Background: This study aimed to investigate the impact of self-consciousness on depression of college students, and mainly focus on confirming the mediator role of life meaningful and self-efficacy, as well as the moderator role of social support. Methods: In the present study, convenient sampling method was adopted, 583 college students were recruited from Harbin city and Wenzhou city in China. All students were assessed using self-assessment scales, including self-consciousness scale, life meaningful scale, self-efficacy scale, social support scale, and self-rating depression scale. Descriptive statistical analysis and correlation analysis, structural equation model analysis were conducted by SPSS 25.0 and M-plus. Results: Results showed that self-consciousness was negatively related to depression, life meaningful and self-efficacy partially mediated the relation between self-consciousness and depression. Moderated mediation analysis further indicated that the relation between self-efficacy and depression were moderated bu social support. Compare with college students who had high social support, depression in those with low social support was more susceptible to the effect of self-efficacy. Conclusion: These findings imply that college students with low levels of self-consciousness are more easy to be depressive, enhancing their sense of life meaning and self-efficacy can effectively alleviate depression, and college student with high social support can benefit more from self-efficacy. Therefore we should pay more attention to the mental health problems of low levels self-consciousness college students in university.

Objective: Here, we investigated the relationship between social avoidance and depression in college students, explored the mediating roles of loneliness and trust, and the regulatory role of self-esteem, to provide a theoretical intervention approach based on internal mechanisms.Methods: Using a simple random overall sampling method, 1,021 college students were investigated using self-rating depression, social avoidance and distress, loneliness, interpersonal trust and self-esteem scales.Results: There was a significant positive correlation between social avoidance and depression. Loneliness and interpersonal trust played chain-mediating roles between social avoidance and depression. The influence of social avoidance on interpersonal trust was regulated by self-esteem. Specifically, the social avoidance level of the low self-esteem group was more likely to be affected by interpersonal trust issues.Conclusion: Social avoidance not only directly affects college students’ depression, it also has indirect effects through interpersonal trust and loneliness. Thus, interpersonal trust and loneliness have chain-mediating effects between social avoidance and depression in college students, and self-esteem regulates the mediation process.

Many people peel fruits, commonly persimmon, grape, apple, and peach, before eating as table fruits. Differences of bioactive compounds between peels and pulps of daily fruits are widely known but limited to individual compound because understanding of differences in their global metabolites is lack. We employed ¹H NMR-based metabolomics to explore the global metabolite differences between their peels and pulps from the fruits, which included changes of diverse metabolites in persimmon after harvest ripening. Of diverse metabolites observed among the fruits tested, various health-beneficial metabolites were present in the peels rather than the pulps and their classes were dependent on the type of fruit: gallocatechin, epicatechin and epigallocatehin only in persimmon, apple, and peach, respectively; quercetin only in persimmon and apple; kaempferol only in persimmon; chlorogenic acid only in grape and peach; neochlorogenic acid only in apple and peach; p-coumaric acid only in grape; phloridzin and catechin only in apple. These metabolites in the peels of each fruits were strongly correlated with free radical-scavenging activity and delay of carbohydrate digestion. Therefore, intake of whole fruits, rather than removal of their peels, were recommended for potential improvement of healthy lifespan and human wellness. This study highlights the critical role of metabolomic studies in simultaneous determinations of diverse and intrinsic metabolites in different types of fruits and thus providing a strategy for healthy eating habits of daily fruits.

Accurately distinguishing benign from malignant adrenal lesions remains a clinical challenge, especially in oncology patients with indeterminate imaging findings. This study aimed to develop and interpret machine learning (ML) models for classifying adrenal lesions based on 18 F-FDG PET/CT imaging and clinical parameters. A retrospective cohort of 255 patients undergoing 18 F-FDG PET/CT was analyzed. Imaging features—including adrenal SUVmax, SUVpeak, tumor diameter, CT attenuation, and tumor-to-liver SUVmax ratio (T/L SUVmax)—along with clinical variables were extracted. Two classification tasks were constructed: (1) differentiation of benign and malignant adrenal lesions; and (2) subtyping of malignant lesions into lung cancer metastases or lymphoma. Seven ML models were trained and evaluated using 10-fold cross-validation. SHAP (SHapley Additive exPlanations) analysis was applied to elucidate feature contributions. For the benign/malignant classification, ensemble models (Random Forest, Bagging, XGBoost) achieved outstanding performance (AUC > 0.99), with Bagging yielding 100% recall. T/L SUVmax, adrenal SUVmax, and CT attenuation emerged as top predictors. In malignancy subtyping, the artificial neural network (ANN) attained the highest AUC (0.887) and F1-score (0.851). SHAP analysis highlighted distinct metabolic patterns, with lymphoma showing higher SUVmax and T/L ratios, and lung metastases associated with higher CT values. Machine learning models based on PET/CT-derived features enable highly accurate and interpretable classification of adrenal lesions. Integrating metabolic and anatomical parameters improves diagnostic precision, while SHAP analysis offers clinical transparency, supporting personalized decision-making in adrenal lesion management.

Background: Patients with differentiated thyroid cancer (DTC) may have occult lung metastases before ¹³¹ iodine ( ¹³¹ I) treatment. Identifying occult lung metastases before ¹³¹ I treatment is of great clinical value for the correct staging of patients and the establishment of ¹³¹ I treatment plans. Our research is of great significance in establishing statistical models for clinical data using machine learning algorithms to study the prediction of lung metastasis before ¹³¹ I treatment. Methods: Patients were selected from Zhejiang cancer hospital and data was from two groups of DTC patients treated with ¹³¹ I, where the experimental group consisted of 55 patients who showed no lung metastases on CT but tested positive on ¹³¹ I-whole body scan ( ¹³¹ I-WBS). The control group included 316 patients who tested negative for metastases across CT, ultrasound, and ¹³¹ I-WBS. Six machine learning algorithms such as Support Vector Machines (SVM), Decision Trees (DT), Random Forests (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN) were employed to predict models and AUC, sensitivity, accuracy, precision, specificity, F1 Score were used to compare the performance between each models. Finally, the SHAP algorithm was used to explain the importance rank of the features. Results: A total of 371 thyroid cancer patients were included in this study, 55 patients with occult lung metastasis and 316 patients in the control group. The data is divided into a training set and a testing set in a 7:3 ratio. Eleven acceptable variables analyzed including gender, age, T stage, N stage, tumor size, degree of invasion, number of lymph node metastases count, Thyroid Stimulating Hormone (TSH), thyroglobulin (Tg), Thyroglobulin antibodies (Tgab), and administrated activity were screened out by multivariate Cox regression. Evaluation indicators of the best model- LR were as following: accuracy (0.91), recall rate (0.64), precision (0.92), F1-s core (0.70), Area Under Curve (AUC) value (0.93), and the Specificity score (0.96). Conclusion: The logistic model (LR) showed the best performance in predicting occult lung metastases of thyroid cancer patients before ¹³¹ I-WBS. Lymph nodes metastases and throglobulin have the most significant impact on the prediction.