OBJECTIVE To develope a predictive model for medication deviation risks during the hospital-to-home transition period in coronary heart disease （CHD） patients with initial treatment， aiming to assist medical staff in rapidly identifying high-risk groups for medication deviation. METHODS A total of 462 CHD patients with initial treatment from the Affiliated Hospital of North China University of Science and Technology （hereinafter referred to as “our hospital”） between January and July 2024 were enrolled. The patients were randomly divided into a modeling group and an internal validation group. The modeling group was further categorized into a medication deviation group and a non-medication deviation group based on whether medication deviations occurred. Similarly， 57 CHD patients with initial treatment from the cardiology department of our hospital between June and September 2025 were collected as an external validation group. Univariate analysis was used to screen predictive factors， followed by multivariate Logistic regression to construct the predictive model. Internal validation methods were employed to evaluate model performance， while external validation methods were used to test the model’s generalizability. RESULTS The 462 patients were divided into a modeling group （319 cases） and an internal validation group （143 cases）. In the modeling group， the medication deviation group （192 cases， 60.19%） and the non-medication deviation group （127 cases， 39.81%） were identified. Multivariate Logistic regression analysis revealed that age， medication type， medication adherence， and self-efficacy in rational medication use were predictive factors for medication deviations in CHD patients with initial treatment （ P ＜0.05）. The predictive model equation was logit P ＝ln[ P /（1－ P ） ] ＝1.321+1.732×age+4.091×medication type －4.360×medication adherence －3.081×self-efficacy in rational medication use. The model demonstrated good discrimination， with a Hosmer-Lemeshow goodness-of-fit test P -value of 0.439， an area under the receiver operating characteristic curve （AUC） of 0.870， sensitivity of 0.970， and specificity of 0.607. A risk nomogram with a total score of 350 points and a cutoff value of 110 points was plotted. The internal validation group showed an AUC o f 0.787 and a prediction accuracy of 77.6%， while the external validation group exhibited an AUC of 0.802 and a prediction accuracy of 73.7%. CONCLUSIONS This study successfully developed a predictive model for medication deviation risks during the hospital-to-home transition period in CHD patients with initial treatment. The model demonstrates excellent discrimination and predictive accuracy， effectively identifying high-risk populations for medication deviations. Age （＞70 years）， number of drug types≥5， poor medication adherence， and poor self-efficacy in rational medication use are independent risk factors for medication deviations.

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）］

ObjectiveTo analyze the output and efficiency of chronic disease management services for hypertension and diabetes in community health service institutions in Shanghai, to evaluate the allocation and efficiency of chronic disease management resources, and to provide references for improving the efficiency of chronic disease management in Shanghai’s communities. MethodsA total of 73 community health institutions from 15 districts of Shanghai were selected. Based on the chronic disease management resource data of community health institutions in 2019, 2021, and 2023, descriptive methods and the BCC model in Data Envelopment Analysis (DEA) were used to analyze the resource allocation, service output, and the efficiency of chronic disease management services in community health institutions in Shanghai. ResultsFrom 2019 to 2023, the indicators of resource allocation and service output of community health service institutions in Shanghai all showed upward trends. In terms of service efficiency, the average number of chronic disease patients managed by each health technician decreased from 237 to 231, and the number of patients served per 1 000 working hours decreased from 54 to 44. DEA efficiency analyses showed that the average overall efficiency increased from 0.867 in 2019 to 0.886 in 2021 and then decreased to 0.811 in 2023. The average technical efficiency decreased from 0.901 to 0.847, which was the main reason for the decline in overall efficiency. The average scale efficiency showed an initial increase followed by a decrease, rising from 0.961 in 2019 to 0.977 in 2021 and dropping back to 0.955 in 2023. The number of strong effective districts (overall efficiency=1.000) decreased from 6 in 2019 to 3 in 2023, and the efficiency differences between districts expanded. The range of overall efficiency widened from 0.622‒1.000 to 0.513‒1.000, indicating a decline in overall efficiency and an increase in inter-district heterogeneity. ConclusionFrom 2019 to 2023, the total volume of resource allocation and service output for chronic disease management in community health service institutions in Shanghai increased continuously. However, both the service output efficiency and the overall resource allocation efficiency declined, driven primarily by a decrease in technical efficiency, and accompanied by widening inter‑district disparities.

