Objective: To explore the dynamic developmental trajectories of college students class belongingness during their college years and its longitudinal predictive effects on depressive symptoms, so as to provide targeted insights for precise campus psychological interventions. Methods: In October 2021 (T1), a total of 4 720 college students from a university in Shandong Province were selected by cluster sampling method and followed up for 3 years. Surveys were conducted annually (T2: October 2022, T3: October 2023, T4: October 2024). The Class Belongingness Scale and Patient Health Questionnaire-9 (PHQ-9) were used to assess students class belongingness and depressive symptoms. Latent growth mixture modeling was employed to identify trajectories of class belonging, and multinomial Logistic regression analysis was used to examine the predictive effects of these trajectory classes on depressive symptoms. Results: Mean scores of class belongingness across T1-T4 were (73.24±11.95, 74.76±12.25, 75.25±12.38, 77.64±11.63), and the scores of depressive symptoms were [1.00 (0, 5.00), 0 (0, 3.00), 0 (0, 2.00), 0 (0, 2.00)]. The developmental trajectories of class belongingness were categorized into three types: the high-starting ascending group ( 56.61 %), the low-starting descending group (11.91%), and the medium-starting stable group (31.48%). Multinomial Logistic regression analysis showed that, compared to the medium-starting stable group, the high-starting ascending group had a lower probability of developing mild depressive symptoms ( OR=0.27, 95%CI =0.15-0.47) and moderate or above depressive symptoms ( OR=0.29, 95% CI = 0.14-0.60) (both P <0.05). Conversely, the low-starting descending group had a higher probability of developing mild depressive symptoms ( OR=2.31, 95%CI =1.65-3.22) and moderate or above depressive symptoms ( OR=7.49, 95%CI = 3.82-14.69) (both P <0.05). Conclusion Declining trajectory of class belongingness is a risk factor for depressive symptoms, while sustained upward trend may mitigate such risks.

Objective: To analyze the current status and hotspots of the construction of healthy cities, so as to provide the reference for promoting healthy city construction. Methods: Retrieve relevant articles on the construction of healthy cities from CNKI and Web of Science databases until December 2024. Count the number of publications, countries/regions of publication, and research institutions. Keyword co-occurrence maps were drawn by using CiteSpace 6.3.R1 software, and combined cluster analysis to summarize the main research hotspot themes. Results: A total of 1 120 articles were retrieved. Among them, there were 670 English articles, with the first one published in 2006, and the peak number (109 articles) of publications was reached in 2021. There were 426 Chinese articles, with the first one published in 1996, and the peak number (62 articles) of publications was reached in 2020. The overall number of publications showed an upward trend. Among the English articles, China, the United States, and the United Kingdom ranked the top three in terms of the number of publications, with 171, 97, and 80 articles, respectively. Wuhan University, the University of Hong Kong, and Peking University were the top three core institutions, with 18, 17, and 14 articles, respectively. High-frequency keywords included "healthy cities", "physical activity", "healthy city", "city", "built environment", "health", and "public health". The main research hotspot themes encompassed four aspects: mental health and the built environment, public health policies, climate change and urban sustainability, and the multidimensional complex relationships of health determinants. Among the Chinese articles, China had the highest number of publications, with 422 articles (99.06%). Tongji University, Tsinghua University, and Nanjing University were the top three core institutions, with 30, 11, and 8 articles, respectively. High-frequency keywords were "healthy cities", "urban planning", "public health", "built environment", "indicator system", and "influencing factors". The main research hotspot themes included three aspects: urban planning and spatial layout, the comprehensive practice and policy framework of healthy city construction, and the interaction between health activities and the built environment. Conclusions The number of publications on the construction of healthy cities has increased, and both Chinese and English articles focus on public health and the built environment. Construction of healthy cities has interdisciplinary characteristics.

