Background Anxiety, depression, and dementia are important issues affecting the mental health of the older population. Given the relationship between mental health and physical disorders, it is particularly important to diagnose and identify these psychological problems in older people. Methods Psychological data of 15,173 older people living in various districts and counties of Shanxi province, China, were extracted from data collected through the ‘13th Five-Year Plan for Healthy Aging—Psychological Care for the Elderly Project’ of the National Health Commission of China in 2019. Three different ensemble learning classifiers [random forest (RF), Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)] were evaluated, and the best classifier with the selected feature set was selected. The ratio of training to testing cases was 8:2. The predictive performance of the three classifiers was evaluated by calculating the area under the receiver operating characteristic curve (AUC), accuracy, recall, and F measurement based on 10-fold cross-validation and ranked by AUC. Results All the three classifiers have achieved good prediction results. In the test set, the AUC value range for the three classifiers was 0.79 to 0.85. The LightGBM algorithm showed higher accuracy than both the baseline and XGBoost. A new machine learning (ML)-based model to predict mental health problems in older people was constructed. The model was interpretative and could hierarchically predict psychological problems including anxiety, depression, and dementia in older people. Experimental results showed that the method could accurately identify those suffering from anxiety, depression, and dementia in different age groups. Conclusions A simple method model was designed based on only eight problems, which had good accuracy and was widely applicable to the older of all ages. Overall, this research approach avoided the need to identify older people with poor mental health through the traditional standardized questionnaire approach.
The present study aimed to identify the function of miR-491-3p in regulating non-small cell lung cancer (NSCLC). Tumor tissues and adjacent normal tissues were collected from 43 patients with NSCLC. A549 and H1299 cells were transfected with microRNA (miR)-491-3p mimic, mimic negative control (NC), miR-491-3p inhibitor, inhibitor NC, pcDNA3.1-FGF5 vector and control vector. Cell counting kit-8 assay and Edu experiments were performed to assess cell viability and proliferation. Matrigel experiment, wound healing assay and flow cytometric analysis were performed to explore cell invasion, migration and apoptosis, respectively. A dual-luciferase reporter experiment was performed to identify the relationship between miR-491-3p and fibroblast growth factor 5 (FGF5). In vivo study was conducted by using nude mice. The miR-491-3p and FGF5 protein expression levels were investigated using reverse transcription-quantitative polymerase chain reaction and western blot analysis. In NSCLC tumor tissues, miR-491-3p was downregulated and FGF5 was upregulated (P<0.01). Low miR-491-3p expression and high FGF5 mRNA expression was associated with poor outcomes in patients, including advanced TNM stage and lymph node metastasis (P<0.05). upregulation of miR-491-3p suppressed viability, proliferation, invasion and migration of NSCLC cells; however, it promoted apoptosis (P<0.01). FGF5 was a target gene for miR-491-3p. miR-491-3p directly inhibited FGF5 expression. upregulation of FGF5 significantly reversed the inhibitory effects of miR-491-3p on malignant phenotypes of NSCLC cells (P<0.01). miR-491-3p overexpression suppressed the in vivo growth of NSCLC. Thus, it was identified that miR-491-3p functions as a tumor suppressor in NSCLC by directly targeting FGF5.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.