Washington Institutional Review Board (IRB number STUDY00000959).Plasma was collected from 11 pigtail macaques immunized with a replicating RNA (repRNA) vaccine expressing full-length SARS-CoV-2 Spike protein. A subset of these animals was previously described . All aniSerum was collected from 12 rhesus macaques housed at the Rocky Mountain Laboratories , 14 days after the second of two SARS-CoV-2 infections spaced 42 days apart. The SARS-CoV-2 isolate used for infection was nCoV-WA1-2020 (MN985325.1), which was provided by the Centers for Disease Control and Prevention and propagated as described previously . This isWe obtained serum from 15 individuals who received two 100μg doses of the Moderna mRNA-1273 vaccine as part of a phase I clinical trial (NCT04283461) . Phage-DPlasma was collected from 12 individuals post-mild COVID-19 illness as part of the Hospitalized or Ambulatory Adults with Respiratory Viral Infections (HAARVI) study in Seattle, WA. The date of symptom onset for these individuals ranged from February—March 2020, representing infections with early circulating strains of SARS-CoV-2. Phage-DMS results from these samples were reported previously ,57. AgaiAll plasma and sera were heat inactivated at 56°C for 1 hour prior to use. Full details of all samples are available in Tables The experimental protocol was performed exactly as described previously . Brieflyhttps://github.com/matsengrp/phage-dms-nhp-analysis. Within each biological replicate, “in-line” technical replicates were run for some samples. In these cases, the technical replicate with the highest mapped read count was selected for analysis.Biological replicates were analyzed in parallel to assess reproducibility of results. For simplicity, results from only one biological replicate are shown and described, with the same figures generated with the second biological replicate available to view online at The enrichment of wildtype peptides was calculated as described previously to quantify the proportion of each peptide in an antibody-selected sample relative to the proportion of that peptide in the input phage library . On enriThe effect of a mutation on antibody-peptide binding was quantified as “differential selection,” which is the log fold change in the enrichment of a mutation-containing peptide compared to the wildtype peptide. This number is multiplied by the average of the wildtype peptide enrichments at that site and its two adjacent sites to get a “scaled differential selection” value, as described previously . The enrhttps://matsengrp.github.io/phippery/esc-prof.html. An overview of the method is shown in The comparison of two escape profiles is quantified by an escape similarity score computed in the framework of an optimal transport problem ; this alThe structure of a SARS-CoV-2 Spike glycoprotein monomer in the closed state (PDB 6XR8) was examined to visualize epitope regions . ColorinFor comparison of summed wildtype enrichment values across an epitope region between sample groups Figs  and S9, Boxplots are used to summarize summed wildtype enrichment values, summed differential selection values, and similarity scores within a sample group. For all boxplots, the box represents the median and interquartile range (IQR), the lower whisker represents the lowest data point above Q1-1.5IQR, and the upper whisker represents the highest data point below Q3+1.5IQR.https://matsengrp.github.io/phippery/). The phip-flow tools perform read alignment using Bowtie2 [All analyses were performed in RStudio version 1.3.1093, Python version 3.6.12, GraphPad Prism version 9.0.1, and the phip-flow and phippery software suite Click here for additional data file.S1 FigThe locus numbers are shown on the x axis, and each individual is represented in a different color. (A) Wildtype enrichment by group from AA 526–685, spanning the CTD-N’ and CTD-C’ epitopes. (B) Wildtype enrichment by group from AA 777–855, spanning the pre-FP, FP, and post-FP epitopes. (C) Wildtype enrichment by group from AA 1135–1204, spanning the SH-H and HR2 epitopes.(TIF)Click here for additional data file.S2 FigThe x axis indicates each peptide’s location along SARS-CoV-2 Spike protein, and each entry on the y axis is an individual sample. Sample groups are indicated on the left. The same macaques that contributed baseline samples also contributed post-vaccination or post-infection samples. All enrichment values over 20 are plotted as 20 to better show the lower range of the data. Above the heatmap, domains of Spike are shown with grey boxes, with the S1/S2 and S2’ cleavage sites indicated with arrows. The epitope regions defined in the current study are shown as colored boxes .(TIF)Click here for additional data file.S3 FigWildtype enrichment values were summed for all peptides within each region of Spike that showed enrichment. Each point represents an individual macaque. No significant differences were found by Kruskal-Wallis test at a threshold of p = 0.05. LION: Lipid InOrganic Nanoparticle; NLC: Nanostructured Lipid Carrier.(TIF)Click here for additional data file.S4 Fighttps://matsengrp.github.io/phippery/esc-prof.html. Created with BioRender.com.(A) Profile 1 and 2 show example logo plots for two samples across the same region. Negative scaled differential selection values represent mutations that reduce antibody binding. Amino acids of the same color indicate similar chemistry . (B) At each location , the profiles are represented as binned distributions where each bin corresponds to the contribution to escape for an amino acid substitution. (C) The optimal transport solution to transform one profile to the other is computed, where the cost to "exchange" an amino acid contribution in Profile 1 to an amino acid contribution in Profile 2 is derived from the BLOSUM62 matrix. For the purposes of the schematic, the number of dollar signs associated with each line denotes the relative cost of each move . (D) To quantify similarity between profiles, an escape similarity score is calculated as the inverse of the total cost to perform the transformation. For more details, see (TIF)Click here for additional data file.S5 Fig(PNG)Click here for additional data file.S6 Fig(PNG)Click here for additional data file.S7 Fig(PNG)Click here for additional data file.S8 Fig(PNG)Click here for additional data file.S9 FigAs in (TIF)Click here for additional data file.


Although CT and MRI are standard procedures in cirrhosis diagnosis, differentiation of etiology based on imaging is not established. This proof-of-concept study explores the potential of deep learning (DL) to support imaging-based differentiation of the etiology of liver cirrhosis. This retrospective, monocentric study included 465 patients with confirmed diagnosis of (a) alcoholic (n = 221) and (b) other-than-alcoholic (n = 244) cirrhosis. Standard T2-weighted single-slice images at the caudate lobe level were randomly split for training with fivefold cross-validation (85%) and testing (15%), balanced for (a) and (b). After automated upstream liver segmentation, two different ImageNet pre-trained convolutional neural network (CNN) architectures  were evaluated for classification of alcohol-related versus non-alcohol-related cirrhosis. The highest classification performance on test data was observed for ResNet50 with unfrozen pre-trained parameters, yielding an area under the receiver operating characteristic curve of 0.82  0.71–0.91) and an accuracy of 0.75 (95% CI 0.64–0.85). An ensemble of both models did not lead to significant improvement in classification performance. This proof-of-principle study shows that deep-learning classifiers have the potential to aid in discriminating liver cirrhosis etiology based on standard MRI. In particular, patients with liver cirrhosis have a concomitant risk for the development of hepatocellular carcinoma as well as complications arising from decompensation such as variceal bleeding or hepatic encephalopathy. Overall, the prevalence of chronic liver disease is internationally expected to grow within the next decades3. Thereby, identification of the underlying cause of disease is crucial as appropriate treatment may not only stop disease from progression, but in certain cases also may facilitate regression of fibrosis5. In the majority of countries, alcohol consumption still represents the leading cause of liver disease and is directly related to liver mortality6. In these patients, alcohol abstinence was shown to be critical for long-term outcome, may improve various aspects of disease severity and is also fundamental with regard to potential liver transplantation8. However, until experiencing severe complications such as acute decompensation, many patients with cirrhosis are unaware of their underlying condition3. Liver cirrhosis results from chronic inflammation and hence leads to distinct changes in hepatic morphology, which in part can be detected by high-resolution imaging methods such as MRI10.Many factors that contribute to the development of cirrhosis have been identified. The most common etiologies are alcohol consumption, obesity, and chronic viral infections12. In a previous study, a convolutional neural network (CNN) was already shown to be able to detect liver cirrhosis based on standard clinical MRI sequences with expert-level accuracy irrespective of etiology13. Therefore, the aim of this proof-of-concept study was to investigate deep learning for standard MRI based characterization of disease etiology, differentiating alcoholic- versus other-than-alcoholic cirrhosis.Although it has been described that the micro- and macroscopic appearance of cirrhosis in medical imaging varies to some extent according to the underlying etiology, the use of imaging features as a means to determine the cause of the disease has not been established yetThe study was approved by the Ethics Committee at the Medical Faculty of the Rheinische Friedrich-Wilhelms-Universität Bonn and the need for written informed consent was waived due to its retrospective, single-center nature. The research was performed in accordance with the Declaration of Helsinki. Patients with confirmed diagnosis of liver cirrhosis, defined by clinical manifestations of liver cirrhosis , laboratory parameters (e.g. presence of parameters of hepatocyte damage or impaired hepatic synthesis), and/or histopathological criteria, who underwent liver MRI for diagnostic purposes between 2017 and 2019 at the Department of Diagnostic and Interventional Radiology at the University Hospital of Bonn, were evaluated for inclusion. The clinical information management system of the relevant institution