PubMed COVID-19 Clinical Care

CONCLUSIONS: Among the three major factors of vaccine hesitancy, complacency was proved the most notable one among COVID-19 recovered patients, educational campaigns can focus on raising the awareness of risk of infection and the benefits of

No abstract

Since the outbreak of the 2019 pandemic coronavirus disease (COVID-19), great attention has been given to identifying the main clinical features of the disease. Identification of laboratory parameters able to classify patients based on their risk is

Altered circulating hormone and metabolite levels have been reported during and post-COVID-19. Yet, studies of gene expression at the tissue level capable of identifying the causes of endocrine dysfunctions are lacking. Transcript levels of endocrine

The COVID-19 pandemic has escalated the risk of SARS-CoV-2 transmission in the dental practice, especially as droplet-aerosol particles are generated by high-speed instruments. This has heightened awareness of other orally transmitted viruses

This research aims to uncover how the association between social determinants of health and COVID-19 cases and fatality rate have changed across time and space. To begin to understand these associations and show the benefits of analysing temporal and

CONCLUSION: Parental anxiety and depression caused by the COVID-19 pandemic and lockdown resulted in decreased attention to childcare, leading to increased accidental ingestion of harmful materials among paediatric forensic cases admitted to the

This study employs the media system dependency theory to explore both macro- and micro-level dependencies between South Korean sojourners and Chinese media during the COVID-19 pandemic. Through semi-structured interviews with 25 South Korean

CONCLUSIONS: Pre-existing risk may interact with subsequent perceptions of adversity to impact well-being. In combination with existing research, this small study suggests prevention programs should focus upon managing concurrent mental health and

In this work, we aim to accurately predict the number of hospitalizations during the COVID-19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising dynamics-based deep learning model for spatiotemporal COVID-19