Objective: To investigate the longitudinal associations between dietary patterns in early childhood and myopia among children, so as to provide evidence for improving early childhood dietary patterns and promoting healthy ocular development among children. Methods: Participants were derived from the Ma-anshan Birth Cohort (MABC). A total of 1 432 children were included. Dietary patterns at 2.5 years of age were collected in 2017 using a food frequency questionnaire completed by parents. In 2022, cycloplegic refraction and ocular biometric parameters were measured in children aged 7-8 years. Food components were grouped into tertiles ( T1-T 3), and latent class analysis (LCA) was used to identify dietary pattern at the age of 2.5. Log-binomial regression and multiple linear regression model were applied to examine the associations of dietary patterns and their latent classes with myopia, spherical equivalent (SE), and axial length (AL). Results: The prevalence of myopia among children was 23.9%. After adjusting for covariates, the T 2 group of cereals and tubers was negatively associated with myopia rate ( RR=0.72, 95%CI =0.57-0.90) and AL ( β=-0.15, 95%CI =-0.24 to -0.05), and positively associated with SE ( β=0.21, 95%CI =0.06-0.35)(all P ＜0.05). Linear regression models showed that the T 2 group of fruits and vegetables was positively associated with AL ( β=0.09, 95%CI =0.00- 0.19 ); the T 3 group of high sugar foods ( β=0.11, 95%CI =0.00-0.21) and the T 3 group of processed foods ( β= 0.10 , 95%CI =0.00-0.19) were positively associated with AL; total scores for high sugar foods ( β=0.07, 95%CI =0.00-0.14) and processed foods ( β=0.09, 95%CI =0.01-0.17) were positively associated with AL (all P <0.05). After adjusting for sex, age, and parental myopia, the comprehensive high intake pattern was positively associated with AL ( β=0.12, 95%CI =0.00-0.23, P <0.05). After further adjusting for daily screen time and daily outdoor activity time, the association was no longer statistically significant ( β= 0.11 , 95%CI =-0.00-0.23, P >0.05). Conclusions Moderate consumption of cereals and tubers in early childhood may reduce the risk of myopia and delay AL growth. In contrast, high sugar and highly processed diets may promote AL and increase myopia risk. Establishing healthy dietary habits in early childhood and limiting intake of high sugar and processed foods is crucial for promoting ocular development in children.

ObjectiveTo observe the clinical efficacy and safety of Yanghe Xiaonang Decoction （阳和消囊汤， YXD） in the treatment of polycystic ovarian syndrome （PCOS）-related infertility with healthy qi deficiency and phlegm stasis syndrome. MethodsA total of 120 PCOS-related infertility participants with healthy qi deficiency and phlegm stasis syndrome were randomly divided into a treatment group and a control group in a 1∶1 ratio， with 60 cases in each group. The treatment group started taking YXD on the first day of the menstrual cycle， one dose daily， until the day of sexual intercourse when follicles matured and were released. Those without mature follicles or ovulation could take it until the 14th to 28th day. The control group was given oral letrozole at 2.5 mg daily starting on the 5th day of the menstrual cycle for 5 consecutive days. If the basal body temperature curve remained monophasic on the 28th day of the menstrual cycle， and no dominant follicle was detected by color Doppler ultrasound， estrogen plus progesterone or progesterone alone was administered for withdrawal bleeding， followed by a new treatment cycle. The treatment lasted for three consecutive menstrual cycles. Patients were followed up for 3 months upon confirmed pregnancy or after the completion of the 3rd treatment cycle. The clinical pregnancy rate， total effective rate， pregnancy rate， biochemical pregnancy rate， embryonic arrest rate， and ectopic pregnancy rate between the two groups were compared. Transvaginal color Doppler ultrasound was used for monitoring endometrial thickness， cyclic ovulation status， number of follicles between 2-9 mm， and the difference in transverse and longitudinal diameters of dominant follicles. The levels of endocrine hormone indicators including serum testosterone （T）， prolactin （PRL）， luteinizing hormone （LH）， follicle-stimulating hormone （FSH）， progesterone （P）， estradiol （E₂）， free testosterone （F-T）， androstenedione （ASD）， inhibin B （INH-B）， and anti-Müllerian hormone （AMH）， as well as the total traditional Chinese medicine （TCM） symptom scores before and after treatment were compared between the two groups. ResultsA total of 58 patients in each group were included in the statistical analysis. The clinical pregnancy rate in the treatment group was 39.66% （23/58）， higher than 22.41% （13/58） in the control group （P＜0.05）. The total effective rate in the treatment group was 96.55% （56/58）， higher than 79.31% （46/58） in the control group （P＜0.05）. The biochemical pregnancy rate and embryonic arrest rate in the treatment group were lower than those in the control group （P＜0.05）. The treatment group was superior to the control group in increasing endometrial thickness and reducing the number of 2-9 mm follicles at the 2nd and 3rd cycles， as well as in improving the cyclic ovulation status and the difference in transverse and longitudinal diameters of dominant follicles at the 3rd cycle （P＜0.05）. After treatment， the treatment group exhibited significant reductions in serum T， LH， F-T， ASD， INH-B， and AMH levels （P＜0.05）. The control group also showed significant decreases in LH， F-T， INH-B， and AMH levels （P＜0.05）. The levels of F-T， INH-B， and AMH in the treatment group after treatment were lower than those in the control group （P＜0.05）. The improvement of the total TCM symptom score in the treatment group was superior to that in the control group （P＜0.05）. ConclusionYXD can significantly improve the clinical pregnancy rate， reduce the rates of biochemical pregnancy and embryonic arrest， alleviate clinical symptoms， increase endometrial thickness， enhance dominant follicles， and improve endocrine hormone levels in PCOS-related infertility with healthy qi deficiency and phlegm stasis syndrome， showing good safety.

