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Posterior urethral valves (PUV) are the commonest cause of end-stage renal disease in children, but the genetic architecture of this rare disorder remains unknown. We performed a sequencing-based genome-wide association study (seqGWAS) in 132 unrelated male PUV cases and 23,727 controls of diverse ancestry, identifying statistically significant associations with common variants at 12q24.21 (p=7.8 × 10−12; OR 0.4) and rare variants at 6p21.1 (p=2.0 × 10-8; OR 7.2), that were replicated in an independent European cohort of 395 cases and 4151 controls. Fine mapping and functional genomic data mapped these loci to the transcription factor TBX5 and planar cell polarity gene PTK7, respectively, the encoded proteins of which were detected in the developing urinary tract of human embryos. We also observed enrichment of rare structural variation intersecting with candidate cis-regulatory elements, particularly inversions predicted to affect chromatin looping (p=3.1 × 10-5). These findings represent the first robust genetic associations of PUV, providing novel insights into the underlying biology of this poorly understood disorder and demonstrate how a diverse ancestry seqGWAS can be used for disease locus discovery in a rare disease.

[This corrects the article DOI: 10.1371/journal.pgen.1007329.].

As part of a broader collaborative network of exome sequencing studies, we developed a jointly called data set of 5,685 Ashkenazi Jewish exomes. We make publicly available a resource of site and allele frequencies, which should serve as a reference for medical genetics in the Ashkenazim (hosted in part at https://ibd.broadinstitute.org, also available in gnomAD at http://gnomad.broadinstitute.org). We estimate that 34% of protein-coding alleles present in the Ashkenazi Jewish population at frequencies greater than 0.2% are significantly more frequent (mean 15-fold) than their maximum frequency observed in other reference populations. Arising via a well-described founder effect approximately 30 generations ago, this catalog of enriched alleles can contribute to differences in genetic risk and overall prevalence of diseases between populations. As validation we document 148 AJ enriched protein-altering alleles that overlap with "pathogenic" ClinVar alleles (table available at https://github.com/macarthur-lab/clinvar/blob/master/output/clinvar.tsv), including those that account for 10-100 fold differences in prevalence between AJ and non-AJ populations of some rare diseases, especially recessive conditions, including Gaucher disease (GBA, p.Asn409Ser, 8-fold enrichment); Canavan disease (ASPA, p.Glu285Ala, 12-fold enrichment); and Tay-Sachs disease (HEXA, c.1421+1G>C, 27-fold enrichment; p.Tyr427IlefsTer5, 12-fold enrichment). We next sought to use this catalog, of well-established relevance to Mendelian disease, to explore Crohn's disease, a common disease with an estimated two to four-fold excess prevalence in AJ. We specifically attempt to evaluate whether strong acting rare alleles, particularly protein-truncating or otherwise large effect-size alleles, enriched by the same founder-effect, contribute excess genetic risk to Crohn's disease in AJ, and find that ten rare genetic risk factors in NOD2 and LRRK2 are enriched in AJ (p < 0.005), including several novel contributing alleles, show evidence of association to CD. Independently, we find that genomewide common variant risk defined by GWAS shows a strong difference between AJ and non-AJ European control population samples (0.97 s.d. higher, p

Anatomy is an essential component of clinical anesthesiology. The use of simulated patients and alternative materials, including embalmed human bodies, have become increasingly common during resident physician training due to the deemphasis on anatomical education during undergraduate medical training. In this report, the need for a more extensive review of relevant anatomy for the practice of anesthesiology was addressed by the design, evaluation, and dissemination of a human dissection course for procedural training of anesthesiology residents. The course utilized "freedom art" embalmed human bodies that allowed trainees to perform ultrasound-based regional and neuraxial techniques followed by detailed dissections of critical anatomy. One hundred and four residents participated in workshops and small group discussions and were evaluated using pre- and post-course assessments. A variety of clinical techniques were performed on the bodies, including regional blocks and neuraxial catheter placement. Insertion of peripheral/neuraxial catheters was successful, with dissections demonstrating the expected placement. Assessment scores improved following the course (pre-course mean 52.7%, standard deviation ([delta]) 13.1%; post-course mean 72.2%, [delta] 11.6%; t-test p < 0.0001) and feedback highlighted the usefulness and clinical relevance of course content. The ability to correlate ultrasound imaging with subsequent dissections of the "blocked" area and visualization of dye staining was extremely relevant for spatial understanding of the anatomy relevant for the clinical practice of these techniques. This manuscript demonstrates successful implementation of a comprehensive course for anesthesiology resident physicians to address gaps in undergraduate anatomical education and suggests that broader adoption of dissection courses may be beneficial for training anesthesiologists.

Abstract As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Here, we learn self-supervised representations using a Vision Transformer, trained on 1.7 M histology images across 23 healthy tissues in 838 donors from the Genotype Tissue Expression consortium (GTEx). Using these representations, we can automatically segment tissues into their constituent tissue substructures and pathology proportions across thousands of whole slide images, outperforming other self-supervised methods (43% increase in silhouette score). Additionally, we can detect and quantify histological pathologies present, such as arterial calcification (AUROC = 0.93) and identify missing calcification diagnoses. Finally, to link gene expression to tissue morphology, we introduce RNAPath, a set of models trained on 23 tissue types that can predict and spatially localise individual RNA expression levels directly from H&E histology (mean genes significantly regressed = 5156, FDR 1%). We validate RNAPath spatial predictions with matched ground truth immunohistochemistry for several well characterised control genes, recapitulating their known spatial specificity. Together, these results demonstrate how self-supervised machine learning when applied to vast histological archives allows researchers to answer questions about tissue pathology, its spatial organisation and the interplay between morphological tissue variability and gene expression.

