Journal of the Chinese Medical Association : JCMA
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Currently, as the coronavirus disease (COVID-19) has become a pandemic, rapidly obtaining accurate information of patient symptoms and their progression is crucial and vital. Although the early studies in China have illustrated that the representative symptoms of COVID-19 include (dry) cough, fever, headache, fatigue, gastrointestinal discomfort, dyspnea, and muscle pain, there is increasing evidence to suggest that olfactory and taste disorder are related to the COVID-19 pandemic. Therefore, we conduct this study to review the present literature about the correlation between anosmia or dysgeusia and COVID-19. ⋯ Studies have shown that smell and taste disturbances may represent an early symptom of COVID-19 and healthcare professionals must be very vigilant when managing patients with these symptoms. In the pandemic era, this implies testing for COVID-19 by healthcare workers with full personal protective equipment.
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Low birth weight and preterm or early-term babies may have a higher risk of poor health. One of the main factors is the weight gain of a pregnant woman during gestational weeks in the second and third trimesters. Changes in weight over a month in a pregnant woman might also have an impact on infant outcomes. This study aimed to investigate the association between maternal weight at different time points and low birth weight and preterm or early-term babies (premature babies). ⋯ An increase in weight gain after 32 weeks was shown to reduce the risk of low birth weight and premature babies. Maternal weight monitoring was suggested to be conducted every 4 weeks to minimize the chance of having a low birth weight and premature baby.
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In clinical applications, mucosal healing is a therapeutic goal in patients with ulcerative colitis (UC). Endoscopic remission is associated with lower rates of colectomy, relapse, hospitalization, and colorectal cancer. Differentiation of mucosal inflammatory status depends on the experience and subjective judgments of clinical physicians. We developed a computer-aided diagnostic system using deep learning and machine learning (DLML-CAD) to accurately diagnose mucosal healing in UC patients. ⋯ Our DLML-CAD diagnosis achieved 94.5% accuracy for endoscopic mucosal healing and 89.0% accuracy for complete mucosal healing. This system can provide clinical physicians with an accurate auxiliary diagnosis in treating UC.