was used to derive clinical characteristics of the study population including the respective cause of liver cirrhosis. Patients with unknown causes of liver cirrhosis and with overlap of alcoholic and other causes were excluded. The final cohort was separated according to the underlying cause of liver cirrhosis into (a) patients with alcoholic liver cirrhosis and (b) other-than-alcoholic liver cirrhosis  with ResNet34 as backbone that was developed and validated on a dataset of 713 single slice T2- weighted MRI images13. The images were first normalized and image augmentation was applied during training. All patients underwent a standardized imaging protocol including a standard clinical respiratory triggered multi-slice turbo spin echo sequence with non-cartesian k-space filling (T2 MultiVane XD) on a clinical 1.5 Tesla  or 3.0 Tesla  scanner. This sequence was shown to be suitable for deep learning-based detection of liver cirrhosis in a previous studyFor imaging development of a classification CNN that differentiates patients with alcoholic liver cirrhosis and other-than-alcoholic liver cirrhosis, data were randomly split into a training (85%) and a hold-out test set (15%). Training was performed with fivefold cross-validation. An ensemble of the cross-validated models was applied to the test set.14. To investigate whether the use of a different pre-trained architecture than ResNet50 or an ensemble of two architectures is beneficial, a CNN with dense connections (DenseNet121) was additionally evaluated, which has fewer trainable parameters and is less computationally intensive compared to ResNet5015.A CNN with residual connections (ResNet50) with ImageNet pre-trained parameters was used, as this established architecture was shown to be suitable for the detection of liver cirrhosisFurthermore, two different training strategies were evaluated for ResNet50 and DenseNet121 in order to examine whether altering the pre-trained parameters of the CNN may impact classification performance. First, both networks were trained with frozen pre-trained parameters of the convolutional layers. In a second subsequent training run, the pre-trained convolutional layers of both networks were unfrozen with descending learning rates from the last to the first layer at several stages. Training was performed with Adam optimization, a cyclical learning rate scheme, and cross-entropy loss function. 16.Image regions that were particularly relevant to the classification task were highlighted by generating gradient-weighted class activation maps (Grad-CAM) for the test set17 were used for statistical analysis. Patient characteristics are expressed as frequencies or means with standard deviation, as appropriate. Classification accuracy (ACC), as well as receiver operating characteristic (ROC) analyses was performed for the cross-validation and the test sets for both studied CNN architectures  and both training strategies . For the test set, 95% confidence intervals were determined for ACC and AUC values. ROC and precision-recall curves were generated18. Grad-CAM images were visually inspected by one experienced radiologist (A.F.) and highlighted regions were categorized according to their anatomical localization as being predominantly situated in the right liver lobe, the left liver lobe, the portal region, the caudate lobe, or in the image background. Resulting categorical data were compared using either Fisher`s exact test (for a cell count of ≤ 5) or χ2 test (for a cell count > 5), as appropriate. The two-sided t-Test was used to compare differences between groups regarding continuous variables. P < 0.05 was set as the level of statistical significance.Prism 8 (GraphPad software), SPSS Statistics 24 (IBM), MedCalc 20.014  and Scikit-learn 0.23.2A total of 465 patients  were included. Of those, 47.5% (221/465) of patients had alcoholic liver cirrhosis. 52.5% (244/465) of patients had other-than-alcoholic liver cirrhosis.P = 0.110) and gender distribution  were observed between patients with alcoholic and other-than-alcoholic liver cirrhosis. There was no difference in weight between patients with alcoholic and other-than-alcoholic cirrhosis . Values for γ-glutamyltransferase were higher in patients with alcoholic cirrhosis compared to patients with other-than-alcoholic cirrhosis .Liver biopsy was performed in 64.8% 301/465) of patients. The most common causes of non-alcohol related liver cirrhosis were viral hepatitis , non-alcoholic fatty liver disease or non-alcoholic steatohepatitis , and autoimmune hepatitis . In 5% (12/244) of patients with other-than-alcoholic liver cirrhosis, etiology of liver disease was multifactorial. Causes of liver cirrhosis of the entire study population are summarized in detail by Table /465 of pSegmented images of the entire study population were randomly subdivided into a training , and a test set , with training sets being further split for fivefold cross-validation, balanced for patients with alcoholic and non-alcoholic cause of liver cirrhosis.Trained with frozen parameters, a mean accuracy (ACC) and mean area under the curve (AUC) of 0.69 and 0.78 was observed for ResNet50 and a mean ACC of 0.66 and a mean AUC of 0.78 was observed for DenseNet121 for all 5 validation splits . Also, training with unfrozen parameters did not differ significantly compared to frozen parameters for both ResNet50 and DenseNet121 .On test data, the classification performance of ResNet50 was higher than DenseNet121 when training with unfrozen parameters, however the difference was not statistically significant , nor for unfrozen  pre-trained parameters.The ensemble of the two architectures did not lead to statistically significant improvement on the test set compared to ResNet50, neither for frozen  and the portal area  were the imaging regions that were most frequently decisive for the classification process in both groups. Thereby, no significant differences regarding distribution of decisive imaging regions between the two patient groups were observed . Moreover, we exclusively used single-slice T2-weighted images of segmented livers and ImageNet pre-trained models, as these have been shown to be suitable for the detection of liver cirrhosis in a previous studyIn summary, the results of this proof-of-principle study demonstrate that discrimination between alcoholic and other-than-alcoholic cirrhosis based on clinical T2-weighted single-slice images is feasible with acceptable to excellent discrimination ability. This indicates the potential of deep learning for a more comprehensive assessment of diffuse liver disease.Supplementary Information.


This cross-sectional study examines gun-related deaths during the early stages of the US COVID-19 pandemic among children aged 0 to 17 years. In addition to direct effects on health, the COVID-19 pandemic has had indirect effects through stress and economic hardships imposed by mitigation efforts such as lockdowns. For children, the indirect health outcomes may be comparable with direct effects, because SARS-CoV-2–related morbidity and mortality are concentrated at older ages.4One potential indirect health outcome for children is gun-related deaths owing to increased psychological strain in families in combination with school closures, which lead to greater time spent by children at home, where guns are often kept. Although studies have documented increased gun-related deaths during the pandemic, only few studies have considered children and those focused on early stages of the pandemic.5 The study used publicly available data and was thus exempt from human subjects review at Harvard Medical School, and it followed the STROBE reporting guideline.This cross-sectional study analyzed gun-related deaths of children between January 1, 2014, and December 31, 2022, from the Gun Violence Archive (GVA), a repository of gun violence collected from more than 7500 law enforcement, media, government, and commercial sources (eMethods in We plotted 28-day moving averages of the count of children (aged 0 to 17 years) killed, comparing mid-March 2020 onwards to the trend in prior years to account for seasonal variation in shootings. In addition, we conducted an event study regression analysis with 2922 daily observations that modeled the daily count of child gun-related deaths as a function of a cubic time-trend and adjustments for month and day of week, assuming a pandemic period beginning March 16, 2020 (eMethods in the P < .05 using a 2-sided test. Stata version 15.1 (StataCorp) was used for analyses from March to April 2022.We conducted subgroup analyses according to child age, median household income in the Census tract where a death occurred (using the 2019 American Community Survey), and the percentage Black or Hispanic population in the Census tract . We used a significance threshold of Overall, 8044 shootings were analyzed and involved 8477 children killed. Of these 8477 children, 1888 (22.3%) were aged 0 to 11 years and 6589 (77.7%) were 12 to 17 years; 1657 (19.6%) were girls, 6676 (78.8%) were boys, and 144 (1.7%) had unknown gender; and 4914 (58.0%) were from a high-minority area (defined as having more than 50% Black or Hispanic population). An increase in deaths was observed from March 16, 2020, onwards , with an6 implying that our estimated increase in child gun-related deaths was comparable with COVID-19–related deaths in children. Increases in gun-related deaths were concentrated in areas with low median income or a high percentage of Black or Hispanic population, suggesting family and neighborhood socioeconomic conditions may be important mediators. They were also greater among children aged 12 to 17 years (The US Centers for Disease Control and Prevention (CDC) reports that 752 children died from COVID-19 from January 1, 2020, to December 31, 2021,17 years . FindingA comparison of gun-related deaths with CDC-reported COVID-19-related deaths suggests that the direct mortality effects of SARS-CoV-2 on children may be similar in magnitude to indirect outcomes mediated by gun violence. Factors that may have caused the observed increase in gun-related deaths among children are unknown and include psychological and economic strain due to the pandemic as well as greater time spent at home due to school closures. These findings highlight the potential importance of indirect health outcomes in children during the COVID-19 pandemic. Study limitations include analysis of a large gun violence database and confounding due to the study’s observational design. Given the coverage of the GVA in comparison with the CDC data, our figures may be underestimates.


