BackgroundThe middle school stage represents a crucial period for the development of borderline personality features. Negative parenting styles and emotional regulation strategies are associated with the formation of borderline personality features. However， the moderating role of emotional regulation strategies between negative parenting styles and borderline personality features among middle school students remains unclear. ObjectiveTo explore the moderating influence of emotional regulation strategies in the relationship between negative parenting styles and borderline personality features among middle school students， and to provide references for the intervention of borderline personality features. MethodsIn October 2023， a total of 5 965 middle school students from three middle schools in Nanning， Guangxi Zhuang Autonomous Region were selected by cluster sampling， and assessed by the Borderline Personality Features Scale for Children （BPFS-C）， the Egna Minnen Barndoms Uppfostran （EMBU）， and the Emotion Regulation Questionnaire-Chinese Revised Version （ERQ-CRV）. Pearson correlation analysis was used to test the correlation between the scores of each scale， and the model 1 of the Process macro program was used to conduct the moderating effect test. ResultsA total of 5 572 middle school students （93.41%） completed this study， and 1 388 of them （24.91%） were identified as having high borderline personality features. The BPFS-C score of middle school students was positively correlated with the score of the negative parenting style dimension of EMBU （r=0.367， P<0.01）， negatively correlated with the score of the cognitive reappraisal dimension of ERQ-CRV （r=-0.168， P<0.01）， and positively correlated with the score of the expression inhibition dimension of ERQ-CRV （r=0.344， P<0.01）. Cognitive reappraisal played a negative moderating effect between negative parenting styles and borderline personality features （β=-0.072， 95% CI： -0.104–-0.041， P<0.01）， while expressive suppression played a positive moderating effect （β=0.076， 95% CI： 0.055–0.097， P<0.01）. ConclusionCognitive reappraisal strategy may help mitigate the negative influence of negative parenting styles on middle school students' borderline personality features， while expressive suppression may exacerbate the harm of negative parenting styles to the borderline personality features of middle school students.

Objective: To develop and validate a clinically actionable prediction model for postoperative pulmonary complications (PPCs) in cardiac surgery patients, focusing on modifiable preoperative risk factors amenable to targeted optimization. Methods: In this prospective observational cohort study, 492 adults undergoing open-chest cardiac surgery between August 15, 2023 and December 31, 2023 were analyzed. Prespecified predictors included gas exchange variables, pulmonary function, inspiratory muscle strength, and physical performance. Univariable and multivariable logistic regression analyses were used to develop the prediction model. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Results: A total of 90 patients (14.1%) developed PPCs after surgery. Five independent predictors were identified: elevated arterial PaCO2 (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.00–1.26), oxygen desaturation (SpO2<93%) (OR 12.47, 95% CI 3.51–48.13), reduced gait speed (OR 0.17, 95% CI 0.04–0.71), lower FEV1/FVC ratio (OR 0.96, 95% CI 0.92–1.00), and diminished inspiratory muscle strength (MIP % predicted) (OR 0.96, 95% CI 0.92–0.99). The model demonstrated good discriminative ability with an AUC of 0.86 (95% CI 0.80–0.93) in the training cohort and 0.87 (95% CI 0.74–0.93) in the validation cohort. Conclusion This parsimonious model achieved high predictive accuracy using five modifiable physiological variables. By targeting abnormalities in gas exchange, pulmonary mechanics, muscle strength, and functional reserve, the model offers a practical tool to guide individualized prehabilitation strategies for reducing PPC risk in cardiac surgery patients.

