BackgroundThe violent aggressive behaviors of patients with schizophrenia usually have the characteristics of suddenness， unpredictability， high severity， and great difficulty in prevention. Early identification and accurate assessment of their risk of violent aggression have significant clinical significance. ObjectiveTo construct a predictive model for the violence risk in hospitalized patients with schizophrenia， to identify the key factors influencing the occurrence of violent behavior in these patients， so as to provide references for clinical precise quantitative assessment and early intervention. MethodsA total of 200 patients with schizophrenia who were hospitalized at Taiyuan Psychiatric Hospital from March 2022 to September 2024 and met the diagnostic criteria of the International Classification of Diseases， eleventh edition （ICD-11） were collected to form the modeling cohort. They were randomly divided into a training set （n=140） and a test set （n=60） at a ratio of 7∶3. Based on the least absolute shrinkage and selection operator （LASSO） regression algorithm， the feature variables were screened and dimension-reduced. The support vector machine （SVM） from machine learning was selected for model training and prediction. The discrimination efficacy of the model was evaluated by the area under the receiver operating characteristic （ROC） curve （AUC）， accuracy， precision， sensitivity， specificity， F1 value， and Brier value. ResultsLASSO regression screening identified 16 feature variables. Pearson correlation analysis revealed a positive correlation between prior violent behavior frequency and clinical psychiatric symptom scores （r=0.580， P<0.01）， a positive correlation between hospitalization compliance and current disease status （r=0.550， P=0.003）， and a positive correlation between educational level and family per capita monthly income （r=0.367， P<0.01）. The SVM model achieved an AUC of 0.853， accuracy of 0.800， precision of 0.810， sensitivity of 0.895， specificity of 0.636， F1 value of 0.850， and Brier value of 0.168. ConclusionThe SVM model has a relatively high level of applicability and overall predictive performance in the assessment of violent risk in schizophrenia patients， which is helpful for the early identification of violent risks in such patients. ［Funded by Specialized Research Project for Enhancing the Competence of Health Professionals in Taiyuan City （number， Y2023006）］

Objective To evaluate the effectiveness of schistosomiasis surveillance in Jiangsu Province during the stage moving from transmission control to transmission interruption, and to analyze the current risk and challenges, so as to provide the evidence for achieving the target of schistosomiasis elimination. Methods Schistosomiasis surveillance data were collected from Jiangsu Province from 2012 to 2024, and the endemic areas, Schistosoma japonicum infections in humans and livestock, Oncomelania hupensis snail distribution and implementation of integrated interventions were descriptively analyzed. In addition, the trends in areas with snails, seroprevalence of human S. japonicum infections and numbers of advanced schistosomiasis cases were assessed using a Joinpoint regression model. Results The endemic areas of schistosomiasis continued to shrink in Jiangsu Province from 2012 to 2024, with the number of schistosomiasis-eliminated counties (cities, districts) increasing from 53 (75.71%) to 63 (96.92%), and interruption of schistosomiasis transmission was achieved across the province. A total of 4 600 300 person-times were tested for serum antibodies against S. japonicum, with 28 719 person-times positive detected; and 616 500 person-times were tested S. japonicum infections among local residents in Jiangsu Province from 2012 to 2024, with only 3 egg-positives detected, and no egg-positives found since 2017. A total of 187 600 herd-times were tested for schistosomiasis in livestock, and no S. japonicum infections were found. O. hupensis snail survey was performed covering 1 018 408.97 hm², and a total of 35 556.35 hm² was found with snail-infested habitats, including 174.40 hm2 of emerging snail-infested habitats. A total of 1 102 800 O. hupensis snails were identified for S. japonicum infections, and no infections were found. The areas of snail-infested habitats appeared a tendency towards a rise in Jiangsu Province from 2019 to 2023 (APC = 23.67%, P < 0.05), and the actual areas of snail-infested habitats appeared a tendency towards a decline from 2012 to 2015 (APC = −22.77%, P < 0.05), and towards a rise from 2015 to 2023 (APC = 9.76%, P < 0.01). The seroprevalence of anti-S. japonicum antibodies appeared a tendency towards a decline among residents in Jiangsu Province from 2017 to 2023 (APC = −14.92%, P < 0.01). In addition, the number of newly diagnosed advanced schistosomiasis cases appeared a tendency towards a decline from 2012 to 2024 (APC = −12.02%, P < 0.01), and the numbers of advanced schistosomiasis patients requiring treatment showed a tendency towards a decline from 2012 to 2021 (APC = −10.56%, P < 0.01) and from 2021 to 2023 (APC = −20.06%, P < 0.01). Conclusions Great progresses had been achieved in schistosomiasis control in Jiangsu Province following transmission control, and transmission interruption had been achieved; however, there are still snail-infested habitats. High-intensity surveillance and integrated control are required to be maintained to advance the achievement of the target of schistosomiasis elimination in Jiangsu Province.