Daily more than 3,000 children are injured or killed on the road, often along the school route. Road traffic crashes and resulting injuries are preventable. More can be done to reduce injuries and save lives. Traffic Conflict Techniques (TCTs) are simple methods of collecting observational data to evaluate the effectiveness of road safety interventions through counting and analyzing traffic conflicts. A TCT Toolkit was developed and piloted to analyze pedestrian-vehicle traffic conflicts in school zones in low- and middle-income countries.Three non-governmental organizations in Ghana, Vietnam, and Mexico applied three TCTs from the TCT Toolkit to collect traffic conflict data before (pre-intervention) and after (post-intervention) road safety intervention implementation. As the number of traffic conflicts was often less than 100, confidence intervals (CIs) based on gamma distributions were calculated for the traffic conflict rate. Using the calculated traffic conflict rate, the difference between pre- and post-intervention rates was assessed by determining overlap of the CIs. When CIs did not overlap, the difference was said to be statistically significant at the 0.05 level.For each method, results indicated a decrease in traffic conflicts between pre- and post-intervention data collection periods. Pre- and post-intervention traffic conflict rates with non-overlapping CIs demonstrated the results were statistically significant, providing evidence that the road safety interventions were effective.TCTs are relatively low-cost and simple techniques that provide an opportunity to base road safety improvement decisions on real-world data. TCTs are effective in objectively evaluating road safety interventions and can help decision-makers evaluate strategies for improving road safety, preventing injuries and saving lives. Police records and crash surveillance systems are retroactive approaches used to identify unsafe roads or intersections and make decisions on risk reduction strategies. A timelier proactive approach is to collect and analyze traffic conflict data. A traffic conflict occurs when two or more road users are on a collision course and risk crashing if one of the road users does not take evasive action  to change their movement/trajectory thus avoiding a crash.5 Traffic conflict data can provide objective evidence about suitable road safety intervention(s) and establish a baseline for evaluation to determine effectiveness of the intervention(s). Traffic Conflict Techniques (TCTs) are simple methods to help decision makers select and evaluate effective strategies for improving road safety and preventing crashes. Daily over 3,000 children are injured or killed on roads, often along the school route.This paper focuses on pedestrian-vehicle traffic conflicts between non-motorized student road users  and drivers of motorized vehicles  occurring in and around primary and secondary school zones in LMICs. We describe three methods of collecting pedestrian-vehicle traffic conflict data and provide case examples of how local non-governmental organizations (NGOs) have applied the methods to improve road safety in school zones in Ghana, Vietnam, and Mexico. 1 This TCT Toolkit is intended to serve as a comprehensive guide for applying TCTs and provides five methods to evaluate the impact of road safety interventions. This paper focuses on three of the more complex methods in the toolkit. A summary of each method is below; more detailed descriptions are in the toolkit.We utilized the “Traffic Conflict Technique (TCT) Toolkit: Making the Journey to and from School Safer for Students”.Traffic Conflict Technique MethodsMethod 1:6 Once a classification is assigned (A-D), these four vertically ordered numbers provide a final sequence. This sequence is used to determine the conflict severity, ranging between Grade 1 (slight) to Grades 2-4 (serious). Version 1: Institute of Highways and Transportation Conflicts Technique (IHTCT) Pedestrian-Vehicle Conflict Technique categorizes the severity of a traffic conflict based on four classifications, ranked 1-4, within four factors: Factor A (Time to Collision), Factor B (Severity of Evasive Action), Factor C (Complexity of Evasive Action), Factor D (Distance to Collision).Method 2: 7 A severity grading for both the pedestrian and vehicle is then recorded using three criteria: change in speed, change in direction, and vehicle acceleration.Version 2: Institute of Highways and Transportation Conflicts Technique (IHTCT) Pedestrian-Vehicle Conflict Technique first records each pedestrian-vehicle conflict as one of two types of interactions pertaining to the speed of the vehicle: 1) Steady Care-Pedestrian (SC-P) – the vehicle is traveling at a steady speed at the time of interaction with the pedestrian, and 2) Effective Shared Space (ESS) – the vehicle is stopped or travelling at very slow speeds.Method 3:8 The best way to measure pedestrian and vehicle speed and distance is through video recording or in-field observation. Experts of the Swedish TCT believe judging vehicle speed is relatively reliable with skill honed by practice. The speed of the road user is estimated at the moment the first road user (RU1) takes evasive action , with distance estimated from the exact location where RU1 takes the evasive action to where the collision would have occurred if the evasive action had not been taken. The data collector estimates the speed at the exact moment RU1 avoids collision and at the same time estimates the distance from RU1 to the point where a collision would have occurred without evasive action. Speed and distance are used to identify a Time-to-Accident (TA) indicator (time remaining for RU1 to successfully perform an evasive action), which is graphed with the speed on a severity curve to determine if the conflict is serious or non-serious. The Swedish Traffic Conflict Technique (TCT) requires two measurements-speed and distance-to determine the severity of a pedestrian-vehicle conflict.Data collection: road user and traffic conflict counts 2 Third, post-intervention traffic conflict data were collected; ideally occurring at multiple time points . In Ghana, Vietnam, and Mexico, road user counts and traffic conflict counts were collected at each school location on a typical day (non-holiday) to assess traffic conflict burden and change after road safety intervention implementation. Data collection occurred at three points throughout the study. First, road user counts were collected. Data collectors counted the number of pedestrians and vehicles passing through the data collection site, generally occurring one week before and during the same time as planned pre-intervention traffic conflict data collection. These counts provided baseline road user counts and served as a reference when analyzing and interpreting data. The analyst chose one road user  to use throughout analysis. Second, pre-intervention traffic conflict data were collected using method-specific data collection forms. A video camera was used to provide opportunity to review conflicts to ensure data quality.Study Sites3 selected the Oblogo Cluster of Schools located in Accra, Ghana; the Asia Injury Prevention (AIP) Foundation4 selected Minh Duc School in Ho Chi Minh City, Vietnam; and the Institute for Transportation and Development Policy (ITDP)5 selected the intersection of Boulevard Hermanos Serdán and Street Nte 37 within the school zone of the Benemérito Normal State Institute “Gral. Juan Crisóstomo Bonilla” (BINE) in Puebla de Zaragoza, Puebla, Mexico. Detailed data collection and infrastructure information is provided in Data collection occurred during the specific site’s morning and afternoon peak school travel times. For the intervention, the NGOs implemented various infrastructure changes. Amend implemented infrastructure changes because of results from pre-intervention data, community member consultations and technical advice from local authorities. AIP Foundation and ITDP previously planned infrastructure changes before traffic conflict study commencement.Data analysis Pre-intervention data analysis was conducted to help inform or validate selection of infrastructure interventions. Post-intervention data analysis was conducted and compared with pre-intervention results to determine effectiveness of the road safety interventions. The traffic conflict rate was calculated using the number of traffic conflicts divided by the number of hours the conflicts were counted  and by the number of vehicle road users passing through the observation area . Traffic conflict counts may vary between measurement periods even when the true underlying traffic conflict rate is unchanged; that is, the counts are subject to random variation and this variability should be taken into account when assessing significance of change. 6 The difference between the pre- and post-intervention rate was assessed by comparing the CIs. If the CIs do not overlap, the difference can be said to be statistically significant at the 0.05 level. Therefore, it can be concluded that the difference between the traffic conflict rates for pre- and post-intervention data is statistically significant.7For this study, the random variability is incorporated into the calculation of 95% confidence intervals (CIs) for traffic conflict rates. To simplify the CI calculations, the denominator of the rate (number of vehicles passing through the observation area) was assumed a constant . As the number of traffic conflicts was often <100, CIs based on gamma distributions were calculated for traffic conflict rate.1. Funding to develop the TCT Toolkit was provided by FIA Foundation via the CDC Foundation. 