Our international study, the largest of this type ever undertaken, shows that people with diabetes frequently have depressive disorders and also significant levels of depressive symptoms. Our findings indicate that the identification and appropriate care for psychological and psychiatric problems is not the norm and suggest a lack of the comprehensive approach to diabetes management that is needed to improve clinical outcomes.
COVID-19, the most severe public health problem to occur in the past 10 years, has greatly impacted people's mental health. Colleges in China have reopened, and how to prevent college students from suffering secondary damage due to school reopening remains elusive. This cross-sectional study was aimed to evaluate the psychological impact of COVID-19 after school reopening and explore via machine learning the factors that influence anxiety and depression among students. Among the 478 valid online questionnaires collected between September 14th and September 20th, 74 (15.5%) showed symptoms of anxiety (by the Self-Rating Anxiety Scale), and 155 (32.4%) showed symptoms of depression (by Patient Health Questionnaire-9). Descriptive analysis of basic personal characteristics indicated that students at a higher grade, having relatives or friends who have been infected, fearing being infected, and having a pessimistic attitude to COVID-19 easily experience anxiety or depression. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to counteract the imbalance of retrieved data. The Akaike Information Criterion (AIC) and multivariate logistic regression were performed to explore significant influence factors. The results indicate that exercise frequency, alcohol use, school reopening, having relatives or friends who have been infected, self-quarantine, quarantine of classmates, taking temperature routinely, wearing masks routinely, sleep quality, retaining holiday, availability of package delivery, take-out availability, lockdown restriction, several areas in school closed due to COVID-19, living conditions in the school, taking the final examinations after school reopening, and the degree to which family economic status is influenced by COVID-19 are the primary influence factors for anxiety or depression. To evaluate the effect of our model, we used 5-fold cross-validation, and the average area under the curve (AUC) values of the receiver operating characteristic (ROC) curves of anxiety and depression on the test set reached 0.885 and 0.806, respectively. To conclude, we examined the presence of anxiety and depression symptoms among Chinese college students after school reopening and explored many factors influencing students' mental health, providing reasonable school management suggestions.
Background and Aims: COVID-19 has been proven to harm adolescents' mental health, and several psychological influence factors have been proposed. However, the importance of these factors in the development of mood disorders in adolescents during the pandemic still eludes researchers, and practical strategies for mental health education are limited.Methods: We constructed a sample of 1,771 adolescents from three junior high middle schools, three senior high middle schools, and three independent universities in Shandong province, China. The sample stratification was set as 5:4:3 for adolescent aged from 12 – 15, 15 – 18, 18 – 19. We examined the subjects' anxiety, depression, psychological resilience, perceived social support, coping strategies, subjective social/school status, screen time, and sleep quality with suitable psychological scales. We chose four widely used classification models-k-nearest neighbors, logistic regression, gradient-boosted decision tree (GBDT), and a combination of the GBDT and LR (GBDT + LR)-to construct machine learning models, and we utilized the Shapley additive explanations value (SHAP) to measure how the features affected the dependent variables. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves was used to evaluate the performance of the models.Results: The current rates of occurrence of symptoms of anxiety and depression were 28.3 and 30.8% among the participants. The descriptive and univariate analyses showed that all of the factors included were statistically related to mood disorders. Among the four machine learning algorithms, the GBDT+LR algorithm achieved the best performance for anxiety and depression with average AUC values of 0.819 and 0.857. We found that the poor sleep quality was the most significant risk factor for mood disorders among Chinese adolescents. In addition, according to the feature importance (SHAP) of the psychological factors, we proposed a five-step mental health education strategy to be used during the COVID-19 pandemic (sleep quality-resilience-coping strategy-social support-perceived social status).Conclusion: In this study, we performed a cross-sectional investigation to examine the psychological impact of COVID-19 on adolescents. We applied machine learning algorithms to quantify the importance of each factor. In addition, we proposed a five-step mental health education strategy for school psychologists.
Long non-coding RNA (lncRNA) plays a crucial role in modulating genome instability, immune characteristics, and cancer progression, within which genome instability was identified as a critical regulator in tumorigenesis and tumor progression. However, the existing accounts fail to detail the regulatory role of genome instability in lung adenocarcinoma (LUAD). We explored the clinical value of genome instability-related lncRNA in LUAD with multi-omics bioinformatics analysis. We extracted the key genome instability-related and LUAD-related gene modules using weighted gene co-expression network analysis (WGCNA) and established a competing endogenous RNA (ceRNA) network using four lncRNAs (LINC01224, LINC00346, TRPM2-AS, and CASC9) and seven target mRNAs (CCNF, PKMYT1, GCH1, TK1, PSAT1, ADAM33, and DDX11). We found that LINC01224 is primarily located in the cytoplasm and that LINC00346 and TRPM2-AS are primarily located in the nucleus (CASC9 unknown). We found that all 11 genes were positively related to tumor mutational burden and involve drug resistance, cancer stemness, and tumor microenvironment infiltration. Additionally, an eight-lncRNA genome instability-related lncRNA signature was established and validated, predicting the overall survival and immunotherapy outcomes in LUAD. To conclude, we discovered that sponging microRNA, genome instability-related lncRNA functions as ceRNA, modulating genomic integrity. This research provides clinical references for LUAD immunotherapy and prognosis and interprets a potential genome instability-related ceRNA regulatory network in which LINC01224-miR-485-5p/miR-29c-3p-CCNF-RRM2 and LINC01224-miR485-5p-PKMYT1-CDK1 axes were the most promising pathways. However, the potential mechanisms underlying our findings still need biological validation through in vitro and in vivo experiments.
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