Background Currently,the diagnosis of depressive disorder relies on symptomatology,which is greatly influenced by subjective factors such as clinicians' experience.Finding more accurate and reliable quantitative diagnostic methods is currently an urgent problem.Objective To explore the value of Fisher discriminant function based on inflammatory cytokines in the diagnosis of depressive disorder,so as to provide references for clinical diagnosis.Methods A total of 136 patients diagnosed with depressive disorder according to the Diagnostic and Statistical Manual of Mental Disorders,fifth edition(DSM-5)criteria,who underwent inpatient treatment at Hebei Mental Health Center from April 2020 to November 2020,were enrolled as study group.67 healthy participants matched for age and gender,was recruited during the same period.Serum levels of inflammatory cytokine were measured using enzyme-linked immunosorbent assay(ELISA).Fisher discriminant model was employed to establish a discriminant function for inflammatory cytokines exhibiting significant statistical differences between study group and control group,which was then validated.Results The levels of pro-inflammatory cytokines interleukin-1β(IL-1β),interleukin-6(IL-6),interferon-γ(INF-γ)and tumor necrosis factor-α(TNF-α)were higher in the study group compared with control group,with statistically significant differences(U=9.115,5.239,4.431,5.428,P<0.01).Conversely,the levels of anti-inflammatory cytokines interleukin-4(IL-4),interleukin-10(IL-10)and interleukin-13(IL-13)were lower in the study group compared with control group,with statistically significant differences(U=7.398,7.331,7.614,P<0.01).The retrospective test in Fisher discriminant function achieved a correct discrimination rate of 89.66%,and the cross validation achieved a correct discrimination rate of 88.67%.Conclusion The Fisher discriminant function developed in this study may serve as a valuable auxiliary method in the diagnosis of depressive disorder.

Objective:To explore the relationship between metabolic score for insulin resistance (METS-IR) and chronic kidney disease (CKD) and albuminuria in the Chinese population.Methods:This cross-sectional study was conducted from January to December 2018 among residents aged 20 to 70 years in ten regions of eight provinces in China; all residents had lived in their region for more than 5 years. Various parameters were measured, included fasting blood glucose, 2-hour postprandial blood glucose, glycosylated hemoglobin (HbA 1c), blood lipids, renal function, urinary albumin/creatinine ratio (UACR), etc. Data of 5 060 subjects meeting the criteria were included in the study. CKD was defined as estimated glomerular filtration rate (eGFR)<60 ml·min -1·1.73 m -2 or UACR≥30 mg/g. Albuminuria was defined as UACR≥30 mg/g. METS-IR was calculated and categorized into quartiles: Q1, METS-IR≤32.19; Q2, METS-IR 32.20-37.10; Q3, METS-IR 37.11-42.58; and Q4, METS-IR>42.58. The correlation between METS-IR and CKD and albuminuria was analyzed by binary logistic regression, and subgroup analyses were performed. Results:There were 1 266, 1 266, 1 265, and 1 263 participants included in Q1-Q4 groups, respectively. With the increase of METS-IR quartile, various parameters increased, including age, fasting blood glucose, HbA 1c, triglycerides, serum uric acid, waist circumference, body mass index, and systolic and diastolic blood pressure, and the proportion of males also increased (all P<0.05). The proportion of patients with CKD and albuminuria increased significantly with the increase in interquartile range (Q) of METS-IR (all P<0.05). Logistic regression analysis showed that for every 1-unit increment of METS-IR, the risk of CKD and albuminuria were both increased by 2% [for both: odds ratio ( OR)=1.02, 95% confidence interval ( CI) 1.01-1.03]. Compared with the lowest METS-IR group (Q1), the ORs for CKD and albuminuria in the highest METS-IR group (Q4) were 1.57 (95% CI 1.17-2.10) and 1.46 (95% CI 1.09-1.96), respectively. In the subgroup analyses, increased METS-IR was significantly associated with CKD and albuminuria among women (CKD: OR=1.62, 95% CI 1.14-2.31; albuminuria: OR=1.53, 95% CI 1.07-2.18), individuals with HbA 1c<7% ( OR=1.64, 95% CI 1.21-2.23; OR=1.55, 95% CI 1.14-2.11), individuals with eGFR≥90 ml·min -1·1.73 m -2 ( OR=1.78, 95% CI 1.27-2.49; OR=1.80, 95% CI 1.28-2.53), and the Chinese Han population ( OR=1.56, 95% CI 1.13-2.17; OR=1.41, 95% CI 1.01-1.96). Conclusions:METS-IR is significantly associated with CKD and albuminuria in a Chinese population. Furthermore, the higher the METS-IR, the higher the risk of CKD and albuminuria.

