Remdesivir use in patients with coronavirus COVID-19 disease: a systematic review and meta-analysis

BackgroundCoronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, has led to significant global mortality and morbidity. Until now, no treatment has proven to be effective in COVID-19. To explore whether the use of remdesivir, initially an experimental broad-spectrum antiviral, is effective in the treatment of hospitalized patients with COVID-19, we conducted a systematic review and meta-analysis of randomized, placebo-controlled trials investigating its use. MethodsA rapid search of the MEDLINE and EMBASE medical databases was conducted for randomized controlled trials. A systematic approach was used to screen, abstract, and critically appraise the studies. Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method was applied to rate the certainty and quality of the evidence reported per study. ResultsTwo RCTs studies were identified (n=1,299). A fixed-effects meta-analysis revealed reductions in mortality (RR=0.69, 0.49 to 0.99), time to clinical improvement (3.95 less days, from 3.86 days less to 4.05 less days), serious adverse events (RR=0.77, 0.63 to 0.94) and all adverse events (RR=0.87, 0.79 to 0.96). ConclusionIn this rapid systematic review, we present pooled evidence from the 2 included RCT studies that reveal that remdesivir has a modest yet significant reduction in mortality and significantly improves the time to recovery, as well as significantly reduced risk in adverse events and serious adverse events. It is more than likely that as an antiviral, remdesivir is not sufficient on its own and may be suitable in combination with other antivirals or treatments such as convalescent plasma. Research is ongoing to clarify and contextual these promising findings.

A Novel Field-Deployable Method for Sequencing and Analyses of Henipavirus Genomes From Complex Samples on the MinION Platform.

Viruses in the genus Henipavirus encompass 2 highly pathogenic emerging zoonotic pathogens, Hendra virus (HeV) and Nipah virus (NiV). Despite the impact on human health, there is currently limited full-genome sequence information available for henipaviruses. This lack of full-length genomes hampers our ability to understand the molecular drivers of henipavirus emergence. Furthermore, rapidly deployable viral genome sequencing can be an integral part of outbreak response and epidemiological investigations to study transmission chains. In this study, we describe the development of a reverse-transcription, long-range polymerase chain reaction (LRPCR) assay for efficient genome amplification of NiV, HeV, and a related non-pathogenic henipavirus, Cedar virus (CedPV). We then demonstrated the utility of our method by amplifying partial viral genomes from 6 HeV-infected tissue samples from Syrian hamsters and 4 tissue samples from a NiV-infected African green monkey with viral loads as low as 52 genome copies/mg. We subsequently sequenced the amplified genomes on the portable Oxford Nanopore MinION platform and analyzed the data using a newly developed field-deployable bioinformatic pipeline. Our LRPCR assay allows amplification and sequencing of 2 or 4 amplicons in semi-nested reactions. Coupled with an easy-to-use bioinformatics pipeline, this method is particularly useful in the field during outbreaks in resource-poor environments.

Detecting drug-resistant tuberculosis in chest radiographs.

PURPOSE: Tuberculosis is a major global health threat claiming millions of lives each year. While the total number of tuberculosis cases has been decreasing over the last years, the rise of drug-resistant tuberculosis has reduced the chance of controlling the disease. The purpose is to implement a timely diagnosis of drug-resistant tuberculosis, which is essential to administering adequate treatment regimens and stopping the further transmission of drug-resistant tuberculosis. METHODS: A main tool for diagnosing tuberculosis is the conventional chest X-ray. We are investigating the possibility of discriminating automatically between drug-resistant and drug-sensitive tuberculosis in chest X-rays by means of image analysis and machine learning methods. RESULTS: For discriminating between drug-sensitive and drug-resistant tuberculosis, we achieve an area under the receiver operating characteristic curve (AUC) of up to 66%, using an artificial neural network in combination with a set of shape and texture features. We did not observe any significant difference in the results when including follow-up X-rays for each patient. CONCLUSION: Our results suggest that a chest X-ray contains information about the likelihood of a drug-resistant tuberculosis infection, which can be exploited computationally. We therefore suggest to repeat the experiments of our pilot study on a larger set of chest X-rays.

Nephele: a cloud platform for simplified, standardized and reproducible microbiome data analysis.

Motivation: Widespread interest in the study of the microbiome has resulted in data proliferation and the development of powerful computational tools. However, many scientific researchers lack the time, training, or infrastructure to work with large datasets or to install and use command line tools. Results: The National Institute of Allergy and Infectious Diseases (NIAID) has created Nephele, a cloud-based microbiome data analysis platform with standardized pipelines and a simple web interface for transforming raw data into biological insights. Nephele integrates common microbiome analysis tools as well as valuable reference datasets like the healthy human subjects cohort of the Human Microbiome Project (HMP). Nephele is built on the Amazon Web Services cloud, which provides centralized and automated storage and compute capacity, thereby reducing the burden on researchers and their institutions. Availability and implementation: https://nephele.niaid.nih.gov and https://github.com/niaid/Nephele. Contact: darrell.hurt@nih.gov.