2. Videos were only used for the purpose of the study, securely stored and destroyed at the end of data validation.3. Amend is a road safety organization with offices in Ghana, Mozambique and Tanzania dedicated to improving environments to deliver safe and healthy journeys.4. The AIP Foundation is a nonprofit based in Vietnam dedicated to decreasing road crash causalities by providing safe interventions in LMICs.5. ITDP is a global organization making cities more livable by designing and implementing transport systems and policy solutions.6. The gamma distribution formula can be found in the National Vital Statistics Reports, Vol. 68, No. 9, June 24, 2019. 7. Comparing CIs is a conservative method for statistical significance-the difference between two rates may be statistically significant even if CIs overlap. Take caution when interpreting no significant difference between two rates, especially when the lower and upper limits overlap only slightly.8. Due to rain, ITDP collected data on Tuesday instead of Mon-day as planned.9. The children left school earlier because of shortened school hours during the post-intervention period.10. In Vietnam, post-intervention traffic conflict data collection occurred in one day over a 1.5-hour period. The assumption was 45-120 minutes of observation. Method 1: Version 1: IHTCT Pedestrian-Vehicle Conflict TechniqueIn Ghana, there was an average of 929 vehicles per hour through the study area. Pre-intervention observations resulted in 80 total conflicts per 4 hours (average 20 conflicts per hour) . The preIn Ghana, the pre-intervention conflicts varied in severity. Based on the classifications, 18 conflicts were slight (Grade 1), and 62 conflicts were serious  .Post-intervention observations resulted in 62 total conflicts per 4 hours (average 15.5 conflicts per hour) . The posMethod 2: Version 2: IHTCT Pedestrian-Vehicle Conflict TechniqueIn Vietnam, there was an average of 6,202 vehicles per hour through the study area. Pre-intervention observations resulted in 390 total conflicts (153 vehicle conflicts + 212 SC-P pedestrian conflicts + 25 ESS pedestrian conflicts) per 4 hours (average 97.5 conflicts per hour) . Over haPost-intervention observations resulted in 96 total conflicts (39 vehicle conflicts + 53 SC-P pedestrian conflicts + 4 ESS pedestrian conflicts) per 2 hours (average 48 conflicts per hour) . The posMethod 3: Swedish TCT In Mexico, there was a total of 1,149 vehicles per hour through the study area. Pre-intervention observations resulted in 147 total conflicts per 4 hours (average 36.8 conflicts per hour) . The prePost-intervention observations resulted in 35 total conflicts per 4 hours (average 8.8 conflicts per hour) . The posRoad traffic crashes and resulting injuries are preventable. In this paper, we presented a new evidence-based road safety resource and its application in three LMICs to make the journey to school safer. TCTs are methods of quickly collecting observational data to evaluate the effectiveness of road safety interventions by using real-world data and assessing the impact between pre- and post-intervention. Based on case studies in Ghana, Vietnam, and Mexico, TCTs achieved a timely evaluation of the impact of infrastructure interventions implemented in school zones. The results indicate these evaluations can be used in various geographical, cultural, and road safety settings. Since using TCTs is relatively simple, a rigorous evaluation will be more robust, but these observational data proved useful in proactively reducing traffic conflicts in these settings. Even in their simplest form, TCTs provide a way of basing road safety improvement decisions on real-world data and assessing the impact. Thus, TCTs can help decision-makers select and evaluate road safety interventions.Due to the COVID-19 pandemic, more time passed between pre- and post-intervention data collection than previously planned in Ghana. Changes in traffic patterns and vehicle flow related to COVID-19 and infrastructure modifications might have affected post-intervention data, but this was not able to be assessed. Although TCTs are relatively simple, researchers must be properly trained on each method’s principles and theories. Without time dedicated to training, disparate interpretations are possible. In addition, it can be difficult to achieve data collector consistency and objectivity when estimating the conflict severity because of differences in personal perceptions. The severities also might have changed due to unforeseen variations in traffic patterns during the COVID-19 pandemic.In Vietnam, inconsistency between pre- and post-intervention periods resulted in two days pre-intervention versus one day post-intervention-potentially altering the number of conflicts. One day of data collection could have artificially decreased the observed conflicts.AcknowledgementsWe acknowledge the collaborative contributions of Natalie Draisin and Agnieszka Krasnolucka at FIA Foundation. Several partners contributed to this paper: Clara Vadillo, Berenice Pérez, Sonia Medina, Gonzalo Peón Carballo with ITDP in Mexico City, Mexico; Simon Kalolo, Amma Oduro-Dankwah, Juliet Ado, Jeffrey Witte with Amend in Accra, Ghana; Minh Vo, Le Nguyen, Hong Bui, Quyen Bui, My Dang, Jimmy Tang, Mirjam Sidik with AIP Foundation in Ho Chi Minh City, Vietnam. Additionally, we thank Rose Rudd (CDC) for her analytic expertise.


Over the past decade, the Pacific region has experienced many arboviral outbreaks, including dengue, chikungunya, and Zika viruses. Papua New Guinea (PNG) has a high burden of arboviral diseases, but there is a paucity of knowledge about the epidemiology and circulation of these viruses in the country. In this study, we report investigations into suspected arboviral outbreaks of febrile disease in PNG from December 2015 to June 2017. DENV-1 and DENV-2 were the mostly commonly detected viruses, and low circulation of DENV-3 and ZIKV was also detected. DENV-4 and CHIKV were not detected during this period. Full genome sequencing of selected positive samples revealed that circulation was dominated by endemic indigenous strains belonging to DENV-1 (genotype IV) and DENV-2 (genotype C) that have been present in the country for up to a decade. A DENV-2 sublineage was also identified that has been associated with outbreaks of severe dengue in both PNG and the Solomon Islands. Aedes aegypti and Aedes albopictus mosquitoes and is caused by four genetically and antigenically related types of dengue virus (DENV 1–4) belonging to the genus Flavivirus and family Flaviviridae. The clinical manifestations of dengue can range from asymptomatic infections, to mild-moderate febrile illness, through to severe dengue, which is a potentially fatal complication characterized by plasma leakage and hemorrhagic manifestations.Dengue is an ongoing public health threat in many tropical countries, with > 100 million dengue infections and > 40,000 deaths per year.New Guinea C) was isolated from a U.S. soldier.,Papua New Guinea (PNG) is the largest Pacific Island nation, with a population of ∼9 million people. Health and social indicators in the country are poor compared with regional and global averages. Dengue circulation has been confirmed in PNG since 1944, when the DENV-2 type strain , and Zika virus (ZIKV) using previously published assays.Direct viral genome sequencing was attempted for all samples in which the target was detected with a sufficiently high viral load . Nucleic acid extracts were sent to Macrogen  where library preparation was conducted using the Nextera XT kit  and next-generation sequencing was completed using the NovaSeq 6000 .bbduk.sh command in bbmap v.38.84 (sourceforge.net/projects/bbmap). The nonhuman reads were then “cleaned” by removing Illumina adapters, trimming when the phred quality dropped below 20, and discarding remaining reads shorter than 50 bp with Trimmomatic v.0.38.Human-derived sequences were first removed from the raw reads by mapping to the human reference genome, build 38 (NCBI Accession GCF_000001405.39), using the https://github.com/nextstrain/dengue.git) was cloned using git and the config and meta-data files modified to include the NCBI genomes, plus those generated here. Snakemake v.5.26.1The high-quality complete dengue genomes were then used in a phylogenetic analysis with backbone dengue sequences downloaded from NCBI’s GenBank database. All complete dengue genomes from NCBI containing a minimum set of meta-data were used for both DENV-1 and DENV-2 analyses using a modified Nextstrain workflow.N = 2) and ZIKV (N = 2) were also detected in samples collected from Western Province. Dengue patients had an average age of 22.3 years, and 41.2% of patients were female.During the study period, outbreak investigations were carried out in Kiunga (Western Province), Port Moresby , Daru (Western Province), Wabag (Enga Province), Nipa (Southern Highlands Province), Sailum (Morobe Province), Kundiawa (Chimbu Province), and Rabaul (East New Britain Province) . Target Full genome sequences were generated for three DENV-1 strains associated with an outbreak in Kiunga, Western Province (2016), and seven DENV-2 strains associated with outbreaks in Port Moresby  and Daru . Sequence and phylogenetic analysis revealed that the PNG DENV-1 strains formed a monophyletic clade in Genotype IV . Viruses belonging to this lineage have been detected in multiple countries throughout the Pacific region but have not been detected more widely. E-gene phylogenetic analysis of DENV-2 strains revealed that the 2016 isolates belonged to an endemic lineage of viruses that have circulated in PNG since at least 2010 . This lineage likely evolved from a common ancestor of DENV-2 viruses originating from Indonesia, with viruses from Makassar (2007) basal to the PNG group. There is evidence that both of the DENV-1 and DENV-2 lineages endemic to PNG have spread to and caused outbreaks in other locations in the Pacific, such as the Solomon Islands, American Samoa, Fiji, Micronesia, and Australia.Envelope (E) glycoprotein gene phylogenetic trees were generated for the DENV-1 and DENV-2 strains to capture the majority of DENV sequences for which only the E-gene has been reported. Phylogenetic analysis of the E-gene for DENV-1 strains revealed that the viruses circulating in 2016 belonged to an endemic Genotype IV lineage that was first detected in the region  in 2002–2003 (,In the current study, we found that DENV-1 and DENV-2 were the dominant arboviruses associated with febrile illness outbreaks in PNG during 2015–2017. Circulation of DENV-3 and ZIKV was also detected; however, they were not associated with outbreaks but rather co-circulation with DENV-1 and/or DENV-2. DENV-4 was not detected during the study, and indeed has been infrequently detected in PNG in the past. Interestingly, the only previous detection of DENV-4 strains from PNG were reported from two patients who traveled to Australia in July and October 2016,,Both the DENV-1 and DENV-2 viruses sequenced in this study belonged to endemic lineages that had been circulating in the country for up to a decade, confirming previous studies that have demonstrated hyperendemic circulation of indigenous DENVs in PNG. This study and other recent studies,Further research is needed to better understand the circulation of these DENVs in the PNG and determine the burden of disease associated with dengue infections. This is particularly pertinent as there is evidence to suggest that the PNG DENV lineages have been involved with outbreaks in other countries in the Pacific region.Supplemental materials


