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Background. In low-income countries, pediatric emergency care is largely underdeveloped although child mortality in emergency care is more than twice that of adults, and mortality after discharge is high. Aim. We aimed at describing characteristics, triage categories, and post-discharge mortality in a pediatric emergency population in Nepal. Methods. We prospectively assessed characteristics and triage categories of pediatric patients who entered the emergency department (ED) in a local hospital. Patient households were followed-up by telephone interviews at 90 days. Results. The majority of pediatric emergency patients presented with injuries and infections (~40% each). Girls attended ED less frequent than boys. selleck inhibitor High triage priority categories (orange and red) were strong indicators for intensive care need and for mortality after discharge. Conclusion. The study supports the use and development of a pediatric triage systems in a low-resource general ED setting. We identify a need for interventions that can reduce mortality after pediatric emergency care. Interventions to reduce pediatric emergency disease burden in this setting should emphasize prevention and effective treatment of infections and injuries.Elderly patients undergoing hip fracture surgery represent a myriad of perioperative challenges and risks. The arrival of the global pandemic of novel coronavirus disease 2019 (COVID-19) adds an unprecedented challenge to the management of hip fracture patients. We describe the unique experience and favorable outcome of a 100-year-old COVID-positive hip fracture patient that underwent spinal anesthesia for hemiarthroplasty and subsequent hydroxychloroquine (HCQ) therapy. Multiple factors of varying known benefit may have contributed to our outcome, including preoperative medical consultation and assessment, early surgical intervention, regional anesthesia with little to no sedation, early mobilization and HCQ therapy.Exercise is touted as the ideal prescription to treat and prevent many chronic diseases. We examined changes in utilization and cost of medication classes commonly prescribed in the management of chronic conditions following participation in 12-months of supervised exercise within the Veterans Affairs Gerofit program. Gerofit enrolled 480 veterans between 1999 and 2017 with 12-months participation, with 453 having one or more active prescriptions on enrollment. Active prescriptions overall and for five classes of medications were examined. Changes from enrollment to 12 months were calculated, and cost associated with prescriptions filled were used to estimate net cost changes. Active prescriptions were reduced for opioids (77 of 164, 47%), mental health (93 of 221, 42%), cardiac (175 of 391, 45%), diabetes (41 of 166, 25%), and lipid lowering (56 of 253, 22%) agents. Cost estimates resulted in a net savings of $38,400. These findings support the role of supervised exercise as a favorable therapeutic intervention that has impact across chronic conditions.Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment.COVID-19 pandemic represents an unprecedented global health crisis in the last 100 years. Its economic, social and health impact continues to grow and is likely to end up as one of the worst global disasters since the 1918 pandemic and the World Wars. Mathematical models have played an important role in the ongoing crisis; they have been used to inform public policies and have been instrumental in many of the social distancing measures that were instituted worldwide. In this article we review some of the important mathematical models used to support the ongoing planning and response efforts. These models differ in their use, their mathematical form and their scope.Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems. However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest is often very low relative to the available sample size. Though very prevalent in healthcare, such imbalanced classification settings are also common and challenging in many other scenarios. So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems. Specifically, we leverage the imposed extreme-distribution behavior on a latent space to extract information from low-prevalence events, and develop a robust prediction arm that joins the merits of the generalized additive model and isotonic neural nets. selleck inhibitor Results on synthetic studies and diverse real-world datasets, including mortality prediction on a COVID-19 cohort, demonstrate that the proposed approach outperforms existing alternatives.selleck inhibitor