Entropy calculated from alternative RNA splicing and transcription start/end predicted mortality in these patients.
Current upper respiratory tract testing for COVID-19 only determines if the virus is present. Deep RNA sequencing with appropriate computational biology may provide important prognostic information and point to therapeutic foci to be precisely targeted in future studies.
Deep RNA sequencing provides a novel diagnostic tool for critically ill patients. Among ICU patients with COVID-19, RNA sequencings can identify gene expression, pathogens (including SARS-CoV-2), and can predict mortality.
Deep RNA sequencing is a novel technology that can assist in the care of critically ill COVID-19 patients & can be applied to other disease.
Deep RNA sequencing is a novel technology that can assist in the care of critically ill COVID-19 patients & can be applied to other disease.Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3,883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3,125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs. COVID-19-positive model had an AUC of 98%, and 92% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https//github.com/ynaveena/COVID-19-vs-Influenza and may be have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.
The SARS-CoV-2 virus has infected millions of people, overwhelming critical care resources in some regions. Many plans for rationing critical care resources during crises are based on the Sequential Organ Failure Assessment (SOFA) score. The COVID-19 pandemic created an emergent need to develop and validate a novel electronic health record (EHR)-computable tool to predict mortality.
To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA.
We conducted a prospective cohort study of a regional health system with 12 hospitals in Colorado between March 2020 and July 2020. All patients >14 years old hospitalized during the study period without a do not resuscitate order were included. Patients were stratified by the diagnosis of COVID-19. From this cohort, we developed and validated a model using stacked generalization to predict mortality using data widely available in the EHR by combining five previously validated scores and additio and SOFA scores predicted all-cause mortality with AUROCs of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94.Interpretation A novel EHR-based mortality score can be rapidly implemented to better predict patient outcomes during an evolving pandemic.Pandemic SARS-CoV-2 causes a mild to severe respiratory disease called Coronavirus Disease 2019 (COVID-19). Control of SARS-CoV-2 spread will depend on vaccine-induced or naturally acquired protective herd immunity. Until then, antiviral strategies are needed to manage COVID-19, but approved antiviral treatments, such as remdesivir, can only be delivered intravenously. Enisamium (laboratory code FAV00A, trade name Amizon®) is an orally active inhibitor of influenza A and B viruses in cell culture and clinically approved in countries of the Commonwealth of Independent States. Here we show that enisamium can inhibit SARS-CoV-2 infections in NHBE and Caco-2 cells. In vitro , the previously identified enisamium metabolite VR17-04 directly inhibits the activity of the SARS-CoV-2 RNA polymerase. Docking and molecular dynamics simulations suggest that VR17-04 prevents GTP and UTP incorporation. To confirm enisamium's antiviral properties, we conducted a double-blind, randomized, placebo-controlled trial in adult, hon prevent SARS-CoV-2 replication and that its metabolite VR17-04 can inhibit the SARS-CoV-2 RNA polymerase
. Moreover, we find that COVID-19 patients requiring supplementary oxygen, recover more quickly than patients treated with a placebo. Enisamium may therefore be an accessible treatment for COVID-19 patients.
SARS-CoV-2 is the causative agent of COVID-19. selleck Although vaccines are now becoming available to prevent SARS-CoV-2 spread, the development of antivirals remains necessary for treating current COVID-19 patients and combating future coronavirus outbreaks. Here, we report that enisamium, which can be administered orally, can prevent SARS-CoV-2 replication and that its metabolite VR17-04 can inhibit the SARS-CoV-2 RNA polymerase in vitro . Moreover, we find that COVID-19 patients requiring supplementary oxygen, recover more quickly than patients treated with a placebo. Enisamium may therefore be an accessible treatment for COVID-19 patients.As three SARS-CoV-2 vaccines come to market in Europe and North America in the winter of 2020-2021, distribution networks will be in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation is critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs require that distribution is prioritized to the elderly, health-care workers, teachers, essential workers, and individuals with co-morbidities putting them at risk of severe clinical progression. Here, we evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not be included in the first round of vaccination. And, we account for current age-specific immune patterns in both states. We find that allocating a substantial proportion ( > 75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021.selleck
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