Bladder cancer (BC) is the most common malignant tumour of the urinary system and one of the leading causes of cancer-related death. Cuproptosis is a novel form of programmed cell death, and its mechanism in tumours remains unclear. This study aimed to establish the prognostic signatures of cuproptosis-related lncRNAs and determine their clinical prognostic value. RNA sequencing data from The Cancer Genome Atlas were used to detect the expression levels of cuproptosis-related genes in BC. Cuproptosis-related lncRNAs linked to survival were identified using co-expression and univariate Cox regression. Furthermore, consensus cluster analysis divided the lncRNAs into two subtypes. Subsequently, we established a signature model consisting of seven cuproptosis-related lncRNAs  using least absolute shrinkage and selection operator regression. Survival analysis based on risk score showed that the overall survival and progression-free survival of patients in the high-risk group were worse than those in the low-risk group. Multivariate Cox analysis demonstrated the independent prognostic potential of this signature model for patients with BC. Moreover, age and clinical stage were also significantly correlated with prognosis. The constructed nomogram plots revealed good predictive power for the prognosis of patients with BC and were validated using calibration plots. Additionally, enrichment analysis, Single sample gene set enrichment analysis and immune infiltration abundance analysis revealed significant differences in immune infiltration between the two risk groups, with high levels of immune cell subset infiltrations observed in the high-risk group accompanied by various immune pathway activation. Moreover, almost all the immune checkpoint genes showed high expression levels in the high-risk group. Moreover, TIDE analysis suggested that the high-risk group was more responsive to immunotherapy. Finally, eight drugs with low IC50 values were screened, which may prove to be beneficial for patients in the high-risk group. BC can be divided into two types: non-muscular invasion BC (NMIBC) and muscular invasion BC (MIBC), based on the presence ofmuscular invasion2. With the emergence of targeted therapy and immunotherapy, patients with BC requiringneoadjuvant therapy have more combinatorial options based on traditional surgical approaches4. The combination of multiple treatment strategies prolongs patient survival to some extent; however, the prognosis remains poor and the overall recurrence and mortality rates for patients with BC remain high5. Therefore, the exploration of potentially effective prognostic biomarkers and molecular signatures is crucial for early diagnosis and clinical decision-making, and to aid in the further development of new therapies.Bladder cancer (BC) is one of the most common malignant tumours of the urinary system, with high morbidity and mortality rates worldwide. The proportion of male patients with BC is higher than that of females, wherein men with BC have the fourth highest incidence and eighth highest mortality rate among all cancers6. Copper (Cu) causes lipid-acylated protein aggregation and subsequent iron–sulfur cluster protein loss by directly binding to the lipid-acylated component of the tricarboxylic acid (TCA) cycle. This leads to proteotoxic stress and ultimately cell death7. As an essential mineral, Cu participates in various biological processes, including energy metabolism and signal transduction8. Current studies report that Cu levels are significantly altered in various malignant tumours, with higher levels in most tumours than normal tissues12. The dysregulation of Cu homeostasis plays an important role in the development, progression and metastasis of cancer. Cu acts as a tumour promoter and stimulates cancer cell proliferation and tumour growth13. A significant correlation between clinical stage and Cu levels in colorectal cancer has been reported14. Additionally, Cu exposure is involved in the occurrence of prostate cancer and could be a potential risk factor for carcinogenesis15. Furthermore, Cu plays a specific role in cancer progression and resistance to oncology drugs, and Cu chelators are reported to have anti-tumour and anti-metastatic benefits16. Currently, some metal-binding compounds, including Cu, have shown broad prospects in targeted therapy for tumours18. Thus, targeting Cu could be a novel approach in cancer therapy19.Recently, cuproptosis has been reported as a novel copper-dependent form of programmed cell death20. Numerous studies have reported that lncRNAs are closely related to tumorigenesis, metastasis and tumour immunity23. Additionally, it is a key regulatory factor in the development and progression of BC and can be used as a potential biomarker and therapeutic target24. Recently, it has been reported that the overexpression of lncRNA SOX2OT is associated with poor prognosis in BC. SOX2OT knockdown inhibited tumour growth and reduced metastasis in vivo25. Furthermore, lncRNA CASC9 has been reported as a carcinogenic lncRNA that activates the Wnt/β-catenin signalling pathway via the ceRNA mechanism, thereby playing an oncogenic role in BC26. Another study showed that lncRNA-LET reversed gemcitabine chemoresistance in BC, with reduced lncRNA-LET levels predicting poor prognosis in patients with BC27. Additionally, lncRNAs also regulate the PD-1/PD-L1 pathway of tumour cells and participate in immune response and immunotherapy28. The prognostic profile of lncRNAs has been widely reported in BC owing to their key role in tumours30. However, the role of cuproptosis-related lncRNAs in the prognosis and tumour immune microenvironment of BC remains unexplored.Long non-coding RNAs (lncRNAs) are a class of non-protein coding RNAs (ncRNAs) having a length of more than 200 nucleotides, which perform various biological functions in eukaryotes, involving mRNA stability, transcriptional regulation and translationTherefore, this study explores the role of cuproptosis-related lncRNAs in BC, which can contribute to the calcification of the potential association between cuproptosis and lncRNAs with immunotherapy. Public databases were used to mine lncRNAs overexpressed by cuproptosis genes to study the prognosis and immunological significance of cuproptosis-related lncRNAs. Subsequently, a prognostic feature of cuproptosis-related lncRNAs was established to predict survival in patients with BC, which provides novel ideas for exploring new molecular prognostic markers and potential therapeutic targets for BC.7 and are detailed in Table RNA-seq data and relevant clinical information were obtained from The Cancer Genome Atlas database ) on 17 April 2022, which contained 19 normal control samples and 412 BC samples. The FPKM value matrix for RNA-SEQ data provided on the official website was used for subsequent analysis. Mutation data of patients with BC were downloaded from TCGA database in MAF format. A total of 19 cuproptosis genes were extracted from published literature31. The expressions of tumour tissue and normal tissues were visualised using the pheatmap package to label genes with P values < 0.05. Firstly, the limma package was used to screen for lncRNAs that were significantly associated with cuproptosis-related genes, with the filtering criteria of correlation coefficient set at |Pearson R|> 0.4 and P < 0.001. Following this, dplyr, GGalluvial and GGplot2 packages were used to plot Sankey maps of cuproptosis and its associated lncRNAs. Additionally, univariate Cox regression analysis was performed using the survival package to select prognosis-related lncRNAs (P < 0.01) and forest maps were generated. Finally, the differential expression of lncRNAs related to prognosis was visualized using pheatmap. Furthermore, Reshape2 and GGPUBR packages were used to draw heatmaps and boxplots.Limma software package in R software  was used to extract the expression levels of 19 cuproptosis genes from TCGA dataset32. BC samples were classified into two different subtypes and survival differences among different subtypes were analysed. Additionally, Pheatmap package was used to plot a heatmap of the different clusters in correlation with clinicopathological features. Furthermore, the expression of immune checkpoint genes was compared across subtypes along with the correlation of immune checkpoint molecules with cuproptosis-related lncRNAs.To explore the molecular signatures of cuproptosis-related lncRNAs with prognostic value in BC, ConsensusClusterPlus