The NADPH oxidase of neutrophils, essential for innate immunity, passes electrons across the phagocytic membrane to form superoxide in the phagocytic vacuole. Activity of the oxidase requires that charge movements across the vacuolar membrane are balanced. Using the pH indicator SNARF, we measured changes in pH in the phagocytic vacuole and cytosol of neutrophils. In human cells, the vacuolar pH rose to ~9, and the cytosol acidified slightly. By contrast, in Hvcn1 knock out mouse neutrophils, the vacuolar pH rose above 11, vacuoles swelled, and the cytosol acidified excessively, demonstrating that ordinarily this channel plays an important role in charge compensation. Proton extrusion was not diminished in Hvcn1-/- mouse neutrophils arguing against its role in maintaining pH homeostasis across the plasma membrane. Conditions in the vacuole are optimal for bacterial killing by the neutral proteases, cathepsin G and elastase, and not by myeloperoxidase, activity of which was unphysiologically low at alkaline pH.

Coeliac disease (CeD) is a highly heritable common autoimmune disease involving chronic small intestinal inflammation in response to dietary wheat. The human leukocyte antigen (HLA) region, and 40 newer regions identified by genome wide association studies (GWAS) and dense fine mapping, account for ∼40% of the disease heritability. We hypothesized that in pedigrees with multiple individuals with CeD rare [minor allele frequency (MAF)

Sepsis is the leading cause of child death globally with low- and middle-income countries (LMICs) bearing a disproportionate burden of pediatric sepsis deaths. Limited diagnostic and critical care capacity and health worker shortages contribute to delayed recognition of advanced sepsis (severe sepsis, septic shock, and/or multiple organ dysfunction) in LMICs. The aims of this study were to 1) assess the feasibility of a wearable device for physiologic monitoring of septic children in a LMIC setting and 2) develop machine learning models that utilize readily available wearable and clinical data to predict advanced sepsis in children. This was a prospective observational study of children with sepsis admitted to an intensive care unit in Dhaka, Bangladesh. A wireless, wearable device linked to a smartphone was used to collect continuous recordings of physiologic data for the duration of each patient's admission. The correlation between wearable device-collected vital signs (heart rate [HR], respiratory rate [RR], temperature [T]) and manually collected vital signs was assessed using Pearson's correlation coefficients and agreement was assessed using Bland-Altman plots. Clinical and laboratory data were used to calculate twice daily pediatric Sequential Organ Failure Assessment (pSOFA) scores. Ridge regression was used to develop three candidate models for advanced sepsis (pSOFA > 8) using combinations of clinical and wearable device data. In addition, the lead time between the models' detection of advanced sepsis and physicians' documentation was compared. 100 children were enrolled of whom 41% were female with a mean age of 15.4 (SD 29.6) months. In-hospital mortality rate was 24%. Patients were monitored for an average of 2.2 days, with > 99% data capture from the wearable device during this period. Pearson's r was 0.93 and 0.94 for HR and RR, respectively) with r = 0.72 for core T). Mean difference (limits of agreement) was 0.04 (-14.26, 14.34) for HR, 0.29 (-5.91, 6.48) for RR, and -0.0004 (-1.48, 1.47) for core T. Model B, which included two manually measured variables (mean arterial pressure and SpO2:FiO2) and wearable device data had excellent discrimination, with an area under the Receiver-Operating Curve (AUC) of 0.86. Model C, which consisted of only wearable device features, also performed well, with an AUC of 0.78. Model B was able to predict the development of advanced sepsis more than 2.5 hours earlier compared to clinical documentation. A wireless, wearable device was feasible for continuous, remote physiologic monitoring among children with sepsis in a LMIC setting. Additionally, machine-learning models using wearable device data could discriminate cases of advanced sepsis without any laboratory tests and minimal or no clinician inputs. Future research will develop this technology into a smartphone-based system which can serve as both a low-cost telemetry monitor and an early warning clinical alert system, providing the potential for high-quality critical care capacity for pediatric sepsis in resource-limited settings.

Complex human diseases can show significant heterogeneity between patients with the same phenotypic disorder. An outlier detection strategy was developed to identify variants at the level of gene transcription that are of potential biological and phenotypic importance. Here we describe a graphical software package (z-score outlier detection (ZODET)) that enables identification and visualisation of gross abnormalities in gene expression (outliers) in individuals, using whole genome microarray data. Mean and standard deviation of expression in a healthy control cohort is used to detect both over and under-expressed probes in individual test subjects. We compared the potential of ZODET to detect outlier genes in gene expression datasets with a previously described statistical method, gene tissue index (GTI), using a simulated expression dataset and a publicly available monocyte-derived macrophage microarray dataset. Taken together, these results support ZODET as a novel approach to identify outlier genes of potential pathogenic relevance in complex human diseases. The algorithm is implemented using R packages and Java.The software is freely available from http://www.ucl.ac.uk/medicine/molecular-medicine/publications/microarray-outlier-analysis.

Abstract Background Despite frequently providing non-military services in times of crisis, little systematic research has examined the perspectives of crisis-affected community members on the role of armed actors responding to humanitarian crises and public health emergencies. Methods To address this research gap, 175 interviews were conducted (2020–2021) amongst humanitarian and public health practitioners; armed actors; and crisis-affected community members across three country and four crisis contexts. Specifically, this effort included an Ebola outbreak in the Democratic Republic of the Congo; a refugee crisis on the Jordanian-Syrian border; and a volcanic eruption and COVID-19 outbreak in the Philippines. Data was analysed using grounded theory principles. Results Crisis-affected community members held diverse views. Non-state armed groups (NSAGs) and government armed actors were characterised as antagonists by some but supportive by others; gender issues were central to perceptions of armed actors, in ways that were both prejudicing and favourable. Overall perception was most closely linked to armed actor roles rather than the relative amount of conflict in a given area. Conclusions Findings nuance the relevant literature characterizing NSAGs as disruptive agents, and also the relevant literature that does not fully consider the nuances of gender and armed actor roles as deeply relevant to crisis-affected community perspectives on armed actors. These findings have important implications for both policy and academic discourse on militarization and localization.

