PubMed COVID-19 Clinical Care

The appropriate immunosurveillance tools are foundational for the creation of therapeutics, vaccines, and containment strategies when faced with outbreaks of novel pathogens. During the COVID-19 pandemic, there was an urgent need to rapidly assess

CONCLUSIONS: Despite the stress imposed by the COVID-19 pandemic, the adolescents showed a positive development in their socioemotional competences. Family-related factors emerged as important variables in predicting adolescent socioemotional

CONCLUSION: The current findings underscore the need for increased attention on improving the psychological capital of medical interns.

CONCLUSIONS: SARS-CoV-2 RNA was widely distributed on high-touch surfaces and on PPE in the contaminated zone of the Fangcang shelter hospital, implying a potentially high infection risk for healthcare workers. Our findings emphasize the need to

CONCLUSIONS: Saliva sampling for COVID-19 at ECE sites is an acceptable strategy as an additional layer of protection for 4- and 5-year-olds; however, alternate testing strategies may be needed for younger children.

Objectives At the beginning of the COVID-19 pandemic, the number of infected children was lower than that of adults. Most cases were transmitted in the family, asymptomatic, and severe cases were rare. In the sixth wave in Japan the number of

CONCLUSIONS: The COVID-19 pandemic has had a lasting impact on deaths due to respiratory diseases and certain specific causes of death in 2021, and it has disproportionately affected certain regions.

BACKGROUND: Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare complication of adenoviral vector-based vaccines against the SARS-CoV-2 virus. The syndrome is caused by antibodies against platelet factor 4 (PF4, CXCL4) that lead to

The SARS-CoV-2 Omicron variant harbours many mutations in its spike protein compared to the original SARS-CoV-2 strain, which may alter its ability to enter cells, cell tropism, and response to interventions blocking virus entry. To elucidate these

As healthcare data becomes increasingly available from various sources, including clinical institutions, patients, insurance companies, and pharmaceutical industries, machine learning (ML) services are becoming more significant in healthcare-facing