Background: The prevalence of depression among college students is higher than that of the general population. Although a growing body of research suggests that depression in college students and their potential risk factors, few studies have focused on the correlation between depression and risk factors. This study aims to explore the mediating role of perceived social support and resilience in the relationship between trait coping styles and depression among college students. Methods: A total of 1262 college students completed questionnaires including the Trait Coping Styles Questionnaire (TCSQ), the Patient Health Questionnaire-9 (PHQ-9), the Perceived Social Support Scale (PSSS), and the Resilience Scale-14 (RS-14). Common method bias tests and spearman were conducted, then regressions and bootstrap tests were used to examine the mediating effects. Results: In college students, there was a negative correlation between perceived control PC and depression, with a significant direct predictive effect on depression (β = -0.067, P < 0.01); in contrast, negative control NC showed the opposite relationship (β = 0.057, P < 0.01). PC significantly positively predicted perceived social support (β = 0.575, P < 0.01) and psychological resilience (β = 1.363, P < 0.01); conversely, NC exerted a significant negative impact. Perceived social support could positively predict psychological resilience (β = 0.303, P < 0.01), and both factors had a significant negative predictive effect on depression. Additionally, Perceived social support and resilience played a significant mediating role in the relationship between trait coping styles and depression among college students, with three mediating paths: PC/NC → perceived social support → depression among college students (-0.049/0.033), PC/NC→ resilience → depression among college students (-0.122/-0.021), and PC/NC → perceived social support → resilience → depression among college students (-0.016/0.026). Conclusion: The results indicate that trait coping styles among college students not only directly predict lower depression but also indirectly influence them through perceived social support and resilience. This suggests that guiding students to confront and solve problems can alleviate their depression.

Objective: The potential mechanisms linking social participation and depressive symptoms in Chinese individuals with multimorbidity are not yet fully understood. This study aims to explore how cognitive function and activities of daily living (ADLs) mediate the relationship between social participation and depressive symptoms in individuals with multimorbidity. Methods: We selected 3782 participants with multimorbidity from the 2018 China Health and Retirement Longitudinal Study. Data related to social participation, cognitive function, ADLs, and depressive symptoms were extracted. Regression and Bootstrap analyses were used to explore the sequential mediating effects of social participation, cognitive function, ADLs, and depressive symptoms. Results: (1) There was a significant correlation between social participation, cognitive function, activities of daily living, and depressive symptoms (p < 0.01). (2) Social participation directly affected depressive symptoms (β = -0.205, p < 0.05). (3) Cognitive function (β = -0.070, p < 0.01) and activities of daily living (β = -0.058, p < 0.01) played separate mediating roles in the effect of social participation on depressive symptoms. (4) Cognitive function and activities of daily living had a chain-mediated role in the relationship between social participation and depressive symptoms in patients with multimorbidity (β = -0.020, p < 0.01). Conclusion: A chained mediating effect was found between cognitive function, ADLs, social participation, and depressive symptoms in patients with multimorbidity. Social participation was found to improve the cognitive function of patients with multimorbidity, which in turn enhanced their daily life activities and ultimately alleviated their depressive symptoms.

Depressed mood affects a significant number of patients with cancer, and can impair their quality of life and interfere with successful treatment. Our study aims to create a predictive model for identifying high-risk groups of depressed mood in cancer patients, offering a theoretical support for preventing depressed mood in these individuals. The China Health and Retirement Longitudinal Study (CHARLS) provided the data for this research, which used CES-D as a tool to identify individuals with depressed mood. Influencing factors of depressed mood in cancer patients was analyzed using a binary logistic regression model. Using the Harvard Cancer Index, we classified the high-risk patients for depressed mood. In present study, 52.96 % of cancer patients met criteria for depressed mood based on the CES-D. Significant correlations were found between depressed mood and factors such as gender, self-rated health, sleep duration, exercise, satisfaction with family, residence, education, life satisfaction, and medical insurance. Utilizing the Harvard Cancer Index, we classified patients into five risk levels for depressed mood, revealing a significant variation in the number of depressive patients across these levels (x²=99.82, P < 0.05). Notably, the incidence of depressed mood increased with the risk level among cancer patients (x²=103.40, P < 0.05). Lack of data on tumor typing and subgroups makes it unlikely to explore the specifics of depressed mood in patients with various types of cancer. The determinants of depressed mood in cancer patients are multi-dimensional. The Harvard Cancer Index may be helpful in identifying high-risk populations.

Most existing studies focus on either the relationship between sarcopenia and depressive symptoms or sarcopenia and cognitive functions. Few studies have examined depressive symptoms and cognitive functions simultaneously. This study aimed to analyze the relationship between sarcopenia and both depressive symptoms and cognitive functions in the elderly by introducing activities of daily living to explore the mediating role of activities of daily living between sarcopenia and mental health. This study used data from the 2015 and 2018 China Health and Retirement Longitudinal Study, including a total of 3,552 older adults aged 60 and above. We conducted descriptive statistical analysis and correlation analysis on the data and used seemingly unrelated regression to examine the relationship between sarcopenia, depressive symptoms, and cognitive functions in older adults. Bootstrap mediation analysis was used to further explore the mediating role of activities of daily living in the relationship between sarcopenia and depressive symptoms and cognitive functions in the elderly. After adjusting for confounding factors, sarcopenia was positively correlated with depressive symptoms (β = 0.663, p < 0.001) and negatively correlated with cognitive functions (β=-0.748, p < 0.001), indicating a negative impact of sarcopenia on mental health. Activities of daily living mediated the relationship between sarcopenia and mental health, with a stronger mediating effect between sarcopenia and depressive symptoms (18.77%, β = 0.125, 95%CI (0.035, 0.222)) than between sarcopenia and cognitive functions (12.82%, β=-0.099, 95%CI (-0.162, -0.035)). Activities of daily living partially mediated the relationship between sarcopenia and mental health status in Chinese older adults. Interventions targeting sarcopenia and activities of daily living may be an effective way to prevent and improve the mental health status of older adults.

This study aims to develop multiple machine learning models for predicting hypothermia risk in laparoscopic surgery and to perform interpretability analysis of the best-performing model. Our goal is to provide robust decision support for clinicians and ensure safe and effective patient care during surgical procedures. This study included 1,030 patients who underwent laparoscopic surgery at Shandong Provincial Cancer Hospital, affiliated with Shandong First Medical University, between July 2023 and June 2024. We employed the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm for feature selection. We explored the performance of five machine learning algorithms—logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost)—to predict hypothermia risk during laparoscopic surgery. Finally, we conducted an interpretability analysis of the top-performing model using Shapley Additive Explanations (SHAP). LASSO identified nine risk factors: BMI, ASA classification, total volume of intravenous fluids, irrigating fluids during the operation, volume of CO2, blood loss, ambient temperature, long-term alcohol consumption, and type of surgery. Performance comparison among the five models revealed that the XGBoost model performed the best, with an accuracy of 0.762 (95% CI: 0.717–0.807) and an area under the curve (AUC) of 0.835 (95% CI: 0.794–0.872). The model achieved a specificity of 0.749 (95% CI: 0.683–0.816) and a sensitivity of 0.773 (95% CI: 0.713–0.824). The F1 score was 0.778 (95% CI: 0.735–0.828). SHAP analysis revealed that the four most influential factors for hypothermia risk during laparoscopic surgery were operating room temperature, rinsing fluids during the operation, volume of CO2, and blood loss. This study developed an efficient and interpretable predictive model for the risk of hypothermia in patients undergoing laparoscopic surgery. This model supports clinical decision-making and contributes to the overall goal of providing personalized care in the surgical environment.