A central focus of complex disease genetics after genome-wide association studies (GWAS) is to identify low frequency and rare risk variants, which may account for an important fraction of disease heritability unexplained by GWAS. A profusion of studies using next-generation sequencing are seeking such risk alleles. We describe how already-known complex trait loci (largely from GWAS) can be used to guide the design of these new studies by selecting cases, controls, or families who are most likely to harbor undiscovered risk alleles. We show that genetic risk prediction can select unrelated cases from large cohorts who are enriched for unknown risk factors, or multiply-affected families that are more likely to harbor high-penetrance risk alleles. We derive the frequency of an undiscovered risk allele in selected cases and controls, and show how this relates to the variance explained by the risk score, the disease prevalence and the population frequency of the risk allele. We also describe a new method for informing the design of sequencing studies using genetic risk prediction in large partially-genotyped families using an extension of the Inside-Outside algorithm for inference on trees. We explore several study design scenarios using both simulated and real data, and show that in many cases genetic risk prediction can provide significant increases in power to detect low-frequency and rare risk alleles. The same approach can also be used to aid discovery of non-genetic risk factors, suggesting possible future utility of genetic risk prediction in conventional epidemiology. Software implementing the methods in this paper is available in the R package Mangrove.

Abstract Introduction: In response to the COVID-19 pandemic, we rapidly implemented a plasma coordination center, within two months, to support transfusion for two outpatient randomized controlled trials. The center design was based on an investigational drug services model and a Food and Drug Administration-compliant database to manage blood product inventory and trial safety. Methods: A core investigational team adapted a cloud-based platform to randomize patient assignments and track inventory distribution of control plasma and high-titer COVID-19 convalescent plasma of different blood groups from 29 donor collection centers directly to blood banks serving 26 transfusion sites. Results: We performed 1,351 transfusions in 16 months. The transparency of the digital inventory at each site was critical to facilitate qualification, randomization, and overnight shipments of blood group-compatible plasma for transfusions into trial participants. While inventory challenges were heightened with COVID-19 convalescent plasma, the cloud-based system, and the flexible approach of the plasma coordination center staff across the blood bank network enabled decentralized procurement and distribution of investigational products to maintain inventory thresholds and overcome local supply chain restraints at the sites. Conclusion: The rapid creation of a plasma coordination center for outpatient transfusions is infrequent in the academic setting. Distributing more than 3,100 plasma units to blood banks charged with managing investigational inventory across the U.S. in a decentralized manner posed operational and regulatory challenges while providing opportunities for the plasma coordination center to contribute to research of global importance. This program can serve as a template in subsequent public health emergencies.

ABSTRACT Early COVID-19 convalescent plasma (CCP) transfusion to outpatients with COVID-19 decreases progression to hospitalization, but the mechanism of how CCP reduces severity is unknown. Among 882 COVID-19 participants transfused with CCP or control plasma in a randomized controlled trial, 21 cytokines and chemokines were measured using electrochemiluminescence assays. Wilcoxon rank sum tests were used to evaluate the difference between early (transfused within 5 days of symptom onset) CCP vs early control plasma and late (transfused 6–9 days after symptom onset) CCP vs late control plasma at each visit. Linear mixed-effect models were used to assess the difference in the slope of cytokine change. Median cytokine and chemokine levels were similar between the early CCP and early control groups pre-transfusion. At the day 14 visit, only the median IL-6 (P = 0.014) and IL-16 (P = 0.036) levels were lower in the early CCP group compared to the early control group, but these differences were not statistically significant after correcting for multiple comparisons (requiring P < 0.0024). IL-6 levels decreased significantly faster in the early CCP group from screening to the day 14 visit compared to the early control group (P < 0.001). No difference was observed in the slope of cytokine change from screening to day 90 between early CCP and early control groups. Late control and late CCP arms showed similar cytokine and chemokine levels through study follow-up. One mechanism by which early CCP transfusion reduces hospitalization may be by decreasing IL-6 levels, as the reduction is associated with better recovery from COVID-19. IMPORTANCE This study examined the role that cytokines may have played in the beneficial outcomes found when outpatient individuals infected with SARS-CoV-2 were transfused with COVID-19 convalescent plasma (CCP) early in their infection. We found that the pro-inflammatory cytokine IL-6 decreased significantly faster in patients treated early with CCP. Participants with COVID-19 treated with CCP later in the infection did not have the same effect. This decrease in IL-6 levels after early CCP treatment suggests a possible role of inflammation in COVID-19 progression. The evidence of IL-6 involvement brings insight into the possible mechanisms involved in CCP treatment mitigating SARS-CoV-2 severity.

Abstract Inflammatory bowel disease (IBD) is a group of chronic digestive tract inflammatory conditions whose genetic etiology is still poorly understood. The incidence of IBD is particularly high among Ashkenazi Jews. Here, we identify 8 novel and plausible IBD-causing genes from the exomes of 4453 genetically identified Ashkenazi Jewish IBD cases (1734) and controls (2719). Various biological pathway analyses are performed, along with bulk and single-cell RNA sequencing, to demonstrate the likely physiological relatedness of the novel genes to IBD. Importantly, we demonstrate that the rare and high impact genetic architecture of Ashkenazi Jewish adult IBD displays significant overlap with very early onset-IBD genetics. Moreover, by performing biobank phenome-wide analyses, we find that IBD genes have pleiotropic effects that involve other immune responses. Finally, we show that polygenic risk score analyses based on genome-wide high impact variants have high power to predict IBD susceptibility.

