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Effective therapies for coronavirus disease 2019 (COVID-19) are urgently needed, and preclinical data suggest alpha-1 adrenergic receptor antagonists (α 1 -AR antagonists) may be effective in reducing mortality related to hyperinflammation. Using a retrospective cohort design with patients in the Department of Veterans Affairs healthcare system, we use doubly robust regression and matching to estimate the association between use of α 1 -AR antagonists at time of hospitalization and likelihood of death due to COVID-19 during an inpatient stay. Having an active prescription for an α 1 -AR antagonist (tamsulosin, silodosin, prazosin, terazosin, doxazosin, or alfuzosin) at the time of admission had a significant negative association with in-hospital mortality (relative risk reduction 14%; odds ratio 0.75; 95% CI 0.66 to 0.86; p ≤ 0.001). These effects were also found in an expanded cohort of suspected COVID-19 patients, supporting the need for clinical trials.In order to understand preferences about SARS-CoV-2 testing, we conducted a discrete choice experiment among 4793 participants in the Communities, Households, and SARS-CoV-2 Epidemiology (CHASING COVID) Cohort Study from July 30-September 8, 2020. We used latent class analysis to identify distinct patterns of preferences related to testing and conducted a simulation to predict testing uptake if additional testing scenarios were offered. Five distinct patterns of SARS-CoV-2 testing emerged. "Comprehensive testers" (18.9%) ranked specimen type as most important and favored less invasive specimen types, with saliva most preferred, and also ranked venue and result turnaround time as highly important, with preferences for home testing and fast result turnaround time. "Fast track testers" (26.0%) ranked result turnaround time as most important and favored immediate and same day turnaround time. "Dual testers" (18.5%) ranked test type as most important and preferred both antibody and viral tests. "Non-invasive dual SARS-CoV-2 testing, which, despite vaccine availability, will be required for the foreseeable future. We identified substantial differences in preferences for SARS-CoV-2 testing in a discrete choice experiment among a large national cohort of adults in the US. Offering additional testing options that account for or anticipate this heterogeneity in preferences (e.g., both viral and antibody tests, at home testing), would likely increase testing uptake.<br /><br /> Biological Sciences (major); Psychological and Cognitive Sciences (minor).<br /><br />Biological Sciences (major); Psychological and Cognitive Sciences (minor).As the global community strives to discover effective therapies for COVID-19, immunomodulatory strategies have emerged as a leading contender to combat the cytokine storm and improve clinical outcomes in patients with severe disease. Systemic corticosteroids and selective cytokine inhibitory agents have been utilized both as empiric therapies and in clinical trials. While multiple randomized, placebo controlled trials have now demonstrated that corticosteroids improve survival in patients with COVID-19, 1, 2 IL-6 inhibition, which gained significant early interest based on observational studies, has not demonstrated reliable efficacy in randomized, placebo controlled trials. 3, 4 To better understand the mechanistic basis of immunomodulatory therapies being implemented for treatment of COVID-19, we assessed longitudinal biochemical changes in response to such approaches in hospitalized patients with COVID-19. We demonstrate broad suppression of multiple immunomodulatory factors associated with adverse clinical outcomes in COVID-19 in patients who received corticosteroids, but no such response was seen in patients who either received tocilizumab or no immunomodulatory therapy. Our findings provide early insights into molecular signatures that correlate with immunomodulatory therapies in COVID-19 which may be useful in understanding clinical outcomes in future studies of larger patient cohorts.Seasonality of respiratory diseases has been linked, among other factors, to low outdoor absolute humidity and low relative humidity in indoor environments, which increase evaporation of water in the mucosal layer lining the respiratory tract. We demonstrate that normal breathing results in an absorption-desorption cycle inside facemasks, where super-saturated air is absorbed by the mask fibers during expiration, followed by evaporation during inspiration of dry environmental air. For double-layered cotton masks, which have considerable heat capacity, the temperature of inspired air rises above room temperature, and the effective increase in relative humidity can exceed 100%. We propose that the recently reported, disease-attenuating effect of generic facemasks is dominated by the strong humidity increase of inspired air.<br /><br /> Facemasks are the most widely used tool for mitigating the spread of the COVID-19 pandemic. Decreased disease severity by the wearer has also been linked to the use of cloth facemasks. Thishaled air, thereby promoting hydration of the respiratory epithelium which is known to be beneficial to the immune system. Increased humidity of inspired air could be an alternate explanation for the now well-established link between mask wearing and lower disease severity.<br /><br /> The determinants of COVID-19 disease severity and extrapulmonary complications (EPCs) are poorly understood. [https://www.selleckchem.com/products/ly3214996.html LY3214996] We characterise the relationships between SARS-CoV-2 RNAaemia and disease severity, clinical deterioration, and specific EPCs.<br /><br /> We used quantitative (qPCR) and digital (dPCR) PCR to quantify SARS-CoV-2 RNA from nasopharyngeal swabs and plasma in 191 patients presenting to the Emergency Department (ED) with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterised the role of RNAaemia in predicting clinical severity and EPCs using elastic net regression.