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  1. Fulltext A network analysis of the heterogeneity and associated risk and protective factors of depression and anxiety among college students
    Niu C, Jiang Y, Li Y, Wang X, Zhao H, Cheng Z, et al.
    Sci Rep, 2025 Feb 25;15(1):6699.
    PMID: 40000716 DOI: 10.1038/s41598-025-91025-9
    BACKGROUND: Comorbidity of depression and anxiety is common among adolescents and can lead to adverse outcomes. However, there is limited understanding of the latent characteristics and mechanisms governing these disorders and their interactions. Moreover, few studies have examined the impacts of relevant risk and protective factors.

    METHODS: This cross-sectional study involved 1,719 students. Mplus 8.0 software was used to conduct latent profile analysis to explore the potential categories of depression and anxiety comorbidities. R4.3.2 software was used to explore the network of core depression and anxiety symptoms, bridge these disorders, and evaluate the effects of risk and protective factors.

    RESULTS: Three categories were established: "healthy" (57.8%), "mild depression-mild anxiety" (36.6%), and "moderately severe depression-moderate anxiety" (5.6%). "Depressed mood", "nervousness", and "difficulty relaxing" were core symptoms in both the depression-anxiety comorbidity network and the network of risk and protective factors. Stress perception and neuroticism serve as bridging nodes connecting some symptoms of depression and anxiety and are thus considered the most prominent risk factors.

    CONCLUSIONS: According to the core and bridging symptoms identified in this study, targeted intervention and treatment can be provided to groups with comorbid depression and anxiety, thereby reducing the risk of these comorbidities in adolescents.

  2. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models
    Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, et al.
    Sci Total Environ, 2018 Sep 01;634:853-867.
    PMID: 29653429 DOI: 10.1016/j.scitotenv.2018.04.055
    The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers.
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