ObjectiveTo investigate the current professional competency among full-time and part-time personnel of the nosocomial infection control administration in Shanghai, so as to provide a scientific basis for future training programmes. MethodsIn December 2024, a questionnaire survey was conducted by the Shanghai Nosocomial Infection Quality Control Center among full-time and part-time personnel of the nosocomial infection control administration across medical institutions at various levels and types in Shanghai using convenience sampling method. The questionnaire consisted of two parts: demographic information and professional competency assessment. The professional competency scale comprised four dimensions: fundamental cognition, basic skills, professional expertise, and personal qualities, totaling 35 items. ResultsA total of 1 179 questionnaires were distributed, with 1 144 valid responses collected, yielding an effective response rate of 97.03%. Statistically significant differences were observed among full-time and part-time personnel of the nosocomial infection control administration in terms of age (t=5.32, P=0.021), professional background (χ²=9.90, P=0.019), educational qualifications (χ²=19.10, P<0.001), professional titles (χ²=12.60, P=0.002), and the levels of medical institutions (χ²=111.08, P<0.001). The scores of full-time personnel of the nosocomial infection control administration in fundamental cognition [92 (82, 99) points] and basic skills [88 (78, 96) points] were significantly higher than those of part-time personnel(Z=-2.21, P=0.027;Z=-2.74, P=0.006). Statistically significant differences were found in fundamental cognition scores between full-time and part-time personnel of the nosocomial infection control administration regarding occupational safety protection, definition of healthcare-associated infection outbreaks, types of drug-resistant bacteria and their prevention and control strategies, and transmission routes of different infectious diseases (all P<0.05). Statistically significant differences were also observed in basic skills scores including proficient use of monitoring platforms, formulation and revision of standard operating procedures (SOPs), independent completion of targeted surveillance, guidance on basic infection control skills, guidance for key departments, and follow-up of personnel with occupational exposure (all P<0.05). However, no statistically significant differences were found in scores of professional knowledge and personal qualities (P>0.05). ConclusionThere are certain differences in professional competency between full-time and part-time personnel of the nosocomial infection control administration in Shanghai in terms of fundamental cognition and basic skills. Part-time personnel can effectively improve their professional competency through systematic training on basic infection control knowledge and practical skills, thereby comprehensively enhancing the overall quality of the nosocomial infection administration team.

BackgroundModified electroconvulsive therapy （MECT） is a common front-line strategy widely used in psychiatric practice， and the optimal first stimulus dosage in MECT is usually estimated clinically based on the factors influencing the patient's initial seizure threshold （IST）. However， previous studies on the influencing factors of IST have mostly suffered from limitations such as small sample sizes and single-dimensional research perspectives. ObjectiveTo explore the factors influencing IST in MECT for patients with mental disorders， so as to provide references for stimulus dosing strategies in MECT for the patients. MethodsA retrospective study was used to include 1 446 inpatients fulfilling the diagnostic criteria for any specific mental disorder listed in the ICD-10 and receiving MECT at Shandong Daizhuang Hospital from January 1， 2021 to August 1， 2023. Their general and clinical data were collected， including IST， psychiatric diagnostic categories， gender， ethnicity， age， body weight， body mass index （BMI）， course of disease， family history of psychiatric disorders， first episode status， use of antiepileptic drugs the day before treatment， use of benzodiazepines the day before treatment， and previous MECT treatment history. Pearson correlation analysis was utilized to test the correlation of IST with age， height， body weight， BMI， and course of disease， and stepwise multivariate linear regression analysis was performed to identify the factors affecting IST. ResultsIST yielded statistical difference among patients in terms of gender， first episode status， use of antiepileptic drugs the day before treatment， and use of benzodiazepines the day before treatment （t=2.256， -3.059， -2.136， -3.006， P<0.05 or 0.01）. IST in patients of different ages and psychiatric diagnostic categories also demonstrated statistical difference （F=913.120， 6.212， P<0.01）. Within young population， IST varied significantly based on the psychiatric diagnostic categories （F=2.986， P<0.05）. Correlation analysis indicated that IST was positively correlated with age， body weight， BMI and course of disease （r=0.886， 0.055， 0.184， 0.456， P<0.05 or 0.01）， and negatively correlated with height （r=-0.183， P<0.01）. Stepwise multivariate linear regression analysis revealed that age， gender， and body weight were influencing factors of IST （β=0.888， -0.049， -0.035， P<0.01）. ConclusionsAge， gender and body weight may be factors influencing IST in MECT for patients with mental disorders. ［Funded by Key R&D Plan Projects of Jining City in 2024 (number, 2024YXNS202）］