Abstract This article presents a new typology for humanitarian-military relations (HMR). This typology can serve as an analytical framework for assessing, during humanitarian emergencies, how civilian responders can and should engage with armed actors. The typology considers two factors: (1) the nature of crisis-affected population’s perceptions of an armed actor, and (2) the extent of alignment of civilian responders’ and armed actors’ interests and objectives. This typology is empirically rooted in an in-depth analysis of HMR across four humanitarian response contexts: (1) the Kivu Ebola Epidemic in the Democratic Republic of the Congo, (2) the Rukban forced displacement crisis along the Jordan-Syria border, (3) the Taal volcano eruption in the Philippines, and (4) the COVID-19 pandemic in the Philippines. The analysis presented in this article is based on 175 qualitative interviews conducted with civilian responders, armed actors, and crisis-affected individuals across these contexts.

IntroductionDuring the 2018–2020 Ebola virus disease (EVD) outbreak in the eastern part of the Democratic Republic of the Congo (DRC), prevention and control measures, such as Ebola vaccination were challenging by community mistrust. We aimed to understand perceptions regarding Ebola vaccination and identify determinants of Ebola vaccine uptake among HCWs.MethodsIn March 2021, we conducted a cross-sectional survey among 438 HCWs from 100 randomly selected health facilities in three health zones (Butembo, Beni, Mabalako) affected by the 10th EVD outbreak in North Kivu, DRC. HCWs were eligible if they were ≥ 18 years and were working in a health facility during the outbreak. We used survey logistic regression to assess correlates of first-offer uptake (i.e., having received the vaccine the first time it was offered vs. after subsequent offers).ResultsOf the 438 HCWs enrolled in the study, 420 (95.8%) reported that they were eligible and offered an Ebola vaccine. Among those offered vaccination, self-reported uptake of the Ebola vaccine was 99.0% (95% confidence interval (CI) [98.5–99.4]), but first-offer uptake was 70.2% (95% CI [67.1, 73.5]). Nearly all HCWs (94.3%; 95% CI [92.7–95.5]) perceived themselves to be at risk of contracting EVD. The most common concern was that the vaccine would cause side effects (65.7%; 95% CI [61.4–69.7]). In the multivariable analysis, mistrust of the vaccine source or how the vaccine was produced decreased the odds of first-time uptake.DiscussionOverall uptake of the Ebola vaccine was high among HCWs, but uptake at the first offer was substantially lower, which was associated with mistrust of the vaccine source. Future Ebola vaccination efforts should plan to make repeated vaccination offers to HCWs and address their underlying mistrust in the vaccines, which can, in turn, improve community uptake.

Objective: Framework Analysis (FA) and Applied Thematic Analysis (ATA) are qualitative methods that have not been as widely used/cited compared to content analysis or grounded theory. This paper compares methods of FA with ATA for mobile health (mHealth) research. The same qualitative data were analyzed separately using each methodology. The methods, utility, and results of each are compared, and recommendations made for their effective use. Methods: Formative qualitative data were collected in eight focus group discussions with physicians and nurses from three hospitals in Bangladesh. Focus groups were conducted via video conference in the local language, Bangla, and audio recorded. Audio recordings were used to complete a FA of participants’ opinions about key features of novel mHealth application (app) designed to support clinical management in patients with acute diarrhea, called FluidCalc: Rehydration Calculator for Acute Diarrhea. The resulting framework analysis was shared with the app design team and used to guide iterative development of the product for a validation study of the app. Subsequently, focus group audio recordings were transcribed in Bangla then translated into English for ATA; transcripts and codes were entered into NVivo qualitative analysis software. Code summaries and thematic memos explored the clinical utility of FluidCalc including clinicians’ attitudes about using this decision support tool. Results: Each of the two methods contributes differently to the research goal and have different implications for an mHealth research timeline. Recommendations for the effective use of each method in app development include: using FA for data reduction where specific outcomes are needed to make programming and design decisions and using ATA to capture the more nuanced issues that guide use, product implementation, training, and workflow. Conclusions: By describing how both analytical methods were used in this context, this paper provides guidance and an illustration for use of these two methods, specifically in mHealth design.

Rapid diagnostic tools for children with Ebola virus disease (EVD) are needed to expedite isolation and treatment. To evaluate a predictive diagnostic tool, we examined retrospective data (2014–2015) from the International Medical Corps Ebola Treatment Centers in West Africa. We incorporated statistically derived candidate predictors into a 7-point Pediatric Ebola Risk Score. Evidence of bleeding or having known or no known Ebola contacts was positively associated with an EVD diagnosis, whereas abdominal pain was negatively associated. Model discrimination using area under the curve (AUC) was 0.87, which outperforms the World Health Organization criteria (AUC 0.56). External validation, performed by using data from International Medical Corps Ebola Treatment Centers in the Democratic Republic of the Congo during 2018–2019, showed an AUC of 0.70. External validation showed that discrimination achieved by using World Health Organization criteria was similar; however, the Pediatric Ebola Risk Score is simpler to use.

We conducted a retrospective cohort study to assess the effect vaccination with the live-attenuated recombinant vesicular stomatitis virus–Zaire Ebola virus vaccine had on deaths among patients who had laboratory-confirmed Ebola virus disease (EVD). We included EVD-positive patients coming to an Ebola Treatment Center in eastern Democratic Republic of the Congo during 2018–2020. Overall, 25% of patients vaccinated before symptom onset died compared with 63% of unvaccinated patients. Vaccinated patients reported fewer EVD-associated symptoms, had reduced time to clearance of viral load, and had reduced length of stay at the Ebola Treatment Center. After controlling for confounders, vaccination was strongly associated with decreased deaths. Reduction in deaths was not affected by timing of vaccination before or after EVD exposure. These findings support use of preexposure and postexposure recombinant vesicular stomatitis virus–Zaire Ebola virus vaccine as an intervention associated with improved death rates, illness, and recovery time among patients with EVD.

