PubMed COVID-19 Clinical Care

CONCLUSION: During the COVID-19 pandemic, physical inactivity, sedentary lifestyle, and poor eating patterns were the most common risk factors for obesity. Additionally, unhealthy eating habits, excessive behavioural stress, depression, anxiety, low

Since the beginning of the COVID-19 pandemic, great efforts have been made by the academic community to better understand SARS-CoV-2 and COVID-19 features. COVID-19 has been demonstrated to affect several systems of the human body, including the

CONCLUSION: Poor practice of posture and computer ergonomics, despite the good knowledge and attitude is possibly due to the suboptimal work environments. Introducing simple practical measures to facilitate ergonomically appropriate work environments

Healthcare workers experience moral injury (MI), a violation of their moral code due to circumstances beyond their control. MI threatens the healthcare workforce in all settings and leads to medical errors, depression/anxiety, and personal and

Gross anatomy education utilizing body donors and human specimens assists the acquisition of non-traditional, discipline-independent skills (NTDIS) such as teamwork, communication, and leadership. Alterations to anatomy curricula, such as those

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

CONCLUSION: This study demonstrated that neither the chatbot nor the webinar changed VI importantly compared with control. Interactive webinars could be an effective tool to change vaccine confidence. Further study is needed to identify risk factors

Despite the higher transmissibility of Omicron Variant of Concern (VOC), several reports have suggested lower risk for hospitalization and severe outcomes compared to previous variants of SARS-CoV-2. This study, enrolling all COVID-19 adults admitted

Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on

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