Objective Based on the clinical data and imaging manifestations of patients with ischemic stroke to establish a simple clinical prediction model that is used for identifying intracranial atherosclerotic stenosis-acute large vessel occlusion(ICAS-LVO in posterior circulation before surgery.Methods The clinical data of patients with acute large vessel occlusion(LVO in the posterior circulation,who received endovascular intervention at the First Affiliated Hospital of Nanjing Medical University of China from January 2019 to September 2022,were retrospectively analyzed.According to the intraoperative angiographic findings,the patients were divided into ICAS-LVO group and non-ICAS-LVO group.Univariate analysis and multivariate logistic regression analysis were used to analyze the patient's demographic characteristics,clinical history,imaging findings,and laboratory results,based on which a clinical prediction model for ICAS-LVO was established,and according to the relevant parameters a nomogram prediction model was plotted.Results A total of 110 patients with LVO in the posterior circulation who received endovascular treatment were included in the final analysis.In 51 patients(49.6％)the cause of vascular occlusion was the atherosclerotic stenosis of the intracranial arteries.Compared with non-ICAS-LVO group,in ICAS-LVO group the patients were younger,the incidence of atrial fibrillation was lower,and the level of plasma D-dimer was lower.Three factors,including atrial fibrillation,occlusion site and collateral circulation status,were finally screened out to establish the prediction model for ICAS-LVO.This model demonstrated acceptable calibration(Hosmer-Lemeshow test,P=0.562)and good discrimination ability(AUC=0.956;95％CI:0.906-0.986).Conclusion The clinical prediction model for ICAS-LVO,which is established on the three predictive factors(absence of atrial fibrillation,occlusion located at the V4 segment of the vertebral artery or at the proximal to mid segment of the basilar artery,and a favorable collateral circulation),carries high sensitivity and accuracy.This model can help neurointervention physicians to make early identification of ICAS-LVO and to promptly formulate vascular recanalization treatment strategies.

Objective To explore the neuroprotective effects and mechanisms of asiaticoside(AS)in rats with transient cerebral ischemia.Methods One hundred male Sprague-Dawley(SD)rats were randomly divided into five groups:sham-operated(Sham)group,transient middle cerebral artery occlusion(tMCAO)group,and low-,medium-,and high-dose AS(AS-L,AS-M,AS-H)groups,with 20 rats in each group.Except for the Sham group,rats in the other four groups under-went tMCAO surgery.Rats in the AS-L,AS-M,and AS-H groups received intragastric administration of 20,40 and 80 mg/kg AS respectively,once daily for 7 days starting 1 hour post-surgery.Rats in the Sham and tMCAO groups received equivalent volumes of saline.Neurological deficit score,brain water content,and TTC staining were used to evaluate neurological impairment,cerebral edema,and infarct volume.HE staining and Nissl staining wereused to assess histopathological changes and neu-ronal damage.Autophagy was detected via transmission electron microscopy.Immunofluorescence staining was usedto analyze the expression and localization of microtubule-associated protein light chain 3B(LC3B).TUNEL staining was used to evaluate apoptosis,and Western blot was used to measure protein expression.Results Compared with the Sham group,rats in the tMCAO group ex-hibited significantly increased neurological deficit score,brain water content,infarct volume,and histopathological damage,as well as significantly decreased Nissl body counts(P＜0.05).AS dose-dependently reduced neurological deficits,brain water content,infarct volume,and histopatho-logical damage while increased Nissl body numbers.The tMCAO group showed significantly higher numbers of autophagosomes,lysosomes,and LC3B-positive cells,along with significantly elevated LC3-Ⅱ/LC3-Ⅰ,Beclin-1,Bax,cleaved caspase-3 compared to the Sham group;in contrast,p-PI3K,p-Akt,and p-mTOR protein levels,intact mitochondria count and p62 and Bcl-2 protein levels were significantly lower inthe tMCAO group(P＜0.05).Compared with the tMCAO group,AS treatment dose-dependently significantly decreased autophagosomes,lysosomes,LC3B-positive cells,and the expression of LC3-Ⅱ/LC3-Ⅰ,Beclin-1,Bax,cleaved caspase-3 while significantly increased p-PI3K,p-Akt,and p-mTOR protein,intact mitochondria and p62 and Bcl-2 levels(P＜0.05).Conclusion AS exerts neuroprotective effects by inhibiting excessive autophagy and apopto-sis in rats with transient cerebral ischemia via activation of the PI3K/Akt/mTOR signaling pathway.

