There is a substantial incidence of IBD in China. Although still lower than in the West, the emergence of IBD will necessitate specific health care planning and education and offers the possibility of identifying causative factors in a population with a rapidly increasing incidence.

Background The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide. Objectives To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted. Methods Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients’ data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort. Results Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%. Conclusion Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

CD163 is a useful adjunct in distinguishing AFX from other malignant cutaneous spindle cell tumors and offers improved specificity in identifying cutaneous histiocytic/dendritic lesions.

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Nodal, a potent embryonic morphogen in the transforming growth factor-b family, is a proposed key regulator of melanoma tumorigenicity. However, there has been no systematic study of Nodal expression in melanocytic lesions. We investigated Nodal expression by immunohistochemistry in 269 melanocytic lesions, including compound nevi, dysplastic nevi, congenital nevi, Spitz nevi, melanoma in situ, malignant melanoma including the variant desmoplastic melanoma, and metastatic melanoma. We found that the Nodal expression was significantly increased in malignant lesions (including melanoma in situ, malignant melanoma, and metastatic melanoma) compared with compound nevi, Spitz nevi, and dysplastic nevi. Surprisingly, congenital nevi expressed a level of Nodal comparable with malignant lesions, whereas desmoplastic melanoma showed lower expression than nondesmoplastic malignant melanoma (Po0.05). Deep melanoma (Breslow depth 41 mm) displayed a higher percentage of Nodal-positive tumor cells than did superficial melanoma (Breslow depth r1 mm), although there was no statistical difference in the overall staining intensity (P ¼ 0.18). Melanomas in situ showed a lower level of Nodal expression than did deep melanomas and metastatic melanomas (Po0.05). The low expression of Nodal in normal and dysplastic nevi, and its increasing expression with the progression of malignant lesions, are suggestive of a role for Nodal in melanoma progression.