PubMed COVID-19 Clinical Care

CONCLUSIONS: Natural coronavirus infections were common and resulted in strong homotypic immunity but not heterotypic immunity against other coronaviruses, including SARS-CoV-2. Endemic coronavirus and SARS-CoV-2 infections in this US cohort were

Expanding digital data sources, including social media and online news, provide a low-cost way to examine human-nature interactions, such as wildlife exploitation. However, the extent to which using such data sources can expand or bias understanding

No abstract

CONCLUSIONS: Effects of risk perception on depressive symptoms varied by periods of COVID-19. These findings have important implications for developing effective prevention and early psychoeducational intervention strategies for pregnant women with a

During the Covid-19 crisis, citizens turned to Twitter for information seeking, emotional outlet and sense-making of the crisis, creating ad hoc social communities using crisis-specific hashtags. The theory of ambient affiliation posits that the use

[Purpose] The purpose of this study is to determine how the study environment during remote classes affected the physical health of university students during the coronavirus disease 2019 pandemic. [Participants and Methods] A total of 3,359 students

CONCLUSIONS: Vaccine research is rapidly evolving, emerging technologies and new immunization models offer public health new tools and large potential to fight vaccines preventables diseases, with promising new platforms and broadened target

Monoclonal antibodies, as tixagevimab/cilgavimab, have been introduced as prophylaxis against COVID-19 infections in high-risk populations. However, data on efficacy are limited. This study investigates efficacy and tolerability of tixagevimab

CONCLUSIONS: Maintaining the mental health of frontline health workers is crucial during the COVID-19 pandemic, and mindfulness-based therapy is the most extensively employed psychological intervention method. However, its effectiveness requires

Various segmentation networks based on Swin Transformer have shown promise in medical segmentation tasks. Nonetheless, challenges such as lower accuracy and slower training convergence have persisted. To tackle these issues, we introduce a novel