PubMed COVID-19 Clinical Care

No abstract

Since the outbreak of the 2019 pandemic coronavirus disease (COVID-19), great attention has been given to identifying the main clinical features of the disease. Identification of laboratory parameters able to classify patients based on their risk is

Altered circulating hormone and metabolite levels have been reported during and post-COVID-19. Yet, studies of gene expression at the tissue level capable of identifying the causes of endocrine dysfunctions are lacking. Transcript levels of endocrine

CONCLUSIONS: This study will allow us to know the kinetics of anti-RBD SARS-CoV-2 IgG antibodies and data regarding long COVID up to 6 months following infection in the Indonesian pediatric population. This study furthermore has the potential to be a

Healthcare workers experience moral injury (MI), a violation of their moral code due to circumstances beyond their control. MI threatens the healthcare workforce in all settings and leads to medical errors, depression/anxiety, and personal and

This article examines three spatiotemporal methods used for analyzing of infectious diseases, with a focus on COVID-19 in the United States. The methods considered include inverse distance weighting (IDW) interpolation, retrospective spatiotemporal

CONCLUSION: More than half (59.3%) of the healthcare workers in our study reported post-COVID-19 syndrome. Further studies are needed to better understand the frequency and severity of the syndrome among different population groups.

Introduction Neurological manifestations are frequent after acquiring COVID-19 and may persist long-term as part of post-COVID-19 syndrome. Cognitive impairment, chronic fatigue, sleep disturbances, and headache complaints are the most reported

CONCLUSIONS: Pre-existing risk may interact with subsequent perceptions of adversity to impact well-being. In combination with existing research, this small study suggests prevention programs should focus upon managing concurrent mental health and

In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19