and limma package were used33.We randomly divided the TCGA cohort into the training group and the test group in a 1:1 ratio and assessed the degree of difference in clinicopathological features between the two groups. The least absolute shrinkage and selection operator (LASSO) algorithm was used to perform regression analysis on the training group. The penalty parameter (λ) of the least criteria ten-fold cross-validation was used to select the model, and seven genes and their coefficients were determined to establish cuproptosis-related lncRNA signature models. Based on the normalized expression of each lncRNA (Expr) and its regression coefficient (Coef) risk score was calculated as follows: Risk Score = Univariate and multivariate Cox regression were used to analyse the prognostic value of risk scores and clinicopathological parameters and independent risk prognostic factors. Time-dependent receiver operating signature (ROC) curves and area under curve (AUC) values for 1-, 3- and 5-year survival were used to assess the predictive power of prognostic signatures. A nomogram plot was constructed based on the multivariate Cox regression analysis. Moreover, the consistency index and calibration curve were used to evaluate the consistency of the risk profile model using the sample information. The relationship between each clinicopathological parameter and risk score was evaluated based on the clinical information of patients in TCGA cohort, including age, sex, tissue grade and clinical stage. Furthermore, survival differences among different risk groups stratified by clinicopathological parameters were explored in our regression model.35.Patients in TCGA cohort were divided into two subgroups based on the median risk score. The ‘limma’ R package was run to screen for differential genes between the low-risk and high-risk groups according to the following criteria: |log 2 FC|≥ 1.5 and FDR < 0.05. The ‘clusterProfiler’ package was then applied for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and visualization36. The immune cell and pathway scores in the low- and high-risk groups were presented via a boxplot. Additionally, we compared the expression of immune checkpoint-related genes in different risk groups.Based on the enrichment results, we evaluated immune cell infiltration in TCGA dataset using seven algorithms, namely XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, Cibersort-ABS and CIBERSORT. Spearman correlation analysis was used to explore the correlation between immune cell subsets and risk scores. The ESTIMATE algorithm was then used to calculate the immunity scores, including immunity, stromal and ESTIMATE scores for the different risk groups. The ‘GSVA’ package was used to perform ssGSEA, which calculated the score of infiltrating immune cells and assessed the activation of immune-related pathways37.TIDE analysis was used to predict the potential efficacy of immunotherapy in different risk subgroups. The ‘pRRophetic’ package was then used to predict the sensitivity of patients with BC in the high- and low-risk groups to various oncology drugs, which was expressed as a semi-maximum inhibitory concentration (IC50)We collected bladder cancer and adjacent tissues from the Second Affiliated Hospital of Nanchang University from September 2021 to February 2022. RT-PCR was performed using Green qPCR Super Mix  with fluorescent quantitative Real-Time PCR System  according to the instructions. We normalized the relative lncRNA expression levels to B-actin, respectively. The sequences of primers (5′–3′) in this study were listed as follows: MIR4435-2HG, forward primer: GCCAGGACACAGCCATCTAAAGC, reverse primer: TTCCTCAGCATGGTGTGGTTCATTC; PPP1R26-AS1, forward primer: CGTGAAGGGCTTTAGGAAGGAGAAC, reverse primer: TCCACCACCACCAGACCAAGG; B-actin, forward primer: CACCATTGGCAATGAGCGGTTC, reverse primer: AGGTCTTTGCGGATGTCCACGT.All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the Ethics Committee of the Second Affiliate Hospital of Nanchang University (No. Review [2021] No. (115)), China. All of the human tissues used in the present study were obtained with written informed consent from all subjects and their legal guardians.P < 0.05 were considered statistically significant.We utilized the Wilcox rank-sum test for comparing mRNA and lncRNA expression levels between the two groups, and the Chi-square test was used to compare categorical variables. Kaplan–Meier and log-rank analyses were used to compare overall survival (OS) between different groups. Univariate and multivariate Cox regression was used to determine the independent prognostic value of risk models. The correlations among subtypes, clinicopathological features, risk score, immune checkpoint expression and immune infiltration level were examined using Pearson’s correlation test. All statistical analyses were performed using R software (version 4.1.1). P < 0.001. Finally, 762 co-expressed lncRNAs were obtained  from 2 to 10, the samples were divided into two well-distinguishable groups Fig. A. The OSA cohort of 406 patients with BC having complete survival-related and clinical information in TCGA was randomly divided into a training and a test group in a 1:1 ratio, and no significant bias was observed for each parameter of the clinicopathological signatures in both groups. A risk signature model consisting of seven lncRNAs was constructed based on LASSO regression analysis for the training group  were identified to establish the cuproptosis-associated lncRNAs signature. PPP1R26-AS1 has been reported to be upregulated in breast cancer tissues and associated with shorter OS. Moreover, PPP1R26-AS1has been reported to be a specific biomarker for the identification of luminal B subtypeTo assess the prognostic value of cuproptosis-associated lncRNA signatures in different risk subgroups, patients with BC were divided into high- and low-risk groups based on risk scores. The high-risk group had worse outcomes, and the risk score was an independent predictor of prognosis. The ROC curve also showed that the established lncRNA signatures had good prognostic value for BC. Kaplan–Meier analysis of different clinicopathological stratification showed that the feature model could predict the prognosis of patients with BC independently of the clinicopathological parameters. Moreover, the calibration curves confirmed a good agreement between the predicted and actual survival rates of the established nomogram for 1, 3 and 5 years. A comprehensive analysis of the established signature model revealed its accurate predictive ability for the prognosis of patients with BC.49. Moreover, lncRNA ADAMTS9-AS1 promoted the invasion and migration of BC cells through the activation of the PI3K/AKT/mTOR signalling pathway50.Furthermore, pathway enrichment analysis showed that cuproptosis-related lncRNAs were highly correlated with the PI3K−Akt signalling pathway. More than half of the patients with BC from the TCGA cohort had altered the PI3K pathway, and the suppression of PI3K expression inhibited the growth and migration of cancer cells. The combination of PI3K inhibitor and anti-PD-1 therapy improved the anti-tumour effect by improving the immune-stimulating tumour microenvironment51. Moreover, TIDE analysis suggested that the high-risk group was more responsive to immunotherapy, highlighting the fact that patients at a higher risk are more likely to benefit from immune checkpoint blockade immunotherapy. Currently, nivolumab and atezolizumab, as the representative PD-1 and PD-L1 inhibitors, respectively, have been proven to benefit patients with high-risk or metastatic BC53. Finally, we identified eight drugs that had better efficacy in the high-risk group for patients with BC. These findings could aid in drug screening and clinical treatment decisions.Notably, the cuproptosis-associated lncRNA signature model was strongly associated with tumour immune invasion. Immune cell bubble plots based on different platforms showed that patients in the high-risk group were associated with a higher abundance of immune cell infiltrates. ssGSEA further revealed that the high-risk group had significantly high levels of infiltration in all immune cell subsets. Except for type II interferon responses, there were significant differences in most immune pathways between the two risk subgroups, with the activation of numerous immune pathways in the high-risk group. The comparison of immune checkpoint activation between the different risk groups revealed that almost all immune checkpoint genes were highly activated in the high-risk group. Studies have reported that the expression level of immune checkpoint genes was highly correlated with the efficacy of immunotherapyAlthough we confirmed the validity of our feature model from multiple perspectives, there remain some limitations in our study. First, we used data obtained from an online database (TCGA). However, microarray data from other sources are biased, for example, Gene Expression Omnibus data lacks complete lncRNA information. Therefore, it was difficult to conduct external validation using other clinical databases. Second, in vitro and in vivo experiments could not be conducted to further verify differences in molecular expression levels and biological functions. However, clinical samples and reliable clinical data sets for external validation are required in the future.Cuproptosis-associated lncRNA feature models can predict survival in patients with BC and could have value as independent prognostic factors. Moreover, we systematically analysed the relationship between cuproptosis-associated lncRNA signatures, TMB, tumour microenvironment and immune cell infiltration and predicted potential therapeutic drugs. To the best of our knowledge, this is the first study to elucidate potential cuproptosis-related lncRNAs that could guide personalised treatments of patients with BC.Supplementary Table S1.Supplementary Table S2.


