Objective: To explore the interaction between school bullying and insomnia in relation to depression-anxiety-stress emotions among primary and secondary school students,so as to provide a basis for preventing negative emotional states in adolescents. Methods: In October 2024, a stratified cluster sampling method was used to select 3 058 students in grade 5-6 of primary, junior and senior high school in Sheyang County of Jiangsu Province. The Delaware Bullying Victimization Scale, Insomnia Severity Index, Depression-Anxiety-Stress Scale-21, and Study Condition Questionnaire were employed to investigate school bullying, insomnia, depression-anxiety-stress emotions, and academic performance. The χ 2 test and Logistic regression were used to analyze the association between school bullying and insomnia interactions and depression-anxiety-stress emotions among primary and secondary school students, multiplicative interaction analysis was conducted, and additive interaction analysis was performed using R software. Results: The detection rates of depression-anxiety-stress emotions among primary and secondary school students were 21.6%, 28.4% and 10.8%, respectively. The detection rates of physical bullying, relationship bullying, verbal bullying and cyberbullying in school bullying were 10.6%, 14.0%, 22.3%, and 6.2%, respectively. The detection rate for insomnia was 23.1%. Results from Logistic regression analysis showed that, after adjusting for relevant factors, physical, relational, verbal, and cyberbullying and insomnia were positively correlated with the detection rates of depression ( OR = 5.72- 10.93), anxiety ( OR =6.35-12.17), and stress emotions ( OR =5.97-14.52) among primary and secondary school students (all P <0.01). The multiplicative interaction between physical, relational, verbal, and cyberbullying and insomnia was positively correlated with the detection rates of depression ( OR =8.00-18.01), anxiety ( OR =11.35-17.76), and stress emotions ( OR =7.64-9.12) in primary and secondary school students (all P <0.01). Additive interactions were observed between physical, relational, verbal, and cyberbullying and insomnia in relation to the detection rates of depression, anxiety, and stress emotions among primary and secondary school students (both RERI and AP >0 and the credible interval excluded 0, SI >1 and the credible interval excluded 1). Conclusion School bullying and insomnia are associated with depression, anxiety, and stress emotions among primary and secondary school students, and they exhibit both multiplicative and additive interactions.

Objective: To explore the developmental trajectories of depression in children and adolescents and their association with family types, and to analyze the role of being an only child in the context, so as to provide a basis for early identification of mental health issues in children and adolescents. Methods: The study was a secondary analysis based on the existing database of the Jiangsu Provincial Student Common Diseases and Health Influencing Factors Monitoring and Intervention Project. A total of 11 502 students who had completed at least two measurements using the Chinese version of the Center for Epidemiologic Studies Depression Scale (CES-D) between 2022 and 2024, and had complete information on family type, gender, and age, were selected as the study subjects. Latent class trajectory modeling was used to identify depression developmental trajectories. Multinomial Logistic regression was applied to analyze the association between family type and depression developmental trajectories, and the interaction effect of family type and being an only child was tested. Results: Three types of depression developmental trajectories were identified among children and adolescents: low stable type (91.3%, 10 504 students), moderate rising type (4.3%, 500 students), and high declining type (4.3%, 498 students). Significant differences were observed among the different trajectory groups in terms of gender, age, parental education level, family type, baseline CES-D score, and baseline school type ( χ 2/H=17.48, 139.97, 19.72 , 30.77, 1 081.35, 220.81, all P <0.05). Multinomial Logistic regression analysis showed that, using the low stable type as the reference trajectory group and the nuclear family as the reference family type, after adjusting for confounding factors such as gender, age, and parental education level, single parent families ( OR=1.87, 95%CI= 1.16-3.03) and grandparent headed families ( OR=1.83, 95%CI =1.04-3.21) were significantly associated with the high declining type trajectory (both P <0.05). No significant association was found between family type and the moderate rising type trajectory (all P >0.05). The interaction effect between family type and being an only child was not statistically significant ( LRT=7.71, df=8, P =0.46). Conclusions Depressive symptoms in children and adolescents show heterogeneous developmental patterns during school age. Children and adolescents from single parent and intergenerational families are more likely to follow the high decreasing trajectory.

