Objective: To explore the prevalence and influencing factors of depressive symptoms among university students in some universities in Shandong Province, so as to provide a theoretical reference for implementing corresponding intervention measures. Methods: A cluster sampling method was used to select 8 079 university students studying in universities in Shandong Province from March 2023 to May 2024 as the research subjects. They were randomly divided into a training set (6 463) and a validation set (1 616) according to the 8/2 ratio. The influencing factors of depression among university students were analyzed, and a risk prediction model for depression among university students was constructed and validated. Results: In the training set of university students, the detection rate of depression was 35.09%(2 268/6 463), with 1 632 cases (71.96%) of mild depression, 545 cases (24.03%) of moderate depression, and 91 cases (4.01%) of severe depression. In the validation set of university students, the detection rate of depression was 33.97% (549/1 616), with 384 cases (69.95%) of mild depression, 127 cases (23.13%) of moderate depression, and 38 cases (6.92%) of severe depression. In the training set, the proportions of those who surfed the Internet for more than 3 h/d, occasionally or did not participate in physical exercise, had average or poor relationships with classmates, often drank sugary drinks, occasionally or did not have breakfast, had unsatisfactory academic performance, had an average monthly living expense of less than 1 500 yuan on campus, and had divorced or widowed parents in the depression-detected group were all higher than those in the undetected group( χ 2=1 193.85,1 584.41, 1 115.10 ,826.00,1 424.05,924.58,803.68,797.65, P <0.05). The scores of the Chinese version of the Childhood Trauma Questionnaire (CTQ-SF) in the depression detected group were also higher than those in the undetected group( t =98.48, P <0.05). The results of Logistic regression analysis showed that physical exercise, classmate relationships, academic performance, average monthly living expenses on campus, and CTQ-SF scores were influencing factors for depression among university students( OR =3.87, 4.82, 3.63, 3.75, 4.39, P <0.05). The sensitivity of the model in the training set for predicting depression among university students was 89.0%(95% CI =87.6%-90.2%), the specificity was 93.0% (95% CI =92.2%-93.7%), and the area under the curve was 0.9(95% CI =0.8-1.0); the sensitivity of the model in the validation set for predicting depression among university students was 87.6%(95% CI =84.5%-90.1%), the specificity was 91.3%(95% CI =89.4%-92.9%), and the area under the curve was 0.9(95% CI =0.8-1.0). Conclusions The high detection rate of depressive symptoms among university students in some universities in Shandong Province warrants attention. Constructing a risk prediction model is helpful for early identifying the risk of depression among university students.

Objective: To explore the application effect of a guided self help intervention based on dialectical behavior therapy (DBT) to address non suicidal self injury (NSSI) behavior among college students, so as to provide a reference for reducing the occurrence of NSSI behavior in this population. Methods: A total of 106 college students with NSSI admitted to the Department of Child and Adolescent Psychology, the Second Affiliated Hospital of Jining Medical University from January 2021 to January 2023 were selected and divided into an observation group and a control group, with 53 cases in each group, using a random number table method. The control group received routine medication treatment and psychological intervention, while the observation group, on the basis of the interventions provided to the control group, implemented dialectical behavior therapy based guided self help. The approach combines group activities, individual counseling, and selfdirected learning, covering four core modules: mindfulness training, distress tolerance, interpersonal effectiveness, and emotion regulation. Meanwhile, data collection, skill check ins, and personalized recommendation pushes were conducted through a WeChat. Both groups were intervented for 12 weeks. Before the intervention and after the intervention, the Adolescent Non suicidal Self injury Behavior Questionnaire, Ottawa Self injury Inventory (OSI), and Barratt Impulsiveness Scale (BIS) were used to evaluate the patients, and the levels of serum neurotransmitters were detected. The χ 2 test, t test, and Cochran s Q test were used for data comparison and analysis. Results: The incidence rates of NSSI in the observation group after 3, 6, and 12 weeks of intervention were 47.17%, 16.98%, and 5.66%, respectively, all lower than those in the control group (67.92%, 35.85%, 20.75%) ( χ 2=4.67, 4.85, 5.27,all P <0.05). After 12 weeks of intervention, in the NSSI Behavior Questionnaire, the total score of the observation group was (17.94±2.69) points, which was lower than that of the control group (23.04±5.11) points; in the Function Questionnaire, the total score of the observation group was (53.24±8.94) points, which was higher than that of the control group (47.74±8.00) points（both P ＜0.05）. In terms of the OSI, the total score of the observation group was (4.49±0.62) points, lower than that of the control group (6.25±0.81) points;in the BIS, the total score of the observation group was (80.76±7.94) points, lower than that of the control group (87.74±9.34) points，and the differences between groups were statistically significant(both P<0.05). After the intervention, the level of 5-hydroxytryptamine in the observation group was (67.93±5.42) ng/mL, higher than (44.72±5.54) ng/mL of the control group; the levels of substance P and cortisol in the observation group were (35.82±4.47) ng/L and (75.64±8.02) μg/L, respectively, both lower than (48.14±5.32) ng/L and (94.53±10.78) μg/L of the control group, and the differences were statistically significant (all P <0.05). Conclusion The guided self help intervention based on DBT is helpful for reducing NSSI behavior among college students.

Stroke is the leading cause of death and disability. Therefore， it is critical to develop the approaches for improving recovery and neural repair after stroke. Recovery after stroke involves complex interrelated systems of neural repair. The whole process of neural repair requires a series of coordinated interactions， such as response of neuronal cell body to traumatic stimuli， neural stem cell proliferation and migration， axoplasmic transport of signaling molecules， construction of cytoskeleton， and formation of axonal growth cone， to achieve regeneration and growth. As a potential new target for the treatment of neurologic defects， neural remodeling has important research significance in both the specific mechanism of neural repair and the clinical treatment of neurologic defects. After brain injury， single therapy is often ineffective due to the complex mechanism of internal repair， and thus comprehensive therapy becomes the development direction to improve the brain repair. Invigorating qi and promoting blood can promote nerve function remodeling after injury through neural protection， angiogenesis， neurogenesis， loop reconstruction， and cytokine regulation， playing a key role in nerve repair after stroke. Its mechanism is associated with autophagy， immunomodulation， and microRNA regulation， which fully embodies the multi-pathway， multi-target， and overall regulation characteristics of invigorating Qi and activating blood. Therefore， it is of theoretical and guiding significance to study the brain function rehabilitation after stroke by invigorating qi and activating blood.