Objective:To predict the clinical value of World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading of clear cell renal cell carcinoma (ccRCC) pre-scholarly based on ultrasound imaging group.Methods:Clinical and ultrasound imaging data of patients with surgically pathologically confirmed ccRCC at Tianjin Medical University Cancer Institue and Hospital from January 2021 to October 2022 were retrospectively collected and divided into a low grade group (grade Ⅰ and Ⅱ, 105 cases) and a high grade group (grade Ⅲ and Ⅳ, 70 cases) using WHO/ISUP pathological grading criteria. The clear image of the largest diameter of the tumor was selected and imported into ITK-SNAP software for manual segmentation of the image and extraction of ultrasonographic radiomics features. The patients were randomly divided into a training group and a test group in the ratio of 7∶3, with 122 cases in the training group and 53 cases in the test group. Stable radiomics features were obtained by dimensionality reduction. The support vector machines (SVM) algorithm was applied to predict the pathological grading of ccRCC. Finally, a clinical-ultrasound imaging model, an ultrasonographic radiomics model and a comprehensive model combining the two were constructed. The predictive effects of the three models were analyzed by the area under the ROC curve (AUC). The performance of each model was evaluated by applying the calibration curve. The net benefit of patients was obtained by applying the decision curve.Results:A total of 873 radiomics features were extracted, and 10 features were finally obtained for model construction after dimensionality reduction. Final test results showed that the AUC, sensitivity, specificity and accuracy of the clinical-ultrasound imaging model were 0.68, 0.47, 0.78, 0.66. The AUC, sensitivity, specificity and accuracy of the ultrasonographic radiomics model were 0.74, 0.53, 0.88, 0.74. The AUC, sensitivity, specificity and accuracy of the comprehensive model were 0.84, 0.63, 0.86, 0.77. The AUC of the comprehensive model being larger than that of the clinical-ultrasound imaging model ( Z=-3.224, P=0.001) and ultrasonographic radiomics model ( Z=-2.594, P=0.009). The calibration curves showed that the comprehensive model was more stable than the other two models. The decision curve showed a higher net clinical benefit for the comprehensive model than for the other two models within a threshold of 0.1-1.0. Conclusions:The preoperative prediction of ccRCC pathological grading by the radiomics model based on ultrasound images is effective. The comprehensive model constructed by combining relevant clinical and ultrasound parameters has better performance, which can help predict ccRCC pathological grading preoperatively to a certain extent. It is crucial to help physicians choose the best management plan in the era of personalized medicine.

Objective:To investigate the prediction of National Institute of Healthy (NIH) risk stratification of gastrointestinal stromal tumor(GIST) based on clinical ultrasound model, ultrasonographic radiomics model and combined model by oral contrast enhanced ultrasonography.Methods:The clinical and ultrasound imaging data of 204 gastric GIST patients attending Tianjin Medical University Cancer Institute and Hospital from June 2021 to June 2022 were retrospectively analyzed, among whom a total of 101 patients with high and moderate NIH risk stratification GIST confirmed by postoperative pathology were included in the high risk group, and a total of 103 patients with low and extremely low NIH risk stratification GIST were in the low risk group. The ultrasound images of the largest diameter of the GIST were manually segmented by ITK-SNAP software, and Pyradiomics (v3.0.1) module in Python 3.8.7 was applied to extract ultrasonographic radiomics features from the ROI segmented images. The patients were randomly divided into training and validation sets in the ratio of 7∶3. The XGBoost of Sklearn module was applied to construct the clinical ultrasound imaging model, ultrasonographic radiomics model, and combined model. Then the area under ROC curve (AUC), sensitivity, specificity, and accuracy were evaluated; the predictive ability of the three models was compared by Delong test. Calibration Curve was applied to evaluate the model performance, and the clinical Decision Curve Analysis was applied to determine the net benefit to patients.Results:A total of 578 ultrasonographic radiomics features were extracted from ROI, and 8 ultrasonographic radiomics features were finally retained for modeling after regression and dimensionality reduction. Finally, test results showed that AUC, sensitivity, specificity and accuracy of clinical ultrasound imaging model, ultrasonographic radiomics model and combined model were 0.75, 69.3%, 68.9%, 69.1%; 0.87, 79.2%, 81.6%, 80.4%; 0.91, 80.2%, 83.5%, 81.9%, respectively. Delong test showed that the difference of AUC between ultrasonographic radiomics model and clinical ultrasound imaging model was statistically significant ( Z=2.698, P<0.001), and the combined model was significantly better than clinical ultrasound imaging model ( Z=4.062, P<0.001) and ultrasonographic radiomics model ( Z=2.225, P=0.026). Calibration Curve showed the high performance of combined model, and Decision Curve Analysis showed the superior clinical usefulness of combined model. Conclusions:It is feasible to construct an ultrasonographic radiomics model for GIST NIH risk stratification based on oral contrast enhanced ultrasonography images, and the combined model has more advantageous diagnostic performance, which can identify high risk NIH GIST objectively and stably for clinical purposes.