Objective To analyze the differences in structural integrity of cerebral white matter fiber bundles in vegetative state(VS)patients with different prognoses,and to construct an early-stage prognostic prediction model for 1-year post-stabilization prognosis.Methods A retrospective analysis was conducted on 52 VS patients admitted to the Department of Rehabilitation Medicine at Zhujiang Hospital of Southern Medical University.Patients were stratified into good prognosis(n=22)and poor prognosis(n=30)at 1-year follow-up based on Coma Recovery Scale-Revised(CRS-R)scores.The fractional anisotropy values of cerebral white matter fiber bundles were derived from diffusion tensor imaging,and for the first time,the scores of the visual subscales of CRS-R were combined with FA values as input features for the prognostic model.To optimize model construction,the least absolute shrinkage and selection operator regression was employed for feature screening,and synthetic minority over-sampling technique for data balancing.The prognostic prediction model was subsequently developed using a support vector machine algorithm and validated via leave-one-out cross-validation.Model performance was evaluated using area under receiver operating characteristic curve,along with accuracy,sensitivity,specificity,and F1 score metrics.Results Following LASSO regression feature screening,the pontine crossing tract,medial lemniscus,tapetum,splenium of corpus callosum,and visual subscale scores were identified as key predictors.A multimodal SVM-based prediction model constructed with the above features could effectively predict the 1-year prognosis of VS patients,achieving a high predictive performance(AUC=0.894).Conclusion The SVM-based model integrating FA values of specific white matter fiber bundles and visual subscale scores demonstrates excellent predictive performance in predicting the 1-year prognosis of VS patients.

Objective To compare imaging quality and detection capability of bone metastases between Insight NM/CT Pro SPECT/CT(Insight SPECT/CT)and Siemens Symbia T16 SPECT/CT(Symbia T16 SPECT/CT)scanners.Methods Totally 40 patients with diagnosed or suspected bone metastases were prospectively enrolled.Whole-body bone imaging and local tomographic fusion imaging were performed using Symbia T16 and Insight SPECT/CT scanners with same method and parameters,and imaging quality and detection capability were compared between 2 devices.Results Among whole-body bone imaging acquired with Symbia T 16 SPECT/CT,the imaging quality score was 5 in 35 cases and 4 in 5 cases,and detected 118 positive bone lesions,including 36 lesions involved chest,28 involved spinal cord,30 involved pelvic bones,20 involved limbs and 4 involved cranial bones.The imaging quality score of local tomographic fusion imaging obtained with Symbia T16 SPECT/CT was 5 in all 40 cases,and 59 positive lesions involved bone regions were detected,including 14 lesions presented as bone destruction,11 presented as increased bone density and 34 showed uneven bone density on CT.Meanwhile,the whole-body bone imaging quality score acquired with Insight SPECT/CT was 5 in 35 cases,4 in 4 cases and 3 in 1 case,and both the detected positive lesions and the involved bone regions were consistent with those of Symbia T 16 SPECT/CT.Furthermore,the imaging quality,detected positive lesions and their involved regions,as well as CT manifestations on local tomographic fusion imaging obtained with Insight SPECT/CT scanner were all consistent with those of Symbia T 16 scanner.Conclusion The imaging quality of whole-body bone imaging and local tomographic fusion imaging of bone metastases of domestic Insight SPECT/CT were comparable to those of Siemens Symbia T16 SPECT/CT.