Background. Prophylactic vaccination has proven to be the most effective strategy to fight the COVID-19 pandemic. Methods. This was a prospective observational cohort study involving 30 predominantly antibody deficiency disorders (ADD)-afflicted adult patients on immunoglobulin replacement therapy vaccinated with three doses of the mRNA-1273 COVID-19 vaccine, and 10 healthy controls. Anti-RBD IgG antibodies were determined in plasma samples collected just before the first dose of mRNA-based COVID-19 vaccine and on weeks 4, 8, 24, and 28 following the first vaccination. Patients were categorized based on the levels of anti-RBD antibodies determined on w8 as non-, low-, and responders. Chi-square and Kruskal–Wallis tests were used to see if any variables correlated with humoral response levels. Any adverse effects of the mRNA-based vaccine were also noted. Results. The COVID-19 vaccine was safe and well-tolerated. The humoral response elicited at w8 after vaccination depended on the type of ADD, the type of immunoglobulin deficiency, the presence of granulomatous lymphocytic interstitial lung disease, recent use of immunosuppressive drugs, and the switched memory B cells counts. The third vaccine dose boosted humoral response in previous responders to second dose but seldom in non-responders. Conclusions: The humoral response of patients with predominant ADD depends mostly on the type of immunodeficiency and on the frequency of B and T cell populations. The first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus-induced disease COVID-19) was reported in Wuhan, China, in late 2019. Since then, the virus has spread worldwide and more than 598 million cases have been identified, causing more than 6.4 million deaths as of August 2022 [9 was repThe development of different vaccines that have proven to be safe and effective ,3 has drSince the beginning of the pandemic, various studies have been conducted in populations affected by primary or secondary immunodeficiencies , with interesting results. Before the availability of COVID-19 vaccines, different observational studies in populations with inborn errors of immunity (IEI) suggested that this group of patients has a higher risk of mild to severe infection than the general population, as well as a higher case-fatality ratio . Other sConcerning patients with SID, a study showed that the case-fatality ratio of COVID-19 was higher than for both the general population and patients with PID, though it should be noted that most SIDs in the study were related to hematological malignancies, a well-known independent risk factor for COVID-19 morbidity and mortality [Since patients with antibody deficiency disorders (ADD) with COVID-19 seem to have poorer prognoses, prophylactic vaccination might be the most effective preventive strategy . StudiesWe recently evaluated the cellular and humoral responses developed in ADD individuals after three mRNA-1273 vaccine doses (10.21203/rs.3.rs-1551351/v1) . The resThis was a prospective observational efficacy cohort-comparative single-center study conducted at the Hospital Universitari Germans Trias i Pujol (HUGTiP) located in Badalona, Spain. The HUGTiP is a 650-bed tertiary referral hospital that serves the 800,000 inhabitants of the north metropolitan area of Barcelona. The study’s goal was to elucidate which factors have an impact on vaccine-specific immune responses after three doses of mRNA-based COVID-19 vaccine in PID and SID patients on IRT. The study, which received prior approval from the institutional Board of Ethics (code: PI-21-107) began in April 2021 and ended in November 2021.Patients on IRT older than 18 years of age were selected from a database registry, maintained by the HUGTiP’s Infectious Diseases Department since 1986, of PID and SID patients who were clinically and analytically stable. Underlying IEIs in the PID population were defined according to the 2019 European Society for Immunodeficiencies (ESID) criteria .Patients with any of the following conditions were excluded: previous COVID-19 vaccination, vaccine-induced anaphylactic reaction, documented allergy to polysorbate or polyethylene glycol, and pregnant or breastfeeding women.Healthy controls who were age- and gender-balanced from the King cohort extension (HUGTiP/PI-20-217) and who had received two doses of mRNA-based COVID-19 vaccine were included for the purpose of comparing humoral responses to vaccination.Prior written informed consent was obtained from all participants.The primary endpoint was to categorize humoral responses in terms of antibody levels against Receptor Binding Domain (RBD) in ADD patients compared with healthy controls after two doses of COVID-19 vaccine.Secondary endpoints were to evaluate whether humoral responses to two doses of vaccine were related to any of the basal clinical and analytical characteristics of the patients, to determine whether a third dose was able to booster humoral responses, and to describe adverse effects related to any of the mRNA-based vaccine doses.All participants received two doses of the mRNA-based COVID-19 vaccine 28 days apart (April–May 2021) as described in (10.21203/rs.3.rs-1551351/v1) . Blood s® and S. pneumoniae polysaccharide antibody response, type of IRT, years on IRT, last available IgG trough levels, previous COVID-19 infection, and lymphocyte subpopulation counts were collected from clinical charts in an SPSS database.Variables such as demographic data, underlying conditions, immunosuppressive agents received in the last three months, previous rituximab, ESID registry-based categorization of immunodeficiency, years since PID/SID diagnosis, type of immunoglobulin (Ig) deficiency in PID/SID, GLILD, isohemagglutinin levels, Typhim ViWe performed telephonic clinical controls at weeks 4, 8, 24, and 28 and had participants complete a questionnaire reporting adverse effects after each dose of vaccine at weeks 5, 9, and 29.2SO4 (Sigma Aldrich). Signal was analyzed as the optical density at 492 nm with noise correction at 620 nm. Antigen-specific signal was calculated by subtracting background signal obtained for each sample in antigen-free wells. Results are shown as arbitrary units (AU)/mL.Anti-RBD IgG antibodies in plasma samples were determined as described in (10.21203/rs.3.rs-1551351/v1) . In brieChi-square or Fisher’s exact tests were performed to compare categorical variables from ADD patients classified according to their humoral response against the COVID-19 vaccine into three groups: non-responders (NR), low responders (LR), and responders (R).p-value was <0.05. Analyses were performed using IBM SPSS Statistics for Windows, version 24 .The Kruskal–Wallis test was used to compare numerical variables with levels of humoral response to vaccination. The differences were considered statistically significant when a Thirty patients with ADD on IRT and 10 healthy controls were included in the study.The main characteristics of ADD patients are shown in Local pain was the most frequently reported adverse effect of vaccination . No severe adverse effects were reported. Only one patient had to go on sick leave after the first and second dose, due to fever and vomiting, but did not require medical assistance or hospitalization. All adverse effects are summarized in All healthy controls yielded Spike-specific IgG titers at week 8. By contrast, patients with ADD showed heterogeneous humoral responses after two doses of mRNA-based vaccine.After comparing antibody levels in ADD patients and healthy controls at week 8, we classified SID and PID patients into three groups according to the level of their response: those whose antibody levels were undetectable were labeled ‘non-responders’ (NR) ; those who had lower titers than healthy controls were labeled ‘low responders’ (LR) , and those who had titers comparable to those of healthy controls were labeled ‘responders’ (R)  .Streptococcus pneumoniae or Salmonella typhi, and humoral response to two doses of COVID-19 vaccine. Although not significantly different, mean absolute B lymphocyte count and mean absolute CD4 positive lymphocyte count tended to be higher in R patients than in LR or NR, and mean percentage of transitional B lymphocytes tended to be lower in R than in LR and much lower than NR (p = 0.005). Concerning the type of Ig deficiency, 2 out of 16 patients (12.5%) with IgG, IgA, and IgM deficiency were R, while 5 out of 6 (83.3%) with IgG deficiency only were R. In the same line, 7 out of 16 (43.8%) with IgG, IgA, and IgM deficiency were NR, while none of the 6 patients with IgG deficiency only were NR (p = 0.02). Interestingly, all patients with GLILD were NR  (p = 0.004). Patients who had received immunosuppressive drugs in the last three months were more frequently NR  (p = 0.05). Finally, mean percentage of switched memory B cells was significantly higher in LR and R patients  than in NR patients (1.74) (p = 0.05).No statistically significant differences were observed between sex, age, underlying or IEI-related diseases, previous rituximab, IgG trough levels, isohemagglutinin levels or response to the polysaccharide vaccine against  than NR . By contAfter an additional third dose was administered to ADD patients at week 24, a booster effect was observed at week 28 in 19 out of 29 patients (one R patient refused to take a blood test at week 28): one NR , seven LR , and all R .A descriptive analysis of the 10 patients  who did not experience a booster effect after the third vaccine dose is outlined below. Six of them were females, and the mean age was 52.5 years (29–71). Eight were diagnosed with CVID, one was diagnosed with TI, and another (10%) was diagnosed with unPAD. Six of the 10 showing no booster effect were previously NR and four were LR. All three patients diagnosed with GLILD were NR after two doses and did not show a booster effect after the third dose of the vaccine. These three patients with GLILD had been on immunosuppressive drugs during the three months prior to the third vaccination . Four of the 10 showing no booster effect had received rituximab in the past, three of them because of GLILD and the other due to a lymphoma. Mean levels of B lymphocytes were 87.5 cel/µL (0–215).In this study, we analyzed humoral response in 30 ADD patients  after they had received three doses of an mRNA-based COVID-19 vaccine. The results of this analysis provide information that is crucial in giving our best assessment to ADD patients, who are a high-risk group of individuals, in the context of a global infectious pandemic.Although some similar research has recently been published ,15, the In our study we only included ADD patients on IRT, which implies a more advanced stage of the disease, sometimes involving recurrent infections and/or lung damage. This might be a factor underlying an inadequate vaccine response, given that the immune systems of this population are more impaired. On the other hand, studies in this area have been performed involving patients with different vaccine schemes including heterologous vaccination, which is already known to be more immunogenic than the homologous scheme . We consA phase 3 randomized, observer-blinded, placebo-controlled trial carried out in the United States evaluated the safety of the mRNA-1273 vaccine. A comparison of those who received two doses of mRNA-1273, 100ug each, with those who received placebo showed that local and systemic adverse events occurred more frequently in the mRNA-1273 group, especially after the second dose and among younger people (18 to <65 years of age). Pain was the most frequent injection-site event, while fever, headache, fatigue, myalgia, arthralgia, nausea or vomiting, and chills were the most frequent systemic adverse events. It should be noted that severity of systemic adverse events was higher after the second dose ,22. The It seems logical to suppose that COVID-19 vaccine response will be different depending on the ESID registry-based type of ADD, which mainly takes into account the specific immune impairment. However, the studies currently published on COVID-19 vaccine response in patients with IEI distinguish SID from PID patients and the different ESID registry-based levels of PID from each other from the outset. By contrast, the study design employed here has us allowed to demonstrate that the immune response correlates with the type of immunodeficiency and, in consequence, with the type of Ig deficiency. Specifically, we observed that, compared to healthy controls, patients with TI (1/1 NR) and CID (1/1 NR) had the poorest humoral response after two doses of vaccine, patients with CVID had heterogeneous responses, although overall they had poor responses , and that most unPAD patients  and all patients with SID  had a good humoral response. As in another studies , our worp = 0.004). It is well known that GLILD is a non-infectious complication of CVID patients, where lymphocytic granulomas commonly affect the lungs, and its treatment includes high doses of glucocorticoids, rituximab, or mycophenolate. As the presence of GLILD involves being affected with CVID and being on immunosuppressive drugs, both factors related to poorer humoral response, this could be a confusing factor. It should be noted that all patients with GLILD included here were receiving immunosuppressive treatment throughout the study.With regard to the patients in our study affected with GLILD, they all proved to be NR .The limitations of our study include a small sample size, the heterogeneity of the immunological impairments affecting the cohort, and the poor representation of some categories of IEI in this sample, especially for CID, TI and SID.Based on the results of this study, we can conclude that the COVID-19 vaccine is safe in PID and SID patients, and that three different groups  can be identified when compared to humoral response in healthy controls. ESID-registry-based classification , type of Ig deficiency, presence of GLILD, use of immunosuppressive drugs, and low percentage of switched B cells are all factors related to inadequate humoral response to mRNA-based COVID-19 vaccines. A third dose is not able to improve the humoral response in patients previously NR to two doses. Although these results should be confirmed in larger series, other preventive strategies such as tixagevimab/cilgavimab  or earlyFuture research on humoral and cellular response in larger samples should be performed in order to more precisely identify factors that predict inadequate vaccine response.