Despite receiving less attention than high-income countries, low- and middle-income countries (LMICs) experienced more than 85% of global excess deaths during the first two years of the COVID-19 pandemic. Due to the unprecedented speed and scale of the COVID-19 pandemic, which placed large demands on government capacity, many LMICs relied on civil society organizations (CSOs) to assist in implementing COVID-19 response programs. Yet few studies have examined the critical role CSOs played in mitigating the effects of the COVID-19 pandemic in low resource settings. This study explored the CSO response to COVID-19 in five of the most heavily impacted LMICs in the Global South. Interviews were conducted from May to August 2021 with a purposive sample of CSO key informants within each of the five countries. A total of 52 CSOs were selected from which 53 key informants were interviewed either via Zoom or by phone. Interviews were coded and analyzed using NVivo or MAXQDA2020. Out of the 52 CSOs selected, 24 were national organizations, 8 were regional, and 20 were local. CSOs fell into six categories: community-based organizations, non-governmental organizations, unions/professional organizations, campaigns/social movements, research organizations/think tanks, and networks/coalitions. CSOs across all five countries adapted their missions, stretched their resources, and performed a wide range of activities that fit into five programmatic areas: food security and livelihood support, public health and medical care, cash transfer programs, risk communication and community education, and needs assessment. This qualitative analysis demonstrates the critical role CSOs played in supplementing government emergency aid response by delivering necessary resources and supporting highly vulnerable populations during the COVID-19 pandemic, as well as the primary challenges they faced in doing so. Given the generally weak state of public capacity in the LMICs studied, this role was vital to responding to the pandemic.

ObjectiveThis study aims to investigate maternal, fetal, and perinatal outcomes during the 2018-2020 Ebola outbreak in Democratic Republic of Congo (DRC).MethodsMortality between pregnant and non-pregnant women of reproductive age admitted to DRC's Mangina Ebola treatment center (ETC) were compared using propensity score matching. Propensity scores were calculated using age, initial Ebola viral load, Ebola vaccination status, and investigational therapeutic. Additionally, fetal and perinatal outcomes of pregnancies were also described.ResultsTwenty-seven pregnant women were admitted to the Mangina ETC during December 2018-January 2020 among 162 women of childbearing age. We found no evidence of increase mortality among pregnant women compared to non-pregnant women (relative risk:1.0, 95%CI: 0.58-1.72). Among surviving mothers, pregnancy outcomes were poor with at least 58% (11/19) experiencing loss of pregnancy while 16% (3/19) were discharged with viable pregnancy. Two mothers with viable pregnancies were vaccinated, and all received investigational therapeutics. Two live births occurred, with one infant surviving after the infant and mother received an investigational post-exposure prophylaxis and Ebola therapeutic respectively.ConclusionsPregnancy was not associated with increased mortality among women with EVD in the Mangina ETC. Fetal and perinatal outcomes remained poor in pregnancies complicated by EVD, though novel therapeutics may have potential for improving these outcomes.

Diarrhea continues to be a leading cause of death for children under-five. Amongst children treated for acute diarrhea, mortality risk remains elevated during and after acute medical management. Identification of those at highest risk would enable better targeting of interventions, but available prognostic tools lack validation. We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build clinical prognostic models (CPMs) to predict death (in-treatment, after discharge, or either) in children aged ≤59 months presenting with moderate-to-severe diarrhea (MSD), in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using repeated cross-validation. We used data from the Kilifi Health and Demographic Surveillance System (KHDSS) and Kilifi County Hospital (KCH) in Kenya to externally validate our GEMS-derived CPM. Of 8060 MSD cases, 43 (0.5%) children died in treatment and 122 (1.5% of remaining) died after discharge. MUAC at presentation, respiratory rate, age, temperature, number of days with diarrhea at presentation, number of people living in household, number of children

Background: Nearly 150 million children under-5 years of age were stunted in 2020. We aimed to develop a clinical prediction rule (CPR) to identify children likely to experience additional stunting following acute diarrhea, to enable targeted approaches to prevent this irreversible outcome. Methods: We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build predictive models of linear growth faltering (decrease of ≥0.5 or ≥1.0 in height-for-age z-score [HAZ] at 60-day follow-up) in children ≤59 months presenting with moderate-to-severe diarrhea, and community controls, in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using fivefold cross-validation. We used the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study to (1) re-derive, and (2) externally validate our GEMS-derived CPR. Results: Of 7639 children in GEMS, 1744 (22.8%) experienced severe growth faltering (≥0.5 decrease in HAZ). In MAL-ED, we analyzed 5683 diarrhea episodes from 1322 children, of which 961 (16.9%) episodes experienced severe growth faltering. Top predictors of growth faltering in GEMS were: age, HAZ at enrollment, respiratory rate, temperature, and number of people living in the household. The maximum area under the curve (AUC) was 0.75 (95% confidence interval [CI]: 0.75, 0.75) with 20 predictors, while 2 predictors yielded an AUC of 0.71 (95% CI: 0.71, 0.72). Results were similar in the MAL-ED re-derivation. A 2-variable CPR derived from children 0–23 months in GEMS had an AUC = 0.63 (95% CI: 0.62, 0.65), and AUC = 0.68 (95% CI: 0.63, 0.74) when externally validated in MAL-ED. Conclusions: Our findings indicate that use of prediction rules could help identify children at risk of poor outcomes after an episode of diarrheal illness. They may also be generalizable to all children, regardless of diarrhea status. Funding: This work was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award NIH T32AI055434 and by the National Institute of Allergy and Infectious Diseases (R01AI135114).

