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Objectives: Effective and widely available therapies are still needed for outpatients with COVID-19. We aimed to evaluate the efficacy and safety of lopinavir/ritonavir (LPV/r) for early treatment of non-hospitalized individuals diagnosed with COVID-19. Methods: This randomized, placebo (Plb)-controlled, double-blind, multi-site decentralized clinical trial enrolled non-hospitalized adults with confirmed SARS-CoV-2 infection and six or fewer days of acute respiratory infection symptoms who were randomized to either twice-daily oral LPV/r (400 mg/100 mg) or Plb for 14 days. Daily surveys on study days 1 through 16 and again on study day 28 evaluated symptoms, daily activities, and hospitalization status. The primary outcome was longitudinal change in an ordinal scale based on a combination of symptoms, activity, and hospitalization status through day 15 and was analyzed by use of a Bayesian longitudinal proportional odds logistic regression model for estimating the probability of a superior recovery for LPV/r over Plb (odds ratio >1). Results: Between June 2020 and December 2021, 448 participants were randomized to receive either LPV/r (n = 216) or Plb (n = 221). The mean symptom duration before randomization was 4.3 days (SD 1.3). There were no differences between treatment groups through the first 15 days for the ordinal primary outcome (odds ratio 0.96; 95% credible interval: 0.66 to 1.41). There were 3.2% (n = 7) of LPV/r and 2.7% (n = 6) of Plb participants hospitalized by day 28. Serious adverse events did not differ between groups. Conclusion: LPV/r did not significantly improve symptom resolution or reduce hospitalization in non-hospitalized participants with COVID-19. Trial registration: ClinicalTrials.gov Identifier: NCT04372628

Abstract Background Coronavirus disease 2019 (COVID-19) has a heterogeneous outcome in individuals from remaining asymptomatic to death. In a majority of cases, mild symptoms are present that do not require hospitalization and can be successfully treated in the outpatient setting, though symptoms may persist for a long duration. We hypothesize that drugs suitable for decentralized study in outpatients will have efficacy among infected outpatients Methods The TREAT NOW platform is designed to accommodate testing multiple agents with the ability to incorporate new agents in the future. TREAT NOW is an adaptive, blinded, multi-center, placebo-controlled superiority randomized clinical trial which started with two active therapies (hydroxychloroquine and lopinavir/ritonavir) and placebo, with the hydroxychloroquine arm dropped shortly after enrollment began due to external evidence. Each arm has a target enrollment of 300 participants who will be randomly assigned in an equal allocation to receive either an active therapy or placebo twice daily for 14 days with daily electronic surveys collected over days 1 through 16 and on day 29 to evaluate symptoms and a modified COVID-19 ordinal outcome scale. Participants are enrolled remotely by telephone and consented with a digital interface, study drug is overnight mailed to study participants, and data collection occurs electronically without in-person interactions. Discussion If effective treatments for COVID-19 can be identified for individuals in the outpatient setting before they advance to severe disease, it will prevent progression to more severe disease, reduce the need for hospitalization, and shorten the duration of symptoms. The novel decentralized, “no touch” approach used by the TREAT NOW platform has distinction advantages over traditional in-person trials to reach broader populations and perform study procedures in a pragmatic yet rigorous manner. Trial registration ClinicalTrials.gov NCT04372628. Registered on April 30, 2020. First posted on May 4, 2020.

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of “adaptive” clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

