Background: Curcumol, possessing antiviral, antifungal, antimicrobial, anticancer, and anti-inflammatory properties, has been widely used in treating cancers and liver fibrosis. The aim of this study was to determine the effect of curcumol on the progression of asthma.Materials and methods: Curcumol was administrated to platelet-derived growth factor (PDGF)- BB-stimulated airway smooth muscle cells (ASMCs). The proliferation of ASMCs was assessed by MTT and EdU incorporation assays. The apoptosis of ASMCs was measured by flow cytometry and Western blotting. The migration of ASMCs was evaluated by Transwell migration assay and Western blotting. The regulatory effects of curcumol on extracellular signal-regulated protein kinase (ERK)/cAMP response element-binding protein (CREB) pathway was evaluated by Western blotting.Results: The proliferation and migration of ASMCs induced by PDGF-BB was suppressed, and the apoptosis of ASMCs was elevated by curcumol in a dose-dependent manner. The activation of ERK/CREB pathway induced by PDGF-BB was suppressed by curcumol.Conclusion: Curcumol could suppress ERK/CREB pathway to inhibit proliferation and migration and promote apoptosis of PDGF-BB-stimulated ASMCs. These findings suggest that curcumol may act as a potential drug for asthma treatment.

This study aimed to explore the independent risk factors for community-acquired pneumonia (CAP) complicated with acute respiratory distress syndrome (ARDS) and to predict and evaluate the risk of ARDS in CAP patients based on artificial neural network models (ANNs). We retrospectively analyzed eligible 989 CAP patients (632 men and 357 women) who met the criteria from the comprehensive intensive care unit (ICU) and the respiratory and critical care medicine department of Changzhou Second People’s Hospital, Jiangsu Provincial People’s Hospital, Nanjing Military Region General Hospital, and Wuxi Fifth People’s Hospital between February 2018 and February 2021. The best predictors to model the ANNs were selected from 51 variables measured within 24 h after admission. By using this model, patients were divided into a training group (n = 701) and a testing group (n = 288 patients). Results showed that in 989 CAP patients, 22 important variables were identified as risk factors. The sensitivity, specificity, and accuracy of the ANNs model training group were 88.9%, 90.1%, and 89.7%, respectively. When ANNs were used in the test group, their sensitivity, specificity, and accuracy were 85.0%, 87.3%, and 86.5%, respectively; when ANNs were used to predict ARDS, the area under the receiver operating characteristic (ROC) curve was 0.943 (95% confidence interval (0.918–0.968)). The nine most important independent variables affecting the ANNs models were lactate dehydrogenase (100%), activated partial thromboplastin time (84.6%), procalcitonin (83.8%), age (77.9%), maximum respiratory rate (76.0%), neutrophil (75.9%), source of admission (68.9%), concentration of total serum kalium (61.3%), and concentration of total serum bilirubin (50.4%) (all important >50%). The ANNs model and the logistic regression models were significantly different in predicting and evaluating ARDS in CAP patients. Thus, the ANNs model has a good predictive value in predicting and evaluating ARDS in CAP patients, and its performance is better than that of the logistic regression model in predicting the incidence of ARDS patients.