Objective To develop a predictive model for assessment of the risk of the patients on prolonged mechanical ventilation after coronary artery bypass grafting with extracorporeal circulation.Methods A convenience sampling method was employed to select 2 334 patients who received the coronary artery bypass grafting(CABG)with extracorporeal circulation in our hospital from January 2021 to December 2023 as the study subjects.Preoperative,intraoperative and postoperative data were collected through structured queries from the electronic medical record system of hospital.The study subjects were randomly divided into a training set(n=1 633)and a validation set(n=701)following a 3:1 ratio.A risk prediction model was established using Logistic regression based on the training set data.Model fit was assessed using Hosmer-Lemeshow test,and predictive performance of the model was evaluated with the area under curve(AUC)of the receiver operating characteristic(ROC)curve.Results A total of 2,334 patients were included,of whom 215(9.2%)experienced the prolonged mechanical ventilation(>24 hours).The model developed from the training set identified seven factors that contributed to a prolonged mechanical ventilation:age(OR=1.03),body mass index(BMI,OR=1.14),time of extracorporeal circulation(OR=1.01),intraoperative blood transfusion(OR=4.15),postoperative serum total bilirubin(OR=1.08),postoperative serum albumin(OR=0.92)and postoperative re-sternotomy(OR=5.49).The AUC of the model for prediction of prolonged mechanical ventilation after CABG with extracorporeal circulation was 0.761,with a 95%CI of 0.716-0.806,a maximum Youden index of 0.105,a sensitivity of 77.94%,and a specificity of 64.38%.Validation using the validation set data yielded an AUC of 0.733,with a 95%CI of 0.662-0.804,a sensitivity of 75.32%,a specificity of 57.97%,and a predictive accuracy of 73.61%.Conclusion The risk prediction model developed in this study for prolonged mechanical ventilation after a CABG with extracorporeal circulation demonstrates a good predictive performance.It provides a reference for the nurses to identify the patient in high-risk of prolonged mechanical ventilation after a CABG with extracorporeal circulation and to implement preventive nursing measures.

Objective:To evaluate the validity and reliability of the Simplified Chinese version of the Beck Cognitive Insight Scale(SC-BCIS).Methods:Totally 188 patients with schizophrenia meeting the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5)diagnostic criteria were selected for SC-BCIS and the Insight and Treatment Attitudes Questionnaire(ITAQ)assessment.Thirty-eight patients were selected for retest-ing within 4 weeks.The item analysis was conducted using the Spearman correlation method,and the reliability of the scale was tested with Cronbach's α coefficient and ICC coefficient.The structural validity of the scale was ex-amined through principal component analysis and exploratory factor analysis.Results:The correlation coefficients between the 15 item scores and the total score of the SC-BCIS all met the screening criteria.The Cronbach's α coef-ficient of the scale was 0.69,the test-retest ICC value was 0.82,the ICC coefficient between the SC-BCIS and ITAQ scales was 0.83,and the scale had a two-factor structure,with a cumulative contribution rate of 42.4％for the two factors.Conclusion:The Simplified Chinese version of the Beck Cognitive Insight Scale(SC-BCIS)has good validity and reliability in measuring cognitive insight in patients with schizophrenia.

OBJECTIVE To investigate the effects of coptisine on endogenous metabolites in a dextran sulfate sodium(DSS)-in-duced ulcerative colitis(UC)mouse model,and to explore its potential mechanisms of action employing non-targeted metabolomics technology.METHODS SPF-grade male C57BL/6 mice were randomly divided into a control group,a model group,a sulfasalazine group(100 mg·kg-1),and low and high dose groups of coptisine groups(25,50 mg·kg-1).To induce ulcerative colitis(UC),all groups except the control group had free access to a 2.5%DSS solution for 7 days.At the same time,they also received daily intragastric ad-ministration of their corresponding treatments until the 10th day.Body weight changes,stool characteristics,and bloody stool occur-rence were recorded daily,and the disease activity index(DAI)was calculated.After the experiment,colon tissues were collected for pathological examination.Through UPLC-Q-TOF-MS/MS,non-targeted metabolomic analysis was performed to identify differential metabolites,and metabolic pathway enrichment analysis was conducted using the KEGG database.RESULTS Compared to the model group,coptisine significantly ameliorated weight loss,DAI scores,and pathological damage in colon tissues of UC mice(P<0.05,P<0.01).Metabolomic analysis identified 56 differential metabolites,mainly involved in purine metabolism,tryptophan metabo-lism,niacin and nicotinamide metabolism,glutathione metabolism,and the biosynthesis of phenylalanine,tyrosine,and tryptophan.Coptisine intervention significantly reversed the abnormal expression of these metabolites.CONCLUSION Coptisine can markedly improve metabolic disorders in DSS-induced UC mice by modulating multiple key metabolic pathways,thereby exerting a therapeutic effect.