ObjectiveTo develop and evaluate an O-RADSbased multiparametric model integratingcontrast-enhanced US （CEUS） and the z-score standardized risk of ovarian malignancy algorithm （ROMA z-score） to improve the diagnostic accuracy for differentiating benign， borderline， and malignant ovarian epithelial tumors （OETs）. MethodsThis retrospective study included 129 patients with pathologically confirmed OETs between May 2018 and December 2024. Clinical variables （age， menopausal status， BMI， HE4， CA125） and sonographic features （conventional US and CEUS） were collected. Tumors were classified as benign， borderline， or malignant according to pathological findings. A random forest algorithm was used to contruct the predictive model. Receiver operating characteristic （ROC） curves were generated tocompare the diagnostic performance of O-RADS， O-RADS+CEUS， O-RADS+ROMA z-score， and O-RADS+ CEUS+ROMA z-score. ResultsAmong the 129 patients， 63 had benign tumors， 25 had borderline tumors， and 41 had malignant tumors. Compared with O-RADS alone， the combined O-RADS+CEUS+ROMA z-score model demonstrated superior diagnostic performance， with an AUC of 0.87 （95% CI： 0.79-0.95） vs. 0.64 （95% CI： 0.51-0.76）， P＜0.001， for differentiating benign from borderline tumors， and 0.90 （95% CI： 0.83-0.97） vs. 0.64 （95% CI： 0.50-0.78）， P＜0.001， for differentiating borderline from malignant tumors. The random forest analysis identified HE4， CA125 and CEUS features as the most important predictors，contributing 11.02， 8.82， 18.1 points， respectively， to the total importance score of 51.94. In the test cohort， the model achieved an overall diagnostic accuracy of 72.97%， with an AUC of 0.89（95%CI：0.77-1.00）， sensitivity of 67.06%， and specificity of 85.01%. ConclusionsO-RADS+CEUS+ROMA z-score multiparametric model demonstrated the highest diagnostic performance for in differentiating benign from borderline and borderline from malignant OETs. HE4， CA125 and CEUS features were identified as key predictive risk factors， supporting the role of this model in improving preoperative risk stratification of OETs.

This study aims to explore the synergistic effects of the main components in salvianolic acids for Injection(SAFI) on key pathological events in cerebral ischemia, elucidating the pharmacological characteristics of SAFI in neuroprotection. Two major pathological gene modules related to endothelial injury and neuroinflammation in cerebral ischemia were mined from single-cell data. According to the topological distance calculated in network medicine, potential synergistic component combinations of SAFI were screened out. The results showed that the combination of caffeic acid and salvianolic acid B scored the highest in addressing both endothelial injury and neuroinflammation, demonstrating potential synergistic effects. The cell experiments confirmed that the combination of these two components at a ratio of 1∶1 significantly protected brain microvascular endothelial cells(bEnd.3) from oxygen-glucose deprivation/reoxygenation(OGD/R)-induced reperfusion injury and effectively suppressed lipopolysaccharide(LPS)-induced neuroinflammatory responses in microglial cells(BV-2). This study provides a new method for uncovering synergistic effects among active components in traditional Chinese medicine(TCM) and offers novel insights into the multi-component, multi-target acting mechanisms of TCM.
Brain Ischemia/metabolism* ; Neuroprotective Agents/pharmacology* ; Animals ; Drugs, Chinese Herbal/administration & dosage* ; Benzofurans/pharmacology* ; Mice ; Drug Synergism ; Caffeic Acids/pharmacology* ; Polyphenols/pharmacology* ; Humans ; Alkenes/pharmacology* ; Endothelial Cells/drug effects* ; Depsides

