PubMed COVID-19 Clinical Care

As a highly contagious disease, COVID-19 has not only had a great impact on the life, study and work of hundreds of millions of people around the world, but also had a huge impact on the global health care system. Therefore, any technical tool that

SARS-CoV-2, the etiological agent behind the coronavirus disease 2019 (COVID-19) pandemic, has continued to mutate and create new variants with increased resistance against the WHO-approved spike-based vaccines. With a significant portion of the

CONCLUSIONS: Understanding the general public's risk perception toward the COVID-19 infection is essential for determining effective protective measures and can be used to guide proper preventive behaviour.

CONCLUSIONS: Our data suggest that autoreactive type II PAA-specific T cells have a protective role in SARS-CoV-2 infections and the presence of high frequencies of these autoreactive T cells indicates effective viral control in COVID-19 patients

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BACKGROUND: Smell loss is a common problem with an estimated 5% of the population having no functioning sense of smell. Viral causes of smell loss are the second most common cause and the coronavirus (COVID-19) pandemic is estimated to have caused 20

CONCLUSION: These findings present a novel bsAb dual-targeting strategy, directed at SARS-CoV-2 RBD and FP, as an effective approach for rapid development and management against continuously evolving SARS-CoV-2 variants.

In atomic force microscopy (AFM), the probe is a nanometric tip located at the end of a microcantilever which palpates the specimen under study as a blind person manages a walking stick. In this way, AFM allows obtaining nanometric resolution images

CONCLUSIONS: This study provides a comprehensive analysis of the cardiovascular impact of the COVID-19 pandemic on individuals with PA. The findings underscore the increased risk of mortality, critical care, MACE, and MAKE among patients with PA and

Wastewater-based epidemiology has emerged as a viable tool for monitoring disease prevalence in a population. This paper details a time series machine learning (TSML) method for predicting COVID-19 cases from wastewater and environmental variables