OBJECTIVE To investigate the effects of coptisine on endogenous metabolites in a dextran sulfate sodium(DSS)-in-duced ulcerative colitis(UC)mouse model,and to explore its potential mechanisms of action employing non-targeted metabolomics technology.METHODS SPF-grade male C57BL/6 mice were randomly divided into a control group,a model group,a sulfasalazine group(100 mg·kg-1),and low and high dose groups of coptisine groups(25,50 mg·kg-1).To induce ulcerative colitis(UC),all groups except the control group had free access to a 2.5%DSS solution for 7 days.At the same time,they also received daily intragastric ad-ministration of their corresponding treatments until the 10th day.Body weight changes,stool characteristics,and bloody stool occur-rence were recorded daily,and the disease activity index(DAI)was calculated.After the experiment,colon tissues were collected for pathological examination.Through UPLC-Q-TOF-MS/MS,non-targeted metabolomic analysis was performed to identify differential metabolites,and metabolic pathway enrichment analysis was conducted using the KEGG database.RESULTS Compared to the model group,coptisine significantly ameliorated weight loss,DAI scores,and pathological damage in colon tissues of UC mice(P<0.05,P<0.01).Metabolomic analysis identified 56 differential metabolites,mainly involved in purine metabolism,tryptophan metabo-lism,niacin and nicotinamide metabolism,glutathione metabolism,and the biosynthesis of phenylalanine,tyrosine,and tryptophan.Coptisine intervention significantly reversed the abnormal expression of these metabolites.CONCLUSION Coptisine can markedly improve metabolic disorders in DSS-induced UC mice by modulating multiple key metabolic pathways,thereby exerting a therapeutic effect.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

Objective To develop a predictive model for assessment of the risk of the patients on prolonged mechanical ventilation after coronary artery bypass grafting with extracorporeal circulation.Methods A convenience sampling method was employed to select 2 334 patients who received the coronary artery bypass grafting(CABG)with extracorporeal circulation in our hospital from January 2021 to December 2023 as the study subjects.Preoperative,intraoperative and postoperative data were collected through structured queries from the electronic medical record system of hospital.The study subjects were randomly divided into a training set(n=1 633)and a validation set(n=701)following a 3:1 ratio.A risk prediction model was established using Logistic regression based on the training set data.Model fit was assessed using Hosmer-Lemeshow test,and predictive performance of the model was evaluated with the area under curve(AUC)of the receiver operating characteristic(ROC)curve.Results A total of 2,334 patients were included,of whom 215(9.2%)experienced the prolonged mechanical ventilation(>24 hours).The model developed from the training set identified seven factors that contributed to a prolonged mechanical ventilation:age(OR=1.03),body mass index(BMI,OR=1.14),time of extracorporeal circulation(OR=1.01),intraoperative blood transfusion(OR=4.15),postoperative serum total bilirubin(OR=1.08),postoperative serum albumin(OR=0.92)and postoperative re-sternotomy(OR=5.49).The AUC of the model for prediction of prolonged mechanical ventilation after CABG with extracorporeal circulation was 0.761,with a 95%CI of 0.716-0.806,a maximum Youden index of 0.105,a sensitivity of 77.94%,and a specificity of 64.38%.Validation using the validation set data yielded an AUC of 0.733,with a 95%CI of 0.662-0.804,a sensitivity of 75.32%,a specificity of 57.97%,and a predictive accuracy of 73.61%.Conclusion The risk prediction model developed in this study for prolonged mechanical ventilation after a CABG with extracorporeal circulation demonstrates a good predictive performance.It provides a reference for the nurses to identify the patient in high-risk of prolonged mechanical ventilation after a CABG with extracorporeal circulation and to implement preventive nursing measures.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

Objective:To evaluate the validity and reliability of the Simplified Chinese version of the Beck Cognitive Insight Scale(SC-BCIS).Methods:Totally 188 patients with schizophrenia meeting the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5)diagnostic criteria were selected for SC-BCIS and the Insight and Treatment Attitudes Questionnaire(ITAQ)assessment.Thirty-eight patients were selected for retest-ing within 4 weeks.The item analysis was conducted using the Spearman correlation method,and the reliability of the scale was tested with Cronbach's α coefficient and ICC coefficient.The structural validity of the scale was ex-amined through principal component analysis and exploratory factor analysis.Results:The correlation coefficients between the 15 item scores and the total score of the SC-BCIS all met the screening criteria.The Cronbach's α coef-ficient of the scale was 0.69,the test-retest ICC value was 0.82,the ICC coefficient between the SC-BCIS and ITAQ scales was 0.83,and the scale had a two-factor structure,with a cumulative contribution rate of 42.4％for the two factors.Conclusion:The Simplified Chinese version of the Beck Cognitive Insight Scale(SC-BCIS)has good validity and reliability in measuring cognitive insight in patients with schizophrenia.