Objective:To establish a novel method for the early diagnosis of gastric cancer(GC)by screening for the microRNAs within tumor-specific exosomes in peripheral blood.Methods:The gene expression omnibus database was used to download the GSE148334 and GSE130654 datasets of GC exosomes,and differentially expressed RNAs were obtained according to logFC>1 or logFC<-1 and P<0.05.TargetScan and ENCORI databases were used to predict the regulatory relationship between mRNA,miRNA,and ln-cRNA,and Cytoscape software was used to construct a ceRNA network and identify hub genes for validation.A total of 27 patients with early-stage GC,25 healthy controls,and 25 patients with other types of cancer were enrolled,and the ultracentrifugation method was used to isolate exosomes in serum.RT-qPCR was performed for RNA in serum and exosome samples to analyze the expression of hub genes in each group.Results:Three hub genes were identified by the bioinformatics method,namely hsa-miR-105-5p,hsa-miR-219b-3p,and hsa-miR-889-3p,and RT-qPCR showed that the GC group had a significantly higher expression level of hsa-miR-219b-3p in serum and exosome samples than the healthy control group and the other cancer group(serum:4.050±2.697 vs.1.357±0.857/1.934±2.434,P<0.05;exosomes:2.525±1.518 vs.0.774±0.559/1.259±2.127,P<0.05),while there were no significant differences in the expression levels of hsa-miR-889-3p and hsa-miR-105-5p between the GC group and the other two groups(P>0.05).The re-ceiver operating characteristic(ROC)curve showed that hsa-miR-219b-3p in serum-derived exosomes had an area under the ROC curve of 0.896(95%CI=0.800-0.993),which was significantly better than the traditional tumor markers of carcinoembryonic antigen(P=0.015),CA19-9(P=0.021),and CA72-4(P=0.005),and there-fore,exosomal hsa-miR-219b-3p showed a better diagnostic efficacy in GC patients.Conclusion:This study shows that hsa-miR-219b-3p in serum-derived exosomes can be used as a potential marker for the early diagnosis of GC in clinical practice.

Iodine speciations in aquatic environments are affected by dissolved oxygen,redox potential,microbial activity,organic matter decomposition,light reaction,etc.Accurate quantification of iodine speciation can not only help to understand the geochemical cycle of iodine,but also help to trace and study environmental processes.Based on the combination of ion chromatography(IC)and inductively coupled plasma mass spectrometry(ICP-MS),a rapid and sensitive method was established for determining the speciations of iodine in environmental water samples including seawater,river water,lake water,rainwater,groundwater,etc.The results presented here showed that IO3?and I?in seawater were quickly separated and measured within 120 s when using guard column AG22 and 8 mmol/L(NH4)2CO3 as the mobile phase.While for lake water,river water and precipitation samples with high soluble organically bond iodine(SOI),an AS22 separation column(250 mm×4 mm)connected with a guard column and using 50 mmol/L(NH4)2CO3 as mobile phase could effectively separate unknown SOI from IO3? to achieve accurate quantification of IO3?.For accurate correction of iodine measurement signal fluctuations,133Cs was directly added to the(NH4)2CO3 mobile phase as an internal standard.The SOI content was calculated by the total iodine concentrations minus the sum of IO3?and I?.The precision of the established iodine speciation analytical method was better than 3.5%,and the standard addition experiment showed that the analytical method was accurate.When the injection volume was 25 μL,the detection limits were 0.011?0.025 μg/L for IO3? and 0.023?0.031 μg/L for I?,respectively.The method was successfully used to analyze IO3?,SOI and I? in environmental water samples,such as seawater,river water,rainwater and groundwater.

Nitrogen dioxide(NO 2)is a prevalent air pollutant that poses significant threats to the environment and human health,emphasizing the urgent need for high-performance NO 2 sensors for effective environmental monitoring at room temperature.Sensors based on metal halide perovskites have emerged as promising candidates for gas detection at room temperature,but their long-term stability remains a major challenge.In this study,a methylamine thiocyanate(MT)-doped FA 0.8Cs 0.2PbI 2Br(PVK)gas sensor(MT-PVK)was prepared using a simple one-step spin-coating method and ethyl acetate(EA)anti-solvent extraction technique.The crystalline structure,chemical composition,and particle morphology of the MT-PVK thin film were characterized.The MT-PVK thin film was then utilized as a gas sensor for NO 2 detection at room temperature.The results demonstrated that the sensor exhibited outstanding selectivity and reversibility at low concentrations of NO 2 gas,with a detection limit as low as 33 ppb(10-9,nL/L).For 10 ppm(10-6,μL/L)NO 2,the sensor showed rapid response and recovery time of only 1.6 s and 27 s,outperforming most traditional metal oxide NO 2 sensors.Furthermore,compared to the original PVK,the MT molecules significantly enhanced the structural and sensing stability of the MT-PVK sensor under high humidity conditions(55%±5%).These findings suggested that MT-doped FA 0.8Cs 0.2PbI 2Br perovskite gas sensors offered a promising pathway for the development of rapid-response gas sensing technologies suitable for room temperature operation.