Surgical volunteer organizations have been severely limited during the ongoing coronavirus disease pandemic. Our purpose was to identify obstacles to surgical volunteer organizations secondary to COVID-19 and their responses.n = 17) and high donations surgical volunteer organizations . Univariate analyses were used to compare the two cohorts.Forty-one surgical volunteer organizations participated in a web-based survey . Respondents were separated into two groups: low donations surgical volunteer organizations . The three leading obstacles were finances/donations (78%), fewer volunteers (38%), and inadequate personal protective equipment (30%). In response, 39% of surgical volunteer organizations developed novel E-volunteering opportunities. For support, 85% of surgical volunteer organizations suggested monetary donations, 78% promotion through social media platforms, and 54% donation of personal protective equipment.Of responding surgical volunteer organizations, 34 (83%) were unable to maintain full functionality due to COVID-19; 27% of high donations vs. 0% of low donations surgical volunteer organizations (The majority of surgical volunteer organizations were unable to maintain full functionality due to stressors caused by COVID-19, including limitations on finances, volunteers, and personal protective equipment. The ongoing coronavirus disease COVID-19) pandemic has presented unforeseen obstacles for nearly every industry but has arguably impacted none more so than the healthcare industry, both domestically and abroad. The WHO identified a shortage of 4.3 million healthcare workers globally in 2011, which has been exacerbated by the pandemic and expected to increase to 18 million by 2030 (9 pandemiLow- and middle-income countries (LMICs) rely largely on the efforts of international volunteers and organizations to meet their increasing healthcare demands. Over the years, surgical volunteer organizations (SVOs) have collaborated with host countries to overcome hurdles and improve the quality and availability of care in low-resource settings , 5. HighThe purpose of this study was to survey international SVOs to identify obstacles the ongoing pandemic has presented and to elucidate solutions that have been implemented to maintain functionality.via SurveyMonkey . The survey was active from November 2020 to January 2021 and was re-sent to initial non-respondents.The present study was a prospective survey study of SVOs with at least one branch of operation in the United States or Canada. Organizations meeting this criteria were identified through previously published lists , 11 as wThe survey included questions about fundraising and maintaining volunteer operations as well as the use of e-volunteering, telecommunications, and social media. Questions were asked in relation to the start of the COVID-19 pandemic, which was defined as March 11, 2020 per the WHO . RespondDescriptive statistics were generated. Chi-squared tests were utilized to compare proportions as appropriate. Given that only 156 potential participants were identified and some of these organizations were expected to be nonoperational at time of the survey, a small sample size was considered acceptable for simple statistical methodology. All analyses and plot creation were conducted using SAS 9.4 . Alpha was set at 0.05.Of the 156 SVOs identified, 41 completed the survey (26%). There was a 100% completion rate for those who accessed the survey link. Thirty-four (83%) of responding SVOs were unable to maintain the full functionality of their organization in 2020 as a result of COVID-19.p = 0.02)  .p < 0.01). Fifty-five percent of SVOs placed a temporary pause on all their organization's volunteer efforts.With nearly a full cessation of sponsored, international volunteer trips during the pandemic, SVOs redistributed funding toward shipping of medical supplies (53%), provisions available for patients at volunteer sites (35%), host site staffing salary support (33%), host site infrastructure development (38%), and e-volunteering resources (15%) . Amongstp < 0.01). There was no significant difference between high donations (32%) and low donations SVOs (33%) in perceived need to develop host site infrastructure supporting telecommunications (p = 0.92).Regarding challenges encountered during the pandemic, SVOs struggled to maintain donations and financial support (78%), continue operations with fewer volunteers (38%), ensure all volunteers had adequate personal protective equipment , sustain internet connectivity to support telecommunications (25%), train in-country volunteers to take on new roles (23%), and preserve ongoing communications with volunteer sites (18%) . Forty-sp = 0.02). Twenty-five percent of SVOs with a moderate increase and 22% with no changes in personnel numbers indicated struggles maintaining operations. While 50% of SVOs with large personnel reductions and 67% with moderate reductions felt a need to development telecommunications infrastructure, 17% with no changes and 0% with a moderate increase in personnel indicated similar responses (p = 0.02).Thirty-four percent of survey respondents indicated a moderate to large decrease in their organization's total number of “in-country” personnel since the onset of COVID-19. Fifty-six percent noted no change, and 9.8% stated a moderate increase in personnel . OrganizResponding SVOs indicated the following as ways physician volunteers can contribute during the pandemic: monetary donations (85%), promoting participation and donations through social media platforms (78%), donation of medical supplies , and joining e-volunteering opportunities (39%) . Twenty In response to COVID-19, 39% of SVOs developed e-volunteering opportunities. Amongst these organizations, the number of active e-volunteers ranged from 3 to 100 per SVO at the time of survey. Sixty-three percent of responding organizations increased their use of telecommunications with their host sites, most commonly Zoom (53%), WhatsApp (10%), Skype (10%), Microsoft Teams (10%), and GoToMeeting 7.5%) , 7. Addi.5% , 7. The COVID-19 pandemic has presented unforeseen challenges for surgical volunteer organizations that have required adaptability to maximize functionality. With the majority of partnering host sites communicating similar or higher need of international assistance compared with pre-COVID levels, how can we continue to effectively contribute despite the constraints of the pandemic? To our knowledge, this study is the first to investigate the impact of the pandemic on international volunteering.As suspected, an overwhelming majority of responding SVOs reported a diminished level of functionality in 2020 along with a substantial drop in their annual donations. Kobayashi et al.  proposedEven though many organizations were unable to maintain full functionality, SVOs discovered new and creative ways to stay involved with their host countries. Many organizations provided more humanitarian aid, including supplying shelters, blankets, clothes, food, hygiene products and other necessities. Others aimed to provide PPE to not only their healthcare workers but also patients and staff in neighboring hospitals. Despite varying nonoperational periods, 59% of high donations SVOs and 13% of low donations SVOs were able to reallocate funds to development of host site infrastructure in order to help partner countries better cope with the restraints of the pandemic.Further, the majority of SVOs experienced either no change or an increase in the number of in-country personnel. With donations dropping at least 25% for most SVOs, it is unclear how organizations were able to retain and pay the salaries of host country staff. One SVO that managed to continue full operation ascribed their success to a decades-long history of missionary work and a deep local network with which they maintained open communications. Multiple other organizations stated that their LMIC volunteer activity is only augmented by, but not dependent on, foreign short-term volunteers. In fact, some note an increase in surgical volumes during the pandemic driven largely by “in-country” personnel. The efficacy of international SVOs that rely on short-term volunteers is frequently called into question, with previous studies citing the paucity of long-term outcomes and the potential creation of reverse innovation, or perpetuated dependency on high-income country volunteers , 15. TheIn addition, 39% of responding SVOs developed new e-volunteering opportunities since the start of the pandemic and recommended them for physician volunteers hoping to get involved in international healthcare. E-volunteering is defined as “providing remote mentoring, research support, curricula development, case consultation, etc. through email, Skype, and other web-based technologies.” Moreover, as the general public increased reliance on telecommunications during 2020, so did SVOs . InteresIn line with an increased role for telecommunications during COVID-19, many SVOs and their international partners turned to online platforms and social media as a means of training volunteers, connecting with donors and volunteers, continuing to provide medical oversight remotely, and networking . SVOs revia a list generated by Ng-Kamstra et al. in 2015 , which may result in global patients reacquiring distrust in the overburdened healthcare centers and practices of Westernized medicine. SVOs must thus strategize to overcome the obstacles of the pandemic to continue providing surgical services, educating and training local workers and perhaps most importantly, improve capacity-building in LMICs.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.WD performed the data collection, statistical analysis, and wrote the initial manuscript. AX, WD, and SP contributed to data collection and contributed key sections of the manuscript. JO and JB contributed to the conception and design of the study and made critical revisions. All authors contributed to manuscript revision, read, and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.