PubMed COVID-19 Clinical Care

CONCLUSION: This study could aid in crafting pertinent strategies to mitigate the adverse effects of depression on learning amid unexpected public health crises and foster better mental health among college students.

INTRODUCTION: Few artificial intelligence models exist to predict severe forms of COVID-19. Most rely on post-infection laboratory data, hindering early treatment for high-risk individuals.

CONCLUSION: The developed and validated tool would be valuable for assessing the public's perceptions of the drive-thru community pharmacy service during COVID-19 and future pandemics.

No abstract

The coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as a major public health crisis, posing a significant threat to human well-being. Despite the availability of

CONCLUSIONS: People with SCI have experienced living difficulties during the COVID-19 pandemic. Of particular importance, no routine healthcare access and more health conditions are related to more difficulties obtaining both daily necessities and

The advent of single-cell RNA sequencing (scRNA-seq) technologies has enabled gene expression profiling at the single-cell resolution, thereby enabling the quantification and comparison of transcriptional variability among individual cells. Although

Remdesivir 1 is an phosphoramidate prodrug that releases the monophosphate of nucleoside GS-441524 (2) into lung cells, thereby forming the bioactive triphosphate 2-NTP. 2-NTP, an analog of ATP, inhibits the SARS-CoV-2 RNA-dependent RNA polymerase

CONCLUSIONS: Our AI model demonstrated high diagnostic performance for migraine. If secondary headaches can be ruled out, the model can be a powerful tool for diagnosing migraine; however, further data collection and external validation are required

CONCLUSIONS: The findings revealed potential target groups (eg, female and young patients with mild symptoms) for future postpandemic telemental health interventions. However, they also identified patient groups that were harder to reach (eg, older