PubMed COVID-19 Clinical Care

No abstract

The COVID-19 pandemic, on a global scale, has prompted multifaceted challenges, including a notable psychological toll on the general population. This study uses mixed-method approach for a nuanced exploration of these experiences. Using a

Intravenous remdesivir (RDV) is U.S. Food and Drug Administration-approved for hospitalized and nonhospitalized individuals with coronavirus disease 2019. RDV undergoes intracellular metabolic activation to form the active triphosphate, GS-443902

The aim of this study was to evaluate selected parameters of redox signaling and inflammation in the granulocytes of COVID-19 patients who recovered and those who died. Upon admission, the patients did not differ in terms of any relevant clinical

INTRODUCTION: Over the last years, severe respiratory viral infections, particularly those caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the influenza virus, have emerged as risk factor for viral-associated pulmonary

Lipoxins (LXs) have attracted widespread attention as a class of anti-inflammatory lipid mediators that are produced endogenously by the organism. LXs are arachidonic acid (ARA) derivatives that include four different structures: lipoxin A4 (LXA4)

Psychological resilience (PR) plays an important role in fortifying mental health during pandemics. This study aimed to examine the PR and its related factors of college students in China after the deblocking of the China's COVID-19 pandemic strategy

In 2020, a global pandemic caused by SARS-CoV-2 was declared. Different institutes proposed diagnostic molecular methods to detect the virus in clinical samples. This study aims to validate and standardize the use of a loop-mediated isothermal

There has been a noticeable variance between countries in the growth rate of COVID-19 since the start of the pandemic. Researchers attempted to understand this variance from two primary perspectives: the policies implemented to curb the spread of the

Predicting which patients are at greatest risk of severe disease from COVID-19 has the potential to improve patient outcomes and improve resource allocation. We developed machine learning models for predicting COVID-19 prognosis from a retrospective