<br /><br /> 23·0% (44/191) of SARS-CoV-2 positive patients had viral RNA detected in plasma by dPCR, compared to 1·4% (2/147) by qPCR. Most patients with serial measurements had undetectable RNAaemia 10 days after onset of symptoms, but took 16 days to reach maximum severity, and 33 days for symptoms to remmune response.Added value of this study We quantified SARS-CoV-2 RNA in the nasopharynx and plasma of patients presenting to the Emergency Department with COVID-19, and found an array-based dPCR platform to be markedly more sensitive than qPCR for detection of SARS-CoV-2 RNA, with a simplified workflow well-suited to clinical adoption. We collected serial plasma samples during patients' course of illness, and showed that SARS-CoV-2 RNAaemia peaks early, while clinical condition often continues to worsen. Our findings confirm the association between RNAaemia and disease severity, and additionally demonstrate a role for RNAaemia in predicting future deterioration and specific extrapulmonary complications.Implications of all the available evidence Variation in SARS-CoV-2 RNAaemia may help explain disparities in disease severity and extrapulmonary complications from COVID-19. Testing for RNAaemia with dPCR early in the course of illness may help guide patient triage and management.Development of an effective AIDS vaccine remains a challenge. Nucleoside-modified mRNAs formulated in lipid nanoparticles (mRNA-LNP) have proved to be a potent mode of immunization against infectious diseases in preclinical studies, and are being tested for SARS-CoV-2 in humans. A critical question is how mRNA-LNP vaccine immunogenicity compares to that of traditional adjuvanted protein vaccines in primates. Here, we found that mRNA-LNP immunization compared to protein immunization elicited either the same or superior magnitude and breadth of HIV-1 Env-specific polyfunctional antibodies. Immunization with mRNA-LNP encoding Zika premembrane and envelope (prM-E) or HIV-1 Env gp160 induced durable neutralizing antibodies for at least 41 weeks. Doses of mRNA-LNP as low as 5 μg were immunogenic in macaques. Thus, mRNA-LNP can be used to rapidly generate single or multi-component vaccines, such as sequential vaccines needed to protect against HIV-1 infection. Such vaccines would be as or more immunogenic than adjuvanted recombinant protein vaccines in primates.As the mechanistic basis of adaptive cellular antigen recognition, T cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages antigen-enriched repertoires to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly quantify functionally similar TCRs in bulk repertoires. We apply the framework to TCR data from COVID-19 patients, generating 1,915 public TCR meta-clonotypes from the 18 SARS-CoV-2 antigen-enriched repertoires with the strongest evidence of HLA-restriction. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 44% (845/1915) were significantly enriched among COVID-19 patients that expressed the putative restricting HLA allele, demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3 , that implements this framework and facilitates workflows for distance-based TCR repertoire analysis.In our group, we aim to understand metabolism in the nematode Caenorhabditis elegans and its relationships with gene expression, physiology and the response to therapeutic drugs. On March 15, 2020, a stay-at-home order was put into effect in the state of Massachusetts, USA, to flatten the curve of the spread of the novel SARS-CoV2 virus that causes COVID-19. For biomedical researchers in our state, this meant putting a hold on experiments for nine weeks until May 18, 2020. To keep the lab engaged and productive, and to enhance communication and collaboration, we embarked on an in-lab project that we all found important but that we never had the time for the detailed annotation and drawing of C. elegans metabolic pathways. As a result, we present WormPaths, which is composed of two parts 1) the careful manual annotation of metabolic genes into pathways, categories and levels, and 2) 66 pathway maps that include metabolites, metabolite structures, genes, reactions, and pathway connections between maps. These maps are available on our WormFlux website. We show that WormPaths provides easy-to-navigate maps and that the different levels in WormPaths can be used for metabolic pathway enrichment analysis of transcriptomic data. In the unfortunate event of additional lockdowns, we envision further developing these maps to be more interactive, with an analogy of road maps that are available on mobile devices.T-cells play an essential role in the adaptive immune system by seeking out, binding and destroying foreign antigens presented on the cell surface of diseased cells. An improved understanding of T-cell immunity will greatly aid in the development of new cancer immunotherapies and vaccines for life threatening pathogens. Central to the design of such targeted therapies are computational methods to predict non-native epitopes to elicit a T cell response, however, we currently lack accurate immunogenicity inference methods. Another challenge is the ability to accurately simulate immunogenic peptides for specific human leukocyte antigen (HLA) alleles, for both synthetic biological applications and to augment real training datasets. Here, we proposed a beta-binomial distribution approach to derive epitope immunogenic potential from sequence alone. We conducted systematic benchmarking of five traditional machine learning (ElasticNet, KNN, SVM, Random Forest, AdaBoost) and three deep learning models (CNN, ResNet, GNN) using three independent prior validated immunogenic peptide collections (dengue virus, cancer neoantigen and SARS-Cov-2).