Health advances are contingent on continuous development of new methods and approaches to foster data-driven discovery in the biomedical and clinical sciences. Open-science and team-based scientific discovery offer hope for tackling some of the difficult challenges associated with managing, modeling, and interpreting of large, complex, and multisource data. Translating raw observations into useful information and actionable knowledge depends on effective domain-independent reproducibility, area-specific replicability, data curation, analysis protocols, organization, management and sharing of health-related digital objects. This study expands the functionality and utility of an ensemble semi-supervised machine learning technique called Compressive Big Data Analytics (CBDA). Applied to high-dimensional data, CBDA (1) identifies salient features and key biomarkers enabling reliable and reproducible forecasting of binary, multinomial and continuous outcomes (i.e., feature mining); and (2) suggests the most accurate algorithms/models for predictive analytics of the observed data (i.e., model mining). The method relies on iterative subsampling, combines function optimization and statistical inference, and generates ensemble predictions for observed univariate outcomes. The novelty of this study is highlighted by a new and expanded set of CBDA features including (1) efficiently handling extremely large datasets (>100,000 cases and >1,000 features); (2) generalizing the internal and external validation steps; (3) expanding the set of base-learners for joint ensemble prediction; (4) introducing an automated selection of CBDA specifications; and (5) providing mechanisms to assess CBDA convergence, evaluate the prediction accuracy, and measure result consistency. To ground the mathematical model and the corresponding computational algorithm, CBDA 2.0 validation utilizes synthetic datasets as well as a population-wide census-like study. Specifically, an empirical validation of the CBDA technique is based on a translational health research using a large-scale clinical study (UK Biobank), which includes imaging, cognitive, and clinical assessment data. The UK Biobank archive presents several difficult challenges related to the aggregation, harmonization, modeling, and interrogation of the information. These problems are related to the complex longitudinal structure, variable heterogeneity, feature multicollinearity, incongruency, and missingness, as well as violations of classical parametric assumptions. Our results show the scalability, efficiency, and usability of CBDA to interrogate complex data into structural information leading to derived knowledge and translational action. Applying CBDA 2.0 to the UK Biobank case-study allows predicting various outcomes of interest, e.g., mood disorders and irritability, and suggests new and exciting avenues of evidence-based research in the context of identifying, tracking, and treating mental health and aging-related diseases. Following open-science principles, we share the entire end-to-end protocol, source-code, and results. This facilitates independent validation, result reproducibility, and team-based collaborative discovery.

Culture supernatant of sepsis-associated Escherichia coli (SEPEC) isolated from patients with sepsis caused loss of intercellular junctions and elongation of human umbilical vein endothelial cells (HUVEC). The cytotoxic factor was purified from culture supernatant of SEPEC 15 (serogroup O153) by liquid chromatography process. PAGE (polyacrylamide gel electrophoresis) showed that the purified SEPEC cytotoxic factor had a molecular mass of ∼150 kDa and consisted of at least two subunits. At the concentration of 1 CD50 (40 μg/mL) did facilitate transcytosis through the HUVEC cells monolayer of SEPEC 15 as much as E. coli K12 within 30 min without affecting cell viability. These results suggest that this cytotoxic factor, named as SPF (SEPEC's permeabilizing factor), may be an important SEPEC virulence factor that facilitates bacterial access to the bloodstream. Keywords: Cytotoxin (SPF), Sepsis, Transcytosis, Transendothelial electrical resistance

Group A human rotaviruses (HuRVA) are causative agents of acute gastroenteritis. Six viral structural proteins (VPs) and six nonstructural proteins (NSPs) are produced in RV-infected cells. NSP4 is a diarrhoea-inducing viral enterotoxin and NSP4 gene analysis revealed at least 15 (E1-E15) genotypes. This study analysed the NSP4 genetic diversity of HuRVA G2P[4] strains collected in the state of São Paulo (SP) from 1994 and 2006-2010 using reverse transcription-polymerase chain reaction, sequencing and phylogenetic analysis. Forty (97.6%) G2P[4] strains displayed genotype E2; one strain (2.4%) displayed genotype E1. These results are consistent with the proposed linkage between VP4/VP7 (G2P[4]) and the NSP4 (E2) genotype of HuRVA. NSP4 phylogenetic analysis showed distinct clusters, with grouping of most strains by their genotype and collection year, and most strains from SP were clustered together with strains from other Brazilian states. A deduced amino acid sequence alignment for E2 showed many variations in the C-terminal region, including the VP4-binding domain. Considering the ability of NSP4 to generate host immunity, monitoring NSP4 variations, along with those in the VP4 or VP7 protein, is important for evaluating the circulation and pathogenesis of RV. Finally, the presence of one G2P[4]E1 strain reinforces the idea that new genotype combinations emerge through reassortment and independent segregation.