Zedoarondiol inhibits human bronchial smooth muscle cell proliferation through the CAV-1/PDGF signalling pathway.

Airway remodelling in lung diseases can be treated by inhibiting excessive smooth muscle cell proliferation. Zedoarondiol (Zed) is a natural compound isolated from the Chinese herb Curcuma longa. The caveolin-1 (CAV-1) is widely expressed in lung cells and plays a key role in platelet-derived growth factor (PDGF) signalling and cell proliferation. This study aims to investigate the effect of Zed on human bronchial smooth muscle cell (HBSMC) proliferation and explore its potential molecular mechanisms. We assessed the effect of Zed on the proliferation of PDGF-stimulated HBSMCs and performed proteomic analysis to identify potential molecular targets and pathways. CAV1 siRNA was used to validate our findings in vitro. In PDGF-stimulated HBSMCs, Zed significantly inhibited excessive proliferation of HBSMCs. Proteomic analysis of zedoarondiol-treated HBSMCs revealed significant enrichment of differentially expressed proteins in cell proliferation-related pathways and biological processes. Zed inhibition of HBSMC proliferation was associated with upregulation of CAV1, regulation of the CAV-1/PDGF pathway and inhibition of MAPK and PI3K/AKT signalling pathway activation. Treatment of HBSMCs with CAV1 siRNA partly reversed the inhibitory effect of Zed on HBSMC proliferation. Thus, this study reveals that zedoarondiol potently inhibits HBSMC proliferation by upregulating CAV-1 expression, highlighting its potential value in airway remodelling and related diseases.

Comparison of three machine learning models to predict suicidal ideation and depression among Chinese adolescents: A cross-sectional study.

BACKGROUND: Machine learning (ML) algorithms based on various clinicodemographic, psychometric, and biographic factors have been used to predict depression, suicidal ideation, and suicide attempt in adolescents, but there is still a need for more accurate and efficient models for screening the general adolescent population. In this study, we compared various ML methods to identify a model that most accurately predicts suicidal ideation and level of depression in a large cohort of school-aged adolescents. METHODS: Ten psychological scale scores and 20 sociodemographic parameters were collected from 10,243 Chinese adolescents in the first or second year of middle school and high school. These variables were then included in a random forest (RF) model, support vector machine (SVM) model, and decision tree model for factor screening, dichotomous prediction of suicidal ideation (yes/no), and trichotomous prediction of depression (no depression, mild-moderate depression, or major depression). RESULTS: The RF model demonstrated greater accuracy for predicting suicidal ideation (mean accuracy (ACC) = 87.3 %, SD = 3.2 %, area under curve (AUC) = 92.4 %) and depressive status (ACC = 84.0 %, SD = 2.8 %, AUC = 90.1 %) than SVM and decision tree models. We have also used the RF model to predict adolescents with both depression and suicidal ideation with satisfactory results. Significant differences were found in several sociodemographic parameters and scale scores among classification groups and differences in six factors between sexes. CONCLUSIONS: This RF model may prove valuable for predicting suicidal ideation, depression, and non-suicidal self-injury among the general population of Chinese adolescents.

Mental states and personality based on real-time physical activity and facial expression recognition.

Introduction: To explore a quick and non-invasive way to measure individual psychological states, this study developed interview-based scales, and multi-modal information was collected from 172 participants. Methods: We developed the Interview Psychological Symptom Inventory (IPSI) which eventually retained 53 items with nine main factors. All of them performed well in terms of reliability and validity. We used optimized convolutional neural networks and original detection algorithms for the recognition of individual facial expressions and physical activity based on Russell's circumplex model and the five factor model. Results: We found that there was a significant correlation between the developed scale and the participants' scores on each factor in the Symptom Checklist-90 (SCL-90) and Big Five Inventory (BFI-2) [r = (-0.257, 0.632), p < 0.01]. Among the multi-modal data, the arousal of facial expressions was significantly correlated with the interval of validity (p < 0.01), valence was significantly correlated with IPSI and SCL-90, and physical activity was significantly correlated with gender, age, and factors of the scales. Discussion: Our research demonstrates that mental health can be monitored and assessed remotely by collecting and analyzing multimodal data from individuals captured by digital tools.