Objective To utilize single-cell and peripheral blood transcriptomic data from 3D brain organoids,combined with machine learning,to analyze the role of mitochondrial autophagy genes in schizophrenia(SCZ).Methods By integrating two machine learning algorithms,we identified differentially expressed mitochondrial autophagy-related genes between schizophrenia patients and healthy controls using peripheral blood RNA sequencing data.The relationship between mitophagy gene,immune cells and inflammatory factors was further explored.Comprehensive single-cell analysis was used to explore the signaling pathways and specific transcription factors based on mitophagy genes.Results Using machine learning,seven key mitophagy genes expressed in schizophrenia patients were identified.Based on Mitoscore analysis,at the single-cell level,neurons with high mitochondrial autophagy activity(Mitohigh_Neuron)formed new interactions with endothelial cells via the SPP1 signaling pathway.Conclusion This study identified two subtypes of mitophagy and seven key mitophagy genes in schizophrenia,providing new insights into the pathogenesis of the disease.

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

Objective:To investigate the value of nomogram model based on CT features in differentiating ectopic pancreas (EP) from gastrointestinal stromal tumors (GIST) with a long diameter less than 3 cm.Methods:This study was a case-control study. The clinical and imaging data of 43 patients with EP and 90 patients with GIST confirmed by pathology in the Affiliated Hospital of Guizhou Medical University from August 2013 to March 2024 were retrospectively analyzed. Preoperative CT images were analyzed to obtain qualitative features (number of lesions, location, morphology, growth pattern, borders, cystic degeneration, calcification, ulceration, catheter sign, central umbilication) and quantitative features (lesion long diameter, short diameter, long/short diameter, lesion and normal pancreas arterial-phase and venous-phase CT values, and enhancement ratio). Statistical analyses, including independent sample t-tests, Mann-Whitney U tests, χ2 tests, and Fisher exact tests, were performed to compare CT characteristics between the two groups. Binary logistic regression analysis was used to obtain independent predictors to identify the two groups, to establish a joint model, and to draw a nomogram. The discriminative performance of the independent predictors and the combined model was assessed using receiver operating characteristic (ROC) curves, while calibration curves were used to evaluate model fit. Results:The differences in age, location, morphology, border, catheter sign, central umbilication, short diameter, long/short diameter, arteriovenous phase enhancement CT value and arteriovenous phase enhancement ratio were statistically significant between the EP group and the GIST group (all P<0.05). The logistic analysis showed that the differences in age ( OR=0.920, 95% CI 0.885-0.956, P<0.001), border ( OR=5.994, 95% CI 2.111-17.022, P=0.001), long/short diameter ( OR=7.820, 95% CI 1.841-33.224, P=0.005), and venous phase enhancement ratio ( OR=8.847, 95% CI 1.103-70.972, P=0.040) were the independent predictors for distinguishing EP from GIST, and the area under the ROC curve (AUC) were 0.782 (95% CI 0.698-0.866), 0.684 (95% CI 0.600-0.767), 0.705 (95% CI 0.607-0.803), and 0.693 (95% CI 0.605-0.781), respectively. Combined age, border, long diameter/short diameter and venous phase enhancement ratio were plotted in a nomogram with an AUC of 0.881 (95% CI 0.817-0.945), sensitivity and specificity of 74.4% and 93.3%, respectively. The calibration curve demonstrated a strong agreement between predicted and actual probabilities (Hosmer-Lemeschow test, P=0.267). Conclusions:CT imaging reveals significant differences between EP and small GISTs (<3 cm). EP is more likely when patients are younger and lesions exhibit indistinct borders, a higher long-to-short diameter ratio, and greater venous-phase enhancement. The nomogram derived from CT features provides a valuable tool for differentiating EP from GIST.

Objective To investigate the value of measuring optic nerve sheath diameter(ONSD)at different times after the return of spontaneous circulation(ROSC)in evaluating the neurological function prognosis of patients with cardiac arrest(CA).Methods A total of 48 patients who successfully resuscitated from CA and transferred to the ICU in Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine from March 2021 to June 2024 were selected as subjects.ONSD was measured initially,12 to 24h,day 2 and day 3 after ROSC.According to the brain function score of 3 months after treatment cerebral performance categories(CPC),the patients were divided into poor neurological group(CPC 3-5 points,n=24)and neurological good group(CPC 1-2 points,n=24).The predictive value of ONSD measurements at each time for neurological outcome in CA patients was analyzed using receiver operating characteristic curves.Results There were significant difference in ONSD from 12 to 24 h,days 2 and 3 between two groups(P＜0.001),and the area under the curve were 0.785,0.837 and 0.841,respectively.Conclusion Measuring the ONSD by ultrasonography at 12-24 hours,day 2,and day 3 after ROSC in post-CA patients have good value in predicting neurologic prognosis.Measurement of ONSD on day 2 may be the optimal time to evaluate the neurological prognosis of patients after CA.