CONCLUSION: Our findings highlight that eight motivational factors and seven barriers influence health students' involvement in COVID-19 volunteering. However, to optimize the potential of health students, further preparation is essential to ensure
This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID-19 patient mortality. An Extreme Gradient Boosting (XGBoost) prediction model is built based on a de-identified Austin area
Accurate and dependable air quality forecasting is critical to environmental and human health. However, most methods usually aim to improve overall prediction accuracy but neglect the accuracy for unexpected incidents. In this study, a hybrid model
Intracranial inoculation of susceptible mice with a glial-tropic strain of mouse hepatitis virus (JHMV), a murine coronavirus, results in an acute encephalomyelitis followed by viral persistence in white matter tracts accompanied by chronic
CONCLUSION: Our findings describe the characteristics of pregnant Hispanic females living in Puerto Rico. The majority reported adhering adequately to their health services, with few or no changes in their prenatal care.
The circulating flu viruses merging with the ongoing COVID-19 pandemic raises a more severe threat that promotes the infectivity of SARS-CoV-2 associated with higher mortality rates. Here, we conjugated recombinant receptor binding domain (RBD) of
CONCLUSIONS: CCT enhanced compassion skills while reducing psychological distress in medical students, this being critical to preserving the mental health of physicians while promoting compassionate care for patients. The need for institutions to
COVID-19 are causing many psychological impacts and change many aspects of human life. Mental health services also experiencing changes because of COVID-19 outbreak. In Indonesia, COVID-19 outbreak prompted the rapid development of online mental
CONCLUSION: PMN-MDSCs are associated with disease severity in COVID-19; however, MDSC levels do not predict increased risk of death during hospitalization.