Epithelial to mesenchymal phenotype transition is a common phenomenon during embryonic development, wound healing, and tumor metastasis. This transition involves cellular changes in cytoskeleton architecture and protein expression. Specifically, this highly regulated biological event plays several important roles during craniofacial development. This review focuses on the regulation of epithelial-mesenchymal transformation (EMT) during neural crest cell migration, and fusion of the secondary palate and the upper lip.

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Background: Information regarding risk factors associated with severe coronavirus disease (COVID-19) is limited. This study aimed to develop a model for predicting COVID-19 severity. Methods: Overall, 690 patients with confirmed COVID-19 were recruited between 1 January and 18 March 2020 from hospitals in Honghu and Nanchang; finally, 442 patients were assessed. Data were categorised into the training and test sets to develop and validate the model, respectively. Findings: A predictive HNC-LL (Hypertension, Neutrophil count, C-reactive protein, Lymphocyte count, Lactate dehydrogenase) score was established using multivariate logistic regression analysis. The HNC-LL score accurately predicted disease severity in the Honghu training cohort (area under the curve [AUC]=0.861, 95% confidence interval [CI]: 0.800À0.922; P<0.001); Honghu internal validation cohort (AUC=0.871, 95% CI: 0.769À0.972; P<0.001); and Nanchang external validation cohort (AUC=0.826, 95% CI: 0.746À0.907; P<0.001) and outperformed other models, including CURB-65 (confusion, uraemia, respiratory rate, BP, age 65 years) score model, MuLBSTA (multilobular infiltration, hypo-lymphocytosis, bacterial coinfection, smoking history, hypertension, and age) score model, and neutrophil-to-lymphocyte ratio model. The clinical significance of HNC-LL in accurately predicting the risk of future development of severe COVID-19 was confirmed. Interpretation: We developed an accurate tool for predicting disease severity among COVID-19 patients. This model can potentially be used to identify patients at risks of developing severe disease in the early stage and therefore guide treatment decisions.