Objective This study examines the factors influencing oral frailty in the elderly community,develops a risk prediction model,and validates its efficacy,so as to provide references for identifying and preventing oral weakness in the elderly.Methods 556 elderly individuals from 4 communities were selected by convenience sampling from June to August 2024 in Zigong City Sichuan Province.They were randomly divided into a training group(383 cases)and a validation group(165 cases).Data were collected by a general information questionnaire,Social Frailty Scale,Geriatric Depression Scale,and the Oral Frailty Index-8 screening tool.Logistic regression was used to determine the influencing factors,and R software was used to establish a nomogram model for predicting the risk of oral frailty.Bootstrap method and the validation group were used for internally validation of the model.Calibration curve was used to evaluate the prediction performance of the model.Results 548 valid questionnaires were collected.The final model variables included whether the age ≥80 years,wearing removable dentures,reduced frequency of going out,brushing teeth less than twice a day,frequent dry mouth,increased difficulty in eating hard foods,and choking.The area under the receiver operating characteristic curve of the training group was 0.95(95％CI:0.93～0.97),and the best cutoff value was 0.687.The model achieved an accuracy of 87％,sensitivity of 91％,specificity of 85％,positive predictive value of 0.75,and negative predictive value of 0.95.The Hosmer-Lemeshow fitting test show that x2=3.036,P=0.932,indicating a good model fit.Conclusion The oral frailty prediction model demonstrated a good discrimination,calibration,and clinical utility,which can provide a scientific basis for the prevention and early screening of oral frailty in the elderly.

Objective To explore the correlation between serum hypoxia-inducible factor-1α(HIF-1α)levels and cerebral microbleeds(CMBs)and cognitive impairment and to assess the predictive value of HIF-1α for CSVD-related cognitive impairment.Methods A total of 104 patients with CSVD who attended the Department of Neurology,First Affiliated Hospital of Xinxiang Medical University from June 2022 to November 2023 were enrolled.All enrolled patients were subjected to basic statistics,cranial nuclear magnetic resonance examination,cognitive function assessment,and serum HIF-1α test,and the number and location of CMBs were counted.Based on the above data the enrolled patients were grouped.The correlation between HIF-1α and cognitive function and CMBs was studied the influencing factors of CMBs and cognitive impairment were analyzed,and the predictive value of HIF-1α on the occurrence of cognitive impairment was evaluated.Results There were statistically significant differences in HIF-1α levels and cognitive function among different CMBs groups.Serum HIF-1α levels were significantly negatively correlated with overall cognitive function,visuospatial and executive function,attention,and delayed recall,and serum HIF-1α was positively correlated with the number of CMBs.HIF-1α may be a risk factor for CMBs and cognitive impairment associated with CSVD,and serum HIF-1α has potential in predict the cognitive impairment caused by CSVD.Conclusion Serum levels of HIF-1α were associated with the number of CMB and CSVD-related cognitive impairment,and serum levels of HIF-1α may have a predictive value for CSVD-related cognitive impairment.

Objective To analyze the differences in structural integrity of cerebral white matter fiber bundles in vegetative state(VS)patients with different prognoses,and to construct an early-stage prognostic prediction model for 1-year post-stabilization prognosis.Methods A retrospective analysis was conducted on 52 VS patients admitted to the Department of Rehabilitation Medicine at Zhujiang Hospital of Southern Medical University.Patients were stratified into good prognosis(n=22)and poor prognosis(n=30)at 1-year follow-up based on Coma Recovery Scale-Revised(CRS-R)scores.The fractional anisotropy values of cerebral white matter fiber bundles were derived from diffusion tensor imaging,and for the first time,the scores of the visual subscales of CRS-R were combined with FA values as input features for the prognostic model.To optimize model construction,the least absolute shrinkage and selection operator regression was employed for feature screening,and synthetic minority over-sampling technique for data balancing.The prognostic prediction model was subsequently developed using a support vector machine algorithm and validated via leave-one-out cross-validation.Model performance was evaluated using area under receiver operating characteristic curve,along with accuracy,sensitivity,specificity,and F1 score metrics.Results Following LASSO regression feature screening,the pontine crossing tract,medial lemniscus,tapetum,splenium of corpus callosum,and visual subscale scores were identified as key predictors.A multimodal SVM-based prediction model constructed with the above features could effectively predict the 1-year prognosis of VS patients,achieving a high predictive performance(AUC=0.894).Conclusion The SVM-based model integrating FA values of specific white matter fiber bundles and visual subscale scores demonstrates excellent predictive performance in predicting the 1-year prognosis of VS patients.