Populations affected by humanitarian crises and emerging infectious disease outbreaks may have unique concerns and experiences that influence their perceptions toward vaccines. In March 2021, we conducted a survey to examine the perceptions toward COVID-19 vaccines and identify the factors associated with vaccine intention among 631 community members (CMs) and 438 healthcare workers (HCWs) affected by the 2018–2020 Ebola Virus Disease outbreak in North Kivu, Democratic Republic of the Congo. A multivariable logistic regression was used to identify correlates of vaccine intention. Most HCWs (81.7%) and 53.6% of CMs felt at risk of contracting COVID-19; however, vaccine intention was low (27.6% CMs; 39.7% HCWs). In both groups, the perceived risk of contracting COVID-19, general vaccine confidence, and male sex were associated with the intention to get vaccinated, with security concerns preventing vaccine access being negatively associated. Among CMs, getting the Ebola vaccine was associated with the intention to get vaccinated (RR 1.43, 95% CI 1.05–1.94). Among HCWs, concerns about new vaccines’ safety and side effects (OR 0.72, 95% CI 0.57–0.91), religion’s influence on health decisions (OR 0.45, 95% CI 0.34–0.61), security concerns (OR 0.52, 95% CI 0.37–0.74), and governmental distrust (OR 0.50, 95% CI 0.35–0.70) were negatively associated with vaccine perceptions. Enhanced community engagement and communication that address this population’s concerns could help improve vaccine perceptions and vaccination decisions. These findings could facilitate the success of vaccine campaigns in North Kivu and similar settings.

BackgroundThe availability of mobile clinical decision support (CDS) tools has grown substantially with the increased prevalence of smartphone devices and apps. Although health care providers express interest in integrating mobile health (mHealth) technologies into their clinical settings, concerns have been raised, including perceived disagreements between information provided by mobile CDS tools and standard guidelines. Despite their potential to transform health care delivery, there remains limited literature on the provider’s perspective on the clinical utility of mobile CDS tools for improving patient outcomes, especially in low- and middle-income countries. ObjectiveThis study aims to describe providers’ perceptions about the utility of a mobile CDS tool accessed via a smartphone app for diarrhea management in Bangladesh. In addition, feedback was collected on the preliminary components of the mobile CDS tool to address clinicians’ concerns and incorporate their preferences. MethodsFrom November to December 2020, qualitative data were gathered through 8 web-based focus group discussions with physicians and nurses from 3 Bangladeshi hospitals. Each discussion was conducted in the local language—Bangla—and audio recorded for transcription and translation by the local research team. Transcripts and codes were entered into NVivo (version 12; QSR International), and applied thematic analysis was used to identify themes that explore the clinical utility of an mHealth app for assessing dehydration severity in patients with acute diarrhea. Summaries of concepts and themes were generated from reviews of the aggregated coded data; thematic memos were written and used for the final analysis. ResultsOf the 27 focus group participants, 14 (52%) were nurses and 13 (48%) were physicians; 15 (56%) worked at a diarrhea specialty hospital and 12 (44%) worked in government district or subdistrict hospitals. Participants’ experience in their current position ranged from 2 to 14 years, with an average of 10.3 (SD 9.0) years. Key themes from the qualitative data analysis included current experience with CDS, overall perception of the app’s utility and its potential role in clinical care, barriers to and facilitators of app use, considerations of overtreatment and undertreatment, and guidelines for the app’s clinical recommendations. Participants felt that the tool would initially take time to use, but once learned, it could be useful during epidemic cholera. Some felt that clinical experience remains an important part of treatment that can be supplemented, but not replaced, by a CDS tool. In addition, diagnostic information, including mid-upper arm circumference and blood pressure, might not be available to directly inform programming decisions. ConclusionsParticipants were positive about the mHealth app and its potential to inform diarrhea management. They provided detailed feedback, which developers used to revise the mobile CDS tool. These formative qualitative data provided timely and relevant feedback to improve the utility of a CDS tool for diarrhea treatment in Bangladesh.

Background: Diarrheal illness is a leading cause of antibiotic use for children in low- and middle-income countries. Determination of diarrhea etiology at the point-of-care without reliance on laboratory testing has the potential to reduce inappropriate antibiotic use. Methods: This prospective observational study aimed to develop and externally validate the accuracy of a mobile software application (‘App’) for the prediction of viral-only etiology of acute diarrhea in children 0–59 months in Bangladesh and Mali. The App used a previously derived and internally validated model consisting of patient-specific (‘present patient’) clinical variables (age, blood in stool, vomiting, breastfeeding status, and mid-upper arm circumference) as well as location-specific viral diarrhea seasonality curves. The performance of additional models using the ‘present patient’ data combined with other external data sources including location-specific climate, data, recent patient data, and historical population-based prevalence were also evaluated in secondary analysis. Diarrhea etiology was determined with TaqMan Array Card using episode-specific attributable fraction (AFe) >0.5. Results: Of 302 children with acute diarrhea enrolled, 199 had etiologies above the AFe threshold. Viral-only pathogens were detected in 22% of patients in Mali and 63% in Bangladesh. Rotavirus was the most common pathogen detected (16% Mali; 60% Bangladesh). The present patient+ viral seasonality model had an AUC of 0.754 (0.665–0.843) for the sites combined, with calibration-in-the-large α = −0.393 (−0.455––0.331) and calibration slope β = 1.287 (1.207–1.367). By site, the present patient+ recent patient model performed best in Mali with an AUC of 0.783 (0.705–0.86); the present patient+ viral seasonality model performed best in Bangladesh with AUC 0.710 (0.595–0.825). Conclusions: The App accurately identified children with high likelihood of viral-only diarrhea etiology. Further studies to evaluate the App’s potential use in diagnostic and antimicrobial stewardship are underway. Funding: Funding for this study was provided through grants from the Bill and Melinda GatesFoundation (OPP1198876) and the National Institute of Allergy and Infectious Diseases (R01AI135114). Several investigators were also partially supported by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK116163). This investigation was also supported by the University of Utah Population Health Research (PHR) Foundation, with funding in part from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002538. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funders had no role in the study design, data collection, data analysis, interpretation of data, or in the writing or decision to submit the manuscript for publication.

