In the context of climate change and global warming, heat-related illness (HRI) is anticipated to escalate and become a major concern. Patients with severe HRI primarily present to the emergency department (ED), but there are no prediction tools for mortality in HRI patients who visit ED. The objective of this study was to use machine learning approaches to establish prediction models for mortality in patients with HRI who visit ED. We included all patients aged 20 and above with a final diagnosis of HRI who visited the EDs of three hospitals (Chi Mei Medical Center, Chi Mei Hospital Liouying, and Chi Mei Hospital Chiali) between January 2010 and October 2021. Patients who had transferred to other hospitals or had insufficient data were excluded. A total of 11 predictive feature variables were used in the algorithms. The primary outcome was in-hospital mortality or impending death discharge. We used machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP), and eXtreme Gradient Boosting (XGBoost) to establish prediction models for mortality in such patients. Accuracy, sensitivity, specificity, and area under curve (AUC) were used as indicators to evaluate the performance of prediction models. Out of the 820 HRI patients included in the analysis, 1.5% had mortality. All six prediction models had a high AUC, ranging from 0.825 to 0.991, and LightGBM which included peripheral oxygen saturation (SpO₂₎ and Glasgow Coma Scale (GCS) score on arrival as the two main features had the highest AUC. The accuracy, sensitivity, and specificity of LightGBM were 0.976, 1.000 and 0.975, respectively. Machine learning-based prediction models are promising tools in accurately predicting mortality in HRI patients who present to the ED.

Oral submucous fibrosis (OSF) is a premalignant condition of the oral cavity, and its pathogenesis remains largely unknown. A multitude of non-coding RNAs are aberrantly expressed in OSF, and their implication for the development of OSF is a matter meriting investigation. The functional role of long non-coding RNA NCK1-AS1 in myofibroblast activation of fibrotic buccal mucosal fibroblasts (fBMFs) derived from OSF tissues was assessed. Wound healing, collagen gel contraction and transwell migration assays have been employed to assess the myofibroblast activities. In addition, a luciferase-based reporter assay was used to illustrate the potential mechanism underlying the regulation of NCK1-AS1 in myofibroblast activation. Silencing of NCK1-AS1 markedly downregulated myofibroblast activation and the expression of fibrosis markers in fBMFs. Besides, we demonstrated that NCK1-AS1 directly interacted with microRNA-137 (miR-137) and was negatively correlated with it. Moreover, we found that NCK1 was a target of miR-137 and positively related to NCK1-AS1. Our results demonstrated that NCK1-AS1 may regulate myofibroblast activation by suppressing miR-137 and upregulating NCK1. We showed that NCK1-AS1 acted as a sponge of miR-137 and titrated the suppressive effect of miR-137 on NCK1 to modulate myofibroblast activation in OSF condition.