PubMed COVID-19 Clinical Care

CONCLUSION: Previous studies reported that mental health deteriorated and suicide risk increased significantly during the second wave of COVID-19 in Japan. These trends are consistent with the present findings, suggesting increased use of the crisis

The purpose of this study is to develop a text clustering-based analysis of COVID-19 research articles. Owing to the proliferation of published COVID-19 research articles, researchers need a method for reducing the number of articles they have to

Accurate and rapid population-scale screening techniques based on SARS-CoV-2 RNA are essential in preventing and controlling the COVID-19 epidemic. However, the sensitivity and specificity of the assay signal are challenged by the problems of target

CONCLUSIONS: RTs are involved in stressful or traumatic clinical events, resulting in psychological/physical distress and turnover intentions. The COVID-19 pandemic has had a significant impact on RTs' SVEs, highlighting the importance of addressing

Background The onset of the coronavirus pandemic (COVID-19/SARS-CoV-2) saw an overall decline in traffic. Fundamental shifts in the pattern of traffic-related traumas were observed across the United States and beyond. Objectives This study aims to

CONCLUSIONS: Rapid antigen tests are beneficial as they can support a decision to release individuals from isolation when they have recovered and maintain isolation for people who may still be infectious. Future isolation policies should be guided by

No abstract

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we

CONCLUSION: Self-efficacy, diabetes beliefs, and depressive symptoms were key psychosocial factors that affected medication adherence among Blacks/African Americans. Contrasting perceptions of beliefs in medicines, social support, provider

This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using