Objective:To analyze the association between unhealthy lifestyles and depressive symptoms, anxiety symptoms and stress among secondary school students in Jiangsu Province.Methods:From September to November 2023, a multistage stratified cluster random sampling method was used to select secondary school students from 13 districts and cities in Jiangsu Province. A questionnaire survey was conducted on their unhealthy lifestyles (low physical activity, smoking, drinking, internet addiction, poor sleep quality, and unhealthy diet), as well as their depressive symptoms, anxiety symptoms and stress. The multivariate logistic regression model and mixed graph model were used to construct a network and analyze the association between unhealthy lifestyles and depressive symptoms, anxiety symptoms and stress.Results:A total of 81 414 secondary school students were finally included in this study, including 39 725 (48.79%) female students and 41 689 (51.21%) male students. The prevalence of depressive symptoms, anxiety symptoms and stress were 18.55%, 32.09% and 12.91%, respectively. The multivariate logistic regression model showed that after adjusting for age, gender, urban-rural status, residential status, and family type, compared with students without unhealthy lifestyles, students with low physical activity, smoking, alcohol consumption, internet addiction, poor sleep quality, and unhealthy diet had a significantly increased risk of depressive symptoms ( OR=1.12, 95% CI:1.07-1.17; OR=1.60, 95% CI: 1.49-1.72; OR=1.79, 95% CI: 1.71-1.88; OR=3.05, 95% CI: 2.77-3.36; OR=6.66, 95% CI: 6.40-6.93; OR=1.29, 95% CI: 1.24-1.34) and a significantly increased risk of anxiety symptoms ( OR=1.09, 95% CI: 1.05-1.13; OR=1.42, 95% CI: 1.33-1.52; OR=1.76, 95% CI: 1.69-1.83; OR=2.40, 95% CI: 2.17-2.65; OR=5.79, 95% CI: 5.59-6.00; OR=1.16, 95% CI: 1.12-1.21). Students who smoked, drank alcohol, had internet addiction, and had poor sleep quality had a significantly increased risk of stress ( OR=1.49, 95% CI: 1.38-1.61; OR=1.79, 95% CI: 1.70-1.89; OR=2.25, 95% CI: 2.04-2.48; OR=6.74, 95% CI: 6.43-7.06). The node with poor sleep quality (bridge strength=0.48) in the network constructed by the mixed graph model had the strongest centrality of the bridge connecting unhealthy lifestyles with depressive symptoms, anxiety symptoms and stress. Conclusion:Low physical activity, smoking, alcohol consumption, internet addiction, poor sleep quality, and unhealthy diet increase the risk of depressive symptoms and anxiety symptoms among Jiangsu Province secondary school students. Smoking, alcohol consumption, internet addiction, and poor sleep quality increase the risk of stress among Jiangsu Province secondary school students. Sleep quality is an important intervention target for Jiangsu Province secondary school students to alleviate their negative emotions.

Objective This study examines the factors influencing oral frailty in the elderly community,develops a risk prediction model,and validates its efficacy,so as to provide references for identifying and preventing oral weakness in the elderly.Methods 556 elderly individuals from 4 communities were selected by convenience sampling from June to August 2024 in Zigong City Sichuan Province.They were randomly divided into a training group(383 cases)and a validation group(165 cases).Data were collected by a general information questionnaire,Social Frailty Scale,Geriatric Depression Scale,and the Oral Frailty Index-8 screening tool.Logistic regression was used to determine the influencing factors,and R software was used to establish a nomogram model for predicting the risk of oral frailty.Bootstrap method and the validation group were used for internally validation of the model.Calibration curve was used to evaluate the prediction performance of the model.Results 548 valid questionnaires were collected.The final model variables included whether the age ≥80 years,wearing removable dentures,reduced frequency of going out,brushing teeth less than twice a day,frequent dry mouth,increased difficulty in eating hard foods,and choking.The area under the receiver operating characteristic curve of the training group was 0.95(95％CI:0.93～0.97),and the best cutoff value was 0.687.The model achieved an accuracy of 87％,sensitivity of 91％,specificity of 85％,positive predictive value of 0.75,and negative predictive value of 0.95.The Hosmer-Lemeshow fitting test show that x2=3.036,P=0.932,indicating a good model fit.Conclusion The oral frailty prediction model demonstrated a good discrimination,calibration,and clinical utility,which can provide a scientific basis for the prevention and early screening of oral frailty in the elderly.