Introduction: Membranous nephropathy (MN) is the leading cause of nephrotic syndrome in adults and is characterized by detectable autoantibodies against glomerular antigens, most commonly phospholipase A2 receptor 1 (PLA2R1) and thrombospondin type-1 domain containing 7A (THSD7A). In Europeans, genetic variation in at least five loci, PLA2R1, HLA-DRB1, HLA-DQA1, IRF4, and NFKB1, affects the risk of disease. Here, we investigated the genetic risk differences between different autoantibody states. Methods: 1,409 MN individuals were genotyped genome-wide with a dense SNV array. The genetic risk score (GRS) was calculated utilizing the previously identified European MN loci, and results were compared with 4,929 healthy controls and 422 individuals with steroid-sensitive nephrotic syndrome. Results: GRS was calculated in the 759 MN individuals in whom antibody status was known. The GRS for MN was elevated in the anti-PLA2R1 antibody-positive (N = 372) compared with both the unaffected control (N = 4,929) and anti-THSD7A-positive (N = 31) groups (p < 0.0001 for both comparisons), suggesting that this GRS reflects anti-PLA2R1 MN. Among PLA2R1-positive patients, GRS was inversely correlated with age of disease onset (p = 0.009). Further, the GRS in the dual antibody-negative group (N = 355) was intermediate between controls and the PLA2R1-positive group (p < 0.0001). Conclusion: We demonstrate that the genetic risk factors for PLA2R1- and THSD7A-antibody-associated MN are different. A higher GRS is associated with younger age of onset of disease. Further, a proportion of antibody-negative MN cases have an elevated GRS similar to PLA2R1-positive disease. This suggests that in some individuals with negative serology the disease is driven by autoimmunity against PLA2R1.

Immune thrombotic thrombocytopenic purpura (iTTP) is an ultra-rare, life-threatening disorder, mediated through severe ADAMTS13 deficiency causing multi-system micro-thrombi formation, and has specific human leukocyte antigen associations. We undertook a large genome-wide association study to investigate additional genetically distinct associations in iTTP. We compared two iTTP patient cohorts with controls, following standardized genome-wide quality control procedures for single-nucleotide polymorphisms and imputed HLA types. Associations were functionally investigated using expression quantitative trait loci (eQTL), and motif binding prediction software. Independent associations consistent with previous findings in iTTP were detected at the HLA locus and in addition a novel association was detected on chromosome 3 (rs9884090, P=5.22x10-10, odds ratio 0.40) in the UK discovery cohort. Meta-analysis, including the French replication cohort, strengthened the associations. The haploblock containing rs9884090 is associated with reduced protein O-glycosyltransferase 1 (POGLUT1) expression (eQTL P

Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation. Our method enables electronic real-time updating and flexibility, such that components can be included or excluded according to data availability. We apply this method to the prediction of etiology of pediatric diarrhea, where 'pre-test’ epidemiologic data may be highly informative. Diarrhea has a high burden in low-resource settings, and antibiotics are often over-prescribed. We demonstrate that our integrative method outperforms traditional prediction in accurately identifying cases with a viral etiology, and show that its clinical application, especially when used with an additional diagnostic test, could result in a 61% reduction in inappropriately prescribed antibiotics.

The Ebola virus disease (EVD) epidemic killed almost 12,000 people across Sierra Leone, Liberia and Guinea, causing significant psychological distress and suffering. This paper describes International Medical Corps’ innovative and comprehensive model for integrating mental health and psychosocial support (MHPSS) considerations and activities into Ebola treatment units (ETUs) across Sierra Leone and Liberia. This includes staff capacity building as well as psychosocial considerations and activities to address needs and challenges at the ETUs. This model was aimed at reducing patient and family distress and promoting healthy behaviours and recovery. We also include data describing mental health-related symptoms reported by our ETU patients, as well as psychosocial support interventions utilised. We discuss recommendations and lessons learnt and conclude that in line with global guidelines, MHPSS considerations and activities should be integral to all aspects of EVD care. Key implications for practice Patients admitted to ETUs face several stressors and challenges related to the ETU environment and procedures, separation from families and effects of EVD Mental health and psychosocial support considerations should be an integral part of all aspects of care provided at the ETU Paraprofessional psychosocial support workers can play a key role in meeting patient needs at the ETU in line with global mental health and psychosocial support guidelines.

Objective Critical care capabilities needed for the management of septic patients, such as continuous vital sign monitoring, are largely unavailable in most emergency departments (EDs) in low- and middle-income country (LMIC) settings. This study aimed to assess the feasibility and accuracy of using a wireless wearable biosensor device for continuous vital sign monitoring in ED patients with suspected sepsis in an LMIC setting. Methods This was a prospective observational study of pediatric (≥2 mon) and adult patients with suspected sepsis at the Kigali University Teaching Hospital ED. Heart rate, respiratory rate and temperature measurements were continuously recorded using a wearable biosensor device for the duration of the patients’ ED course and compared to intermittent manually collected vital signs. Results A total of 42 patients had sufficient data for analysis. Mean duration of monitoring was 32.8 h per patient. Biosensor measurements were strongly correlated with manual measurements for heart rate (r = 0.87, p < 0.001) and respiratory rate (r = 0.75, p < 0.001), although were less strong for temperature (r = 0.61, p < 0.001). Mean (SD) differences between biosensor and manual measurements were 1.2 (11.4) beats/min, 2.5 (5.5) breaths/min and 1.4 (1.0)°C. Technical or practical feasibility issues occurred in 12 patients (28.6%) although were minor and included biosensor detachment, connectivity problems, removal for a radiologic study or exam, and patient/parent desire to remove the device. Conclusions Wearable biosensor devices can be feasibly implemented and provide accurate continuous heart rate and respiratory rate monitoring in acutely ill pediatric and adult ED patients with sepsis in an LMIC setting.

