PubMed COVID-19 Clinical Care

AIM: This study examines the changes in gastroesophageal reflux disease (GERD) symptom frequency among patients with GERD throughout the COVID-19 pandemic.

For the COVID-19 pandemic, viral transmission has been documented in many historical and geographical contexts. Nevertheless, few studies have explicitly modeled the spatiotemporal flow based on genetic sequences, to develop mitigation strategies

In this study, we explored the genomic architecture and phylogenomic relationship of BA.2.75, a subvariant of Omicron SARS-CoV-2. A set of 1468 whole-genome sequences of BA.2.75, submitted by 28 countries worldwide were retrieved from GISAID and used

CONCLUSIONS: These findings may help public health institutions and social media platforms mitigate the spread of health-related, low-credibility information by revealing vulnerable web-based communities.

CONCLUSIONS: This study offers information regarding the healthcare professionals' experiences with the GC method in a PACU setting; further, it deepens the understanding of the daily patient safety work using this incident reporting method.

CONCLUSIONS: The prevalence of asthma in children differed markedly among the different regions of Mexico; two regions, Northwest and Southeast, stood out. This study puts into context the role of the environment on the prevalence of asthma in

COVID-19 has drastically changed human behaviors and posed a threat to globalism by spurring a resurgence of nationalism. Promoting prosocial behavior within and across borders is of paramount importance for global cooperation to combat pandemics. To

COVID-19 pandemic resulted in an unprecedented crisis with extreme distress for the frontline physicians and increased risk of developing burnout. Burnout has a negative impact on patients and physicians, posing a substantial risk in patient safety

CONCLUSION: Genomic dinucleotides represented as DCR indicate a host-specific separation, and clustering predicts a linear asymptotic adaptation shift of bat CoVs from other mammals to humans via deep learning.

CONCLUSION: The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes.