ObjectiveTo investigate the molecular mechanisms related to angiogenesis during the development and progression of diabetic retinopathy （DR）. MethodsAngiogenesis-related genes were obtained from the Gencard website and intersected with differentially expressed genes from DR datasets （GSE60436 and GSE94019）. Functional enrichment and protein-protein interaction （PPI） networks were then used to screen candidate genes and evaluate their diagnostic value. Gene set enrichment analysis （GSEA） was used to explore potential pathways underlying candidate genes， and immune infiltration analysis revealed associations between candidate genes and immune cells. Cellular experiments were conducted to validate the role of fibronectin 1 （FN1） in human retinal microvascular endothelial cells （HRMECs） under high glucose （HG） conditions. ResultsA total of 237 differentially expressed genes related to angiogenesis were identified， enriched in pathways such as phosphoinositide 3-kinase/protein kinase B signaling pathway （PI3K-Akt）， tumor suppressor protein 53 （P53）， tumor necrosis factor （TNF）， and Janus kinase （JAK）/signal transducer and activator of transcription signaling pathway （STAT）. Among them， collagen type I alpha 1 chain （COL1A1）， COL1A2， FN1， TNF， and tumor protein p53 （TP53） were key genes with high diagnostic value. GSEA indicated that these genes were involved in multiple signaling pathways， including P53. CIBERSORTx analysis revealed significant associations with the infiltration of multiple immune cells. HG treatment led to the upregulation of FN1. In HG-induced HRMECs， compared with the si-NC control group， si-FN1 significantly reduced cell proliferation， migration， and tube formation， while P53 protein expression was increased. ConclusionThis study reveals the important role of FN1 in angiogenesis in DR and suggests that it may be a potential diagnostic and therapeutic target.

ObjectiveTo investigate the mechanism of action of remifentanil （RMZL） in alleviating lung ischemia-reperfusion injury （LIRI） in rats by inhibiting pyroptosis through modulating hypoxia inducible factor-1α （HIF-1α）/NOD-like receptor thermal protein domain associated protein 3 （NLRP3） pathway. MethodsRats were stochastically assigned into Control group， LIRI group， RMZL low-dose group， RMZL medium-dose group， RMZL high-dose group， and RMZL high-dose+HIF-1α activator dimethyloxallyl glycine （DMOG） group， with 18 rats in each group. Rats in Control group only had their left pulmonary hilum free and did not undergo ischemia-reperfusion treatment. Except for the Control group， LIRI models were constructed in all other groups. Rats in LIRI group were intraperitoneally injected with an equal amount of physiological saline 15 minutes before constructing LIRI model； rats in Control group were intraperitoneally injected with an equal amount of physiological saline 15 minutes before freeing left pulmonary hilum； rats in other groups were intraperitoneally injected with corresponding dose of drug 15 minutes before constructing LIRI model. The wet/dry weight ratio of lungs was calculated. HE staining was used to study lung tissue pathology. Immunofluorescence staining was used to detect the relative fluorescence intensity of gasdermin D （GSDMD） and NLRP3 double positive cells in lung tissue. ELISA was used to detect interleukin-1β and IL-18 in lung tissue. Western blot was used to detect HIF-1α， NLRP3， cysteine-aspartic protease-1 （Cleaved caspase-1）， and gasdermin D-N （GSDMD-N） proteins in lung tissue. ResultsCompared to the Control group， the LIRI group showed disordered alveolar structure， thickened alveolar septa， and abundant inflammatory cell infiltration in rats. The lung wet/dry weight ratio， relative fluorescence intensity of GSDMD and NLRP3 double positive cells in lung tissue， IL-1β， IL-18 levels， and HIF-1α， NLRP3， Cleaved caspase-1， and GSDMD-N proteins increased （P0.05）. For the LIRI group， rats in the RMZL low， medium， and high-dose groups displayed attenuated alveolar septal thickening and reduced inflammatory cell infiltration. The lung wet/dry weight ratio， relative fluorescence intensity of GSDMD and NLRP3 double positive cells in lung tissue， IL-1β， IL-18 levels， and HIF-1α， NLRP3， Cleaved caspase-1， and GSDMD-N proteins declined， and the RMZL high-dose group showed the most prominent trend （P0.05）. Compared with the RMZL high-dose group， rats in the RMZL high-dose+DMOG group exhibited thickened alveolar septa and more inflammatory cell infiltration， along with increased lung wet/dry weight ratio， relative fluorescence intensity of GSDMD and NLRP3 double positive cells in lung tissue， levels of IL-1β and IL-18， and protein expression of HIF-1α， NLRP3， Cleaved caspase-1， and GSDMD-N （P0.05）. ConclusionRMZL may inhibit pyroptosis in LIRI rats by suppressing HIF-1α/NLRP3 pathway.

Over the past two decades, genome-wide association study(GWAS) has identified numerous genetic variants and loci associated with heritable diseases. With the gradual maturation and saturation of GWAS methodologies, transcriptome-wide association study(TWAS) offers a novel perspective by linkinggenetic phenotypes to gene expression levels. By integrating TWAS with other multi-omics analyses, researchers can gain a deeper understanding of heritable diseases. This article provides an overview of recent groundbreaking and representative TWAS methods and tools, analyzes their strengths and limitations, and discusses future trends in TWAS development.

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）］

Objective To develop a predictive model for postoperative pulmonary complications (PPC) following video-assisted thoracic surgery (VATS) in lung cancer patients by integrating cardiopulmonary exercise testing (CPET) parameters and machine learning techniques. Methods A retrospective analysis was conducted on patients with early-stage non-small cell lung cancer who underwent CPET and VATS at Guangdong Provincial People’s Hospital between October 2021 and July 2023. Patients were divided into a PPC group and a non-PPC group. The least absolute shrinkage and selection operator (LASSO) regression was used to select important features associated with PPC. Six machine learning algorithms were utilized to construct prediction models, including logistic regression, support vector machine, k-nearest neighbors, random forest, gradient boosting machine, and extreme gradient boosting. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results A total of 325 patients were included, with an average age of 60.36 years, and 55.1% were male. Significant differences were observed between the PPC and non-PPC groups in age, diabetes, coronary heart disease, surgical approach, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FVC% predicted, peak oxygen uptake (peak VO2), anaerobic threshold (AT), and ventilatory equivalent for carbon dioxide slope (VE/VCO2 slope) (P<0.05). In the predictive model constructed by selecting 7 key features using LASSO regression, the random forest model demonstrated the best overall performance across various metrics, with an area under the receiver operating curve of 0.930, an F1 score of 0.836, and a Brier score of 0.133 in the training set. It also exhibited good predictive ability and calibration in the test set. SHAP analysis ranked feature importance as follows: peak VO2, VE/VCO2 slope, age, FEV1, smoking history, diabetes, and surgical approach. Conclusion Integrating CPET parameters, the random forest model can effectively identify high-risk patients for PPC and has the potential for clinical application.

ObjectiveTo construct a multifunctional liposomal delivery system by replacing cholesterol（Chol） in conventional liposomes with saikosaponin D（SSD） and modifying with poloxamer 407（P407） for co-delivery of curcumin（Cur）. The system was evaluated for in vivo tumor targeting and inhibitory effects on mouse subcutaneous solid tumors. MethodsSingle-factor and orthogonal tests combined with information entropy weighting were used to optimize the formulation process of the liposome with encapsulation efficiency and absolute Zeta potential as indexes， and validation studies and liposomal characterization were performed. A subcutaneous solid tumor model was established by injecting H22 hepatocellular carcinoma cells subcutaneously into the dorsal surface of the right forelimb of mice. DiR-loaded traditional Chol liposomes（P407-DiR-Chol-LPs， PDCL） and novel SSD-based liposomes（P407-DiR-SSD-LPs， PDSL） were prepared by the optimized formulation process， and tail vein injection was performed to investigate the impact of SSD on liposome tumor targeting with small animal in vivo imaging. Mice were randomly divided into eight groups， including blank group， model group， free doxorubicin（DOX） group（2 mg·kg^-1）， free Cur group（8 mg·kg^-1）， free SSD group（10 mg·kg^-1）， P407-Cur-Chol-LPs（PCCL） group， P407-SSD-LPs（PSL） group， and P407-Cur-SSD-Lps（PCSL） group. Treatments were administered intraperitoneally every other day for seven doses. Antitumor efficacy and biocompatibility were evaluated by monitoring body weight change， organ indices， tumor volume and mass， relative tumor proliferation rate（T/C）， and tumor growth inhibition rate（TGI）. Histopathological analysis of liver， kidney， and tumor tissues was performed using hematoxylin-eosin（HE） staining. Serum levels of aspartate aminotransferase（AST）， alanine aminotransferase （ALT）， blood urea nitrogen（BUN）， and creatinine（Crea）in mice were quantified by fully automated biochemical analyzer. ResultsOrthogonal test yielded optimal ratios of Cur， SSD， and P407 to soybean phosphatidylcholine（SPC） as 1∶25， 1∶20， and 1∶4. The optimized PCSL exhibited spherical morphology with a particle size of 179.15 nm， a Zeta potential of -47.25 mV， and an encapsulation efficiency of 96.40%. Its in vitro release profile conformed to first-order kinetics， demonstrating excellent storage stability and hemocompatibility. In vivo imaging revealed that the fluorescence signal in tumor tissues and the fluorescence intensity ratio between tumors and organs were significantly higher in the PDSL group than in the PDCL group（P<0.05， P<0.01）. Among the treatment groups， PCSL group showed superior efficacy over free Cur group， free SSD group， PCCL group， and PSL group， with TGI>40% and T/C<60%， indicating pronounced anti-hepatocellular carcinoma effects（P<0.05， P<0.01）. Histopathology and serum biochemistry indicated minimal hepatorenal toxicity and improved hepatic and renal function in PCSL-treated mice. ConclusionReplacing Chol with SSD in preparing multifunctional drug delivery systems not only stabilizes liposomes but also yields superior anti-hepatocellular carcinoma efficacy， achieving the effect of drug-excipient integration. Co-delivery of Cur via this system can be used for treating subcutaneous solid tumors in hepatocellular carcinoma， providing new insights and technical approaches for anti-hepatocellular carcinoma research and the meridian-guiding and messenger-directing theory in traditional Chinese medicine.

Traditional Chinese medicine （TCM） diagnostics is a discipline that studies the basic theories and fundamental skills of diagnostic methods， disease diagnosis， and differentiation in accordance with the theories of TCM. The artificial intelligence （AI） technology has gained remarkable achievements in the intelligentization of the four diagnostic methods in TCM and the standardization of differentiation and diagnosis. However， it still faces many challenges. The standardization of clinical data collection is difficult， and the data quality is uneven， which affects the usability of the data. The integration of the four diagnostic information is insufficient. Most instruments can only collect data from a single diagnostic method， lacking overall integrity. The scientific nature of the diagnostic model needs to be improved. The existing models lack dynamics and the reasoning logic of TCM differentiation. The accuracy of intelligent methods needs to be improved， and the existing evaluation indicators cannot fully reflect the practical application effect of the model. Furthermore， the relevant laws and regulations are still not perfect， and data security and patient privacy lack guarantees. The cultivation of compound talents is insufficient， and there is a lack of interdisciplinary talents who are proficient in both TCM and AI. On this basis， this paper expounded on the current development status， difficulties， and bottlenecks of AI in TCM diagnosis and then explored the development trend of AI in the field of TCM diagnosis. It proposed solutions such as optimizing the data collection process， constructing multimodal diagnostic models， facilitating multi-disciplinary exchanges and cooperation， improving laws and regulations， and cultivating compound talents. It is hoped that modern， standardized， normalized， and intelligent TCM diagnosis can be further promoted， thereby providing new impetus and methods for the inheritance and innovation of TCM.

Objective: To explore the longitudinal association between weight trajectories and physical fitness test composite scores among primary school students, so as to provide empirical evidence for school based weight management and physical health promotion. Methods: A total of 2 112 students from a primary school in Beijing who participated in physical fitness assessments annually from 2008 to 2023 were included and classified into different body weight trajectory groups based on body mass index (BMI) threshold: "normal/overweight-obese". Group based trajectory model was used to identify distinct weight trajectories. Generalized estimating equation and Cox regression were used to analyze the associations between weight trajectories and changes in physical fitness test scores, as well as event risks. Robustness checks were conducted. Results: The prevalence of overweight and obesity among primary school students was 26.50%. BMI showed a significant negative correlation with physical fitness composite scores ( r=-0.19, P <0.01). Four types of weight trajectories were identified: persistent normal group (64.5%, 1 362), persistent overweight- obese group (18.2%, 385), normal to overweight-obese group (14.0%, 296), and overweight-obese to normal group (3.3%, 69). Compared to the persistent normal group, both the normal to overweight-obese and persistent overweight-obese groups showed significantly higher risks of failing the physical fitness test ( HR =4.23, 4.60), and the speed of achieving excellent was slower ( HR = 0.52, 0.40) (all P <0.05). Robustness tests confirmed the stability of the findings. Conclusions Body weight trajectories have a long term impact on physical fitness performance among primary school students. Students with persistent or progressive overweight-obese trajectories have limited score improvement and a higher risk of failure; primary school students with persistent or progressive overweight and obesity should be a key focus.

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.