PubMed COVID-19 Clinical Care

CONCLUSION: COVID-19 is here to stay. Hepatic dysfunction in COVID-19 signals severe COVID-19 infections. Patients with chronic liver disease have higher mortality from COVID-19 than general population. It is important to remember the lessons learned

CONCLUSIONS: Occupational-related factors affected the health and well-being of working migrants during the pandemic. The pressure to get vaccinated and increased structural discrimination in the workplace need to be addressed by Norwegian

Purpose: This study aimed to identify the correlation between occupational stress and professional quality of life (Pro-QOL) among community nurses during the pandemic COVID-19. Design: This study was conducted with a correlational design of 120

Examining information behaviors is often justified by their consequences but theoretical models of information behaviors have focused on antecedents of seeking and avoidance rather than their outcomes. Therefore, the current study aims to explore the

CONCLUSIONS: Prior investments in aligning systems provided unanticipated benefits for organizational and community resilience during the COVID-19 pandemic. Our results illustrate a pathway for investment in system alignment efforts that build

CONCLUSIONS: Based on current trends, the presence of ophthalmology residency programs on social media will likely continue expanding, with major social platforms becoming a vaster source of information for ophthalmology residency applicants.

CONCLUSIONS: This qualitative study can help inform hospital management and public policy on how to better support and meet the needs of nurses working in obstetrical care during pandemics. Moreover, applying the JD-R model offers both a novel and

CONCLUSIONS: The new Omicron variant BA.2.86 has affected 264 people in 23 countries. The disease is more common in females than males and mainly affects old age people (over 60 years of age). However, no deaths have been reported. The variant is

High-throughput technologies and machine learning (ML), when applied to a huge pool of medical data such as omics data, result in efficient analysis. Recent research aims to apply and develop ML models to predict a disease well in time using

No abstract