This article introduces seqfam, a python package which is primarily designed for analysing next generation sequencing (NGS) DNA data from families with known pedigree information in order to identify rare variants that are potentially causal of a disease/trait of interest. It uses the popular and versatile Pandas library, and can be straightforwardly integrated into existing analysis code/pipelines. Seqfam can be used to verify pedigree information, to perform Monte Carlo gene dropping, to undertake regression-based gene burden testing, and to identify variants which segregate by affection status in families via user-defined pattern of occurrence rules. Additionally, it can generate scripts for running analyses in a “MapReduce pattern” on a computer cluster, something which is usually desirable in NGS data analysis and indeed “big data” analysis in general. This article summarises how seqfam’s main user functions work and motivates their use. It also provides explanatory context for example scripts and data included in the package which demonstrate use cases. With respect to verifying pedigree information, software exists for efficiently calculating kinship coefficients, so seqfam performs the necessary extra steps of mapping pedigrees and kinship coefficients to expected and observed degrees of relationship respectively. Gene dropping and the application of variant pattern of occurrence rules in families can provide evidence for a variant being causal. The authors are unaware of other software which performs these tasks in familial cohorts, so seqfam fulfils this need. Gene burden rather than single marker tests are often used to detect rare causal variants due to greater power. Seqfam may be an attractive alternative to existing gene burden testing software due to its flexibility, particularly in grouping and aggregating variants.

We construct infinitely many compact, smooth 4-manifolds which are homotopy equivalent to $S^{2}$ but do not admit a spine (that is, a piecewise linear embedding of $S^{2}$ that realizes the homotopy equivalence). This is the remaining case in the existence problem for codimension-2 spines in simply connected manifolds. The obstruction comes from the Heegaard Floer $d$ invariants.

Rwanda, known as the “Land of a Thousand Hills,” is a small, East African country that was the site of the devastating 1994 genocide. In the past 18 years, this post-conflict country has made tremendous progress in rebuilding itself and its health infrastructure. The country has recovered or surpassed many of its pre-1994 health levels, including reduction in HIV/AIDS prevalence, under-five mortality and road traffic accidents. Nevertheless, Rwanda continues to face a high burden of disease. The leading causes of mortality in Rwanda include complications of HIV/AIDS and related opportunistic infections, severe malaria, pulmonary infections, and trauma, and are best managed with emergency and acute care services. However, health care personal resources remain significantly lacking, and there is currently no emergency medicine-trained workforce. The Rwandan government, partnering with international organizations, has launched a campaign to improve human resources for health, and as a part of that effort the creation of training programs in emergency medicine is now underway. The Rwandan Human Resources for Health program can serve as a guide to the development of similar programs within other African countries. The emergency medicine component of this program includes two tracks: a 2-year postgraduate diploma course, followed by a 3-year Masters of Medicine in Emergency Medicine. The program is slated to graduate its first cohort of trained Emergency Physicians in 2017.

Background: Dehydration due to diarrhoea is a leading cause of child death worldwide, yet no clinical tools for assessing dehydration have been validated in resource-limited settings. The Dehydration: Assessing Kids Accurately (DHAKA) score was derived for assessing dehydration in children with diarrhoea in a low-income country setting. In this study, we aimed to externally validate the DHAKA score in a new population of children and compare its accuracy and reliability to the current Integrated Management of Childhood Illness (IMCI) algorithm. Methods: DHAKA was a prospective cohort study done in children younger than 60 months presenting to the International Centre for Diarrhoeal Disease Research, Bangladesh, with acute diarrhoea (defined by WHO as three or more loose stools per day for less than 14 days). Local nurses assessed children and classified their dehydration status using both the DHAKA score and the IMCI algorithm. Serial weights were obtained and dehydration status was established by percentage weight change with rehydration. We did regression analyses to validate the DHAKA score and compared the accuracy and reliability of the DHAKA score and IMCI algorithm with receiver operator characteristic (ROC) curves and the weighted κ statistic. This study was registered with ClinicalTrials.gov, number NCT02007733. Findings: Between March 22, 2015, and May 15, 2015, 496 patients were included in our primary analyses. On the basis of our criterion standard, 242 (49%) of 496 children had no dehydration, 184 (37%) of 496 had some dehydration, and 70 (14%) of 496 had severe dehydration. In multivariable regression analyses, each 1-point increase in the DHAKA score predicted an increase of 0·6% in the percentage dehydration of the child and increased the odds of both some and severe dehydration by a factor of 1·4. Both the accuracy and reliability of the DHAKA score were significantly greater than those of the IMCI algorithm. Interpretation: The DHAKA score is the first clinical tool for assessing dehydration in children with acute diarrhoea to be externally validated in a low-income country. Further validation studies in a diverse range of settings and paediatric populations are warranted. Funding: National Institutes of Health Fogarty International Center.

We exhibit a knot $P$ in the solid torus, representing a generator of first homology, such that for any knot $K$ in the 3-sphere, the satellite knot with pattern $P$ and companion $K$ is not smoothly slice in any homology 4-ball. As a consequence, we obtain a knot in a homology 3-sphere that does not bound a piecewise-linear disk in any homology 4-ball.