Aim To investigate the effect and mechanism of angiotensin Ⅱ(Ang Ⅱ)on ferroptosis in white adi-pocytes.Methods The 3T3-L1 preadipocytes were differentiated into white adipocytes by inducer stimulation.The experiment was divided into control group,Ang Ⅱ group,Ang Ⅱ+Fer-1(ferroptosis inhibitor)group and Ang Ⅱ+PFT-α(p53 inhibitor)group.Ang Ⅱ was used to treat cells.RT-qPCR and Western blot were used to detect the expression levels of ferroptosis factors and adipokines.JC-1 kit was used to detect mitochondrial membrane potential(MMP)level.Iron ion kit was used to detect intracellular iron content.Glutathione(GSH)kit was used to detect GSH content.Fer-1 and Ang Ⅱ were added to treat cells to detect the the changes of ferroptosis level.The expression of p53 and spermidine/spermine N1-acetyltransferase 1(SAT1)protein was detected.Subsequently,PFT-α and Ang Ⅱ were added to co-treat cells to detect the changes of p53 and SAT1 protein expression,and to observe the effect of inhibiting p53 expression on the expression levels of ferroptosis factors and adipokines.Results 3T3-L1 cells were successfully differentiated into white adipocytes by stimulator-induced differentiation.Ang Ⅱ induced ferroptosis in white adipocytes.RT-qPCR results showed that compared with control group,the mRNA expression of anti-ferroptosis factor glutathione peroxidase 4(GPX4),solute carrier family 7 member 11(SLC7A11)and iron regulatory protein 1(IRP-1)was down-regulated in Ang Ⅱ group,and the mRNA expression of pro-ferroptosis factor acyl-CoA synthetase of long-chain family member 4(ACSL4)was up-regulated.Western blot results showed that compared with control group,the protein expression of SLC7A11 and GPX4 was down-regulated in Ang Ⅱ group,and the protein expression of ACSL4 was up-regulated.Ang Ⅱ treatment increased the content of intracellular iron ions and decreased the levels of GSH and MMP.Compared with Ang Ⅱ group,the mRNA expression of IRP-1 and SLC7A11 was up-regulated in Ang Ⅱ+Fer-1 group.Ang Ⅱ induced changes in the expression profile of adipokines in white adipocytes.Western blot results showed that compared with control group,the protein ex-pression of pro-inflammatory adipokine leptin(LEP),resistin(RETN),interleukin-6(IL-6)and tumor necrosis factor-α(TNF-α)was up-regulated in Ang Ⅱ group,and the protein expression of anti-inflammatory adipokine adiponectin(AD-PN)and omentin 1(ITLN1)was down-regulated.In addition,Ang Ⅱ increased the protein expression of p53 and SAT1.Inhibition of p53 expression can improve the level of ferroptosis and adipokine expression in white adipocytes trea-ted with Ang Ⅱ.Western blot results showed that compared with Ang Ⅱ group,the protein expression of p53 and SAT1 was down-regulated in Ang Ⅱ+PFT-α group,the protein expression of SLC7A11 and GPX4 was up-regulated,and the protein expression of ACSL4 was down-regulated.The protein expression of ADPN was up-regulated in Ang Ⅱ+PFT-αgroup,and the protein expression of TNF-α,LEP and RETN was down-regulated.Conclusion Ang Ⅱ induces fer-roptosis in white adipocytes through activating the p53/SAT1 signaling pathway.