Objective:To explore the predictive value of ultrasound-based radiomics for liver metastasis in pancreatic neuroendocrine tumors (pNEN).Methods:A retrospective analysis was conducted on clinical, pathological, and ultrasound data of 269 pNEN patients confirmed by pathology at Tianjin Medical University Cancer Institute and Hospital from January 2012 to June 2022, including 94 patients with liver metastasis and 175 without liver metastasis. The regions of interest (ROI) were delineated on the maximum diameter section of the tumor using ITKSNAP software, and radiomics features were extracted using Pyradiomics. Radiomics features with an intra-group correlation coefficient greater than 0.90 were retained, and the optimal features were selected using the maximum relevance minimum redundancy (MRMR) algorithm. The dataset was randomly divided into a training set and a validation set in a ratio of 7∶3, and the random forest algorithm (Rfs) was used to predict pNEN liver metastasis. Three models were constructed, including the clinical ultrasound model, the radiomics model, and the comprehensive model that combined clinical ultrasound and radiomics features. The predictive performance of different models for pNEN liver metastasis was analyzed using the ROC curve, and the predictive performance of different models was compared using the Delong test.Results:A total of 874 features were extracted from the ROI, and 12 highly robust radiomics features were retained for model construction based on inter- and intra-observer correlation grading and feature selection. The area under curve(AUC), sensitivity, specificity, and accuracy of the radiomics model, the clinical ultrasound model, and the comprehensive model for predicting liver metastasis in pNEN patients were 0.800, 0.574, 0.789, 0.714; 0.780, 0.596, 0.874, 0.777; and 0.890, 0.694, 0.874, 0.810, respectively. The Delong test showed that the comprehensive model had the best predictive performance, with an AUC superior to that of radiomics model ( Z=3.845, P=0.000 12) and clinical ultrasound model ( Z=3.506, P=0.000 45). Conclusions:The radiomics model based on ultrasound has good performance in predicting liver metastasis in pNEN, and the comprehensive model that combines clinical ultrasound and radiomics features can further improve the predictive performance of the model.

Financial toxicity has a significant impact on the material, psychological, health, and behavior of colorectal cancer patients, leading to poor prognosis and even death. This article reviews the generation, current status, evaluation tools, influencing factors, and response strategies of financial toxicity in colorectal cancer patients, in order to provide reference for domestic research on financial toxicity.

Objective:To investigate the application value of paradoxical intention therapy in patients undergoing PCI.Methods:A total of 116 patients who undergoing PCI in Fuwai Center China Cardiovascular Hospital from January 2018 to December 2019 were enrolled in the present study. They were divided into experimental group and control group with 58 cases in each group. The control group received routine therapy and nursing care, while the experimental group carried out paradoxical intention therapy based on the control group. The effects were evaluated by physiological stress index (blood pressure, heart rate), Symptom Checklist-90 (SCL-90) and General Comfort Questionnaire (GCQ) respectively.Results:At 1 day before surgery, once on the operative day and 1 day after surgery, the systolic pressure were (129.58 ± 13.89), (132.08 ± 12.28), (125.58 ± 12.28) mmHg(1 mmHg=0.133 kPa) in the experimental group, significantly lower than those in the control group (137.22 ± 11.18), (138.35 ± 14.94), (132.35 ± 14.89) mmHg, the differences were statistically significant ( t=3.14, 2.38, 2.59, all P<0.05). At 1 day before surgery and once on the operative day, the heart rate were (74.17 ± 4.18), (76.39 ± 5.14) times/minutes in the experimental group, significantly lower than those in the control group (76.62 ± 5.19), (78.78 ± 6.22) times/minutes, the differences were statistically significant ( t=2.69, 2.17, both P<0.05). After 4 weeks of intervention, the scores of somatization, compulsion, anxiety, depression and total SCL-90 were (1.25 ± 0.19), (1.42 ± 0.23), (1.19 ± 0.28), (1.20 ± 0.16), (121.66 ± 9.66) points in the experimental group, significantly lower than those in the control group (1.84 ± 0.25), (1.68 ± 0.37), (1.84 ± 0.39), (1.73 ± 0.33), (146.47 ± 8.64) points, the differences were statistically significant ( t values were 4.45-14.08, all P<0.05). The scores of physiological dimension, psychological dimension and total GCQ were (10.89 ± 2.22), (24.49 ± 4.92), (77.91 ± 6.20) points in the experimental group, significantly higher than those in the control group (8.51 ± 2.27), (21.84 ± 4.61), (72.09 ± 6.66) points, the differences were statistically significant ( t=5.50, 2.89, 4.69, all P<0.05). Conclusions:Paradoxical intention therapy can effectively alleviate physiological and psychological stress and decrease postoperative discomfort of patients undergoing PCI.