PubMed COVID-19 Clinical Care

CONCLUSIONS: Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each healthcare setting: hospital, primary care, and nursing home settings. Insights into factors

CONCLUSIONS: The implementation rate of NPPV decreased significantly in eligible patients, with a decreasing trend observed in patient populations in COVID-19 receiving facilities and emergency declaration response areas. Tracheal intubation

CONCLUSIONS: Pulmonary rehabilitation improves exercise capacity and reduces fatigue in patients with post-COVID-19 syndrome. Duration of PR (4-8 weeks vs > 8 weeks) or PR modality (telerehabilitation vs face-to-face) did not impact on outcomes but

The COVID-19 pandemic created an unprecedented demand for PPE, with single-use face masks emerging as a critical tool in containing virus transmission. However, the extensive use and improper disposal of these single-use face masks, predominantly

CONCLUSIONS: Simultaneous profiling of a rapid N-antigen test in plasma and anti-S1 levels could help to early identify COVID-19 patients needing hospitalization.

Since the outbreak of the pandemic, various anti-SARS-CoV-2 drugs have been developed. In particular, 3CL protease (3C-like protease, 3CLpro) is an attractive drug target because it is an essential enzyme for viral multiplication and is present only

Nonpharmaceutical interventions (NPI) are an important tool for countering pandemics such as COVID-19. Some are cheap; others disrupt economic, educational, and social activity. The latter force governments to balance the health benefits of reduced

BACKGROUND: Currently available injectable drugs that target proprotein convertase subtilisin/kexin type 9 (PCSK9) reduce serum LDL cholesterol and improve cardiovascular outcomes. This phase 2 study assessed NNC0385-0434, an oral PCSK9 inhibitor, in

BACKGROUND: Viral respiratory tract infections are frequently complicated by secondary bacterial infections. This study aimed to use machine learning to predict the risk of bacterial superinfection in SARS-CoV-2-positive individuals.

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