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）］

BackgroundAnxiety disorder is a common mental disorder， with its prevalence showing a continuous upward trend， significantly affecting the quality of life and social function of patients. Due to the lack of objective and reliable biomarkers in clinical practice， the early identification and treatment of anxiety disorder have been somewhat limited. Plasma proteins have the potential to serve as biomarkers for mental diseases， however， the causal relationship between them and anxiety disorder remains unclear. ObjectiveTo identify the plasma proteins that have a causal relationship with anxiety disorders， and to elucidate the associated biological pathways， in order to provide references for the search for biomarkers of anxiety disorders and the exploration of potential therapeutic targets. MethodsBased on the protein quantitative trait locus （pQTL） data of 4 907 plasma proteins covering 35 559 Icelandic individuals from the deCODE database， and the genome-wide association studies （GWAS） data of 50 486 patients with anxiety disorders and 330 460 healthy controls， the inverse-variance weighted （IVW） method was used as the main analysis method， supplemented by MR-Egger method， weighted median method， simple model method， and weighted model method for bidirectional Mendelian randomization analysis. Enrichment analysis of gene ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathways was conducted for the related proteins. Sensitivity analysis was performed using Cochran's Q test， MR-Egger intercept test， MR-PRESSO test， and leave-one-out analysis to evaluate the robustness of the results. ResultsA total of 10 plasma proteins were identified as significantly associated with anxiety disorders. Among these， SPATA9 （OR=0.856， 95% CI： 0.784–0.934， P<0.01） and PDE5A （OR=0.911， 95% CI： 0.864–0.961， P<0.01） were identified as protective factors， while CRYGD （OR=1.209， 95% CI： 1.095–1.334， P<0.01）， BTN3A3 （OR=1.045， 95% CI： 1.018–1.073， P<0.01）， SERPINB13 （OR=1.102， 95% CI： 1.040–1.168， P<0.01）， ERBB4 （OR=1.283， 95% CI： 1.109–1.484， P<0.01）， LSAMP （OR=1.096， 95% CI： 1.037–1.158， P<0.01）， ICOSLG （OR=1.283， 95% CI： 1.104–1.490， P<0.01）， DNAJB11 （OR=1.172， 95% CI： 1.076–1.277， P<0.01）， and TREML1 （OR=1.115， 95% CI： 1.054–1.179， P<0.01） were identified as risk factors. The sensitivity analysis showed that the results were robust， with no heterogeneity （Cochran's Q test P>0.05） or pleiotropy （MR-Egger intercept test P>0.05）. Enrichment analysis indicated that these plasma proteins were enriched in biological processes such as T-cell signal transduction， lymphocyte proliferation， cell membrane structure and synaptic function， as well as the intestinal immune network that produces IgA and the ErbB signaling pathway. ConclusionThis study identified 10 plasma proteins associated with anxiety disorders. The functions of these plasma proteins involve multiple biological processes such as neural development and immune regulation.

Objective To systematically evaluate the efficacy and safety of video-assisted thoracoscopic surgery (VATS) with different numbers of ports in the treatment of spontaneous pneumothorax. Methods We conducted a comprehensive search of CNKI, PubMed, The Cochrane Library, Web of Science, EMbase, Wanfang Data, and the Chinese Medical Journal Full-text Database for clinical controlled trials on VATS with different port numbers for spontaneous pneumothorax, from their inception to March 2023. Two researchers independently screened the literature and assessed its quality.The Newcastle-Ottawa Scale (NOS) was used to assess the methodological quality of cohort and case-control studies, and the Cochrane risk-of-bias tool was used to evaluate randomized controlled trials (RCT). Meta-analysis was performed using RevMan 5.4.1 software. Results A total of 107 studies were included, comprising 35 RCT, 2 cohort studies, and 70 case-control studies. All cohort and case-control studies included in the analysis had NOS scores≥7. The meta-analysis revealed that compared to two-port VATS (2P-VATS) and three-port VATS (3P-VATS), single-port thoracoscopic surgery (SPTS) was associated with less intraoperative blood loss (SMD=–1.58, 95%CI: –1.93 to –1.22, P<0.001; and SMD=–1.59, 95%CI: –2.03 to –1.14, P<0.001, respectively), shorter postoperative hospital stay (SMD=–1.05, 95%CI: –1.29 to –0.82, P<0.001; and SMD=–1.08, 95%CI: –1.39 to –0.77, P<0.001), shorter duration of postoperative chest tube drainage (SMD=–0.75, 95%CI: –1.00 to –0.50, P<0.001; and SMD=–1.23, 95%CI: –1.72 to –0.75, P<0.001), fewer postoperative complications (OR=0.34, 95%CI: 0.26 to 0.45, P<0.001; and OR=0.47, 95%CI: 0.33 to 0.68, P<0.001), and lower pain scores at 24, 48, and 72 hours after surgery (P<0.05). The operative time for SPTS was shorter than that for 2P-VATS (SMD=–0.53, 95%CI: –0.90 to –0.16, P=0.005) but showed no significant difference compared to 3P-VATS (P=0.21). When comparing 2P-VATS with 3P-VATS, 2P-VATS demonstrated less intraoperative blood loss (SMD=–1.02, 95%CI: –1.81 to –0.22, P=0.01), shorter postoperative hospital stay (SMD=–0.59, 95%CI: –1.11 to –0.06, P=0.03), shorter duration of chest tube drainage (SMD=–0.46, 95%CI: –0.85 to –0.08, P=0.02), fewer postoperative complications (OR=0.36, 95%CI: 0.22 to 0.59, P<0.001), and lower pain scores at 24, 48, and 72 hours after surgery (P≤0.05). Conclusion Both SPTS and 2P-VATS are effective and safe surgical options for spontaneous pneumothorax, deserving further promotion and application in clinical practice. However, due to limitations in the quantity and quality of the included studies, more large-sample, high-quality research is needed to validate these findings.

Objective: To develop a dual-branch deep learning framework for accurate multi-label classification of fundus diseases, addressing the key limitations of insufficient complementary feature extraction and inadequate cross-modal feature fusion in existing automated diagnostic methods. Methods: The fundus multi-label classification dataset with 12 disease categories (FMLC-12) dataset was constructed by integrating complementary samples from Ocular Disease Intelligent Recognition (ODIR) and Retinal Fundus Multi-Disease Image Dataset (RFMiD), yielding 6 936 fundus images across 12 retinal pathology categories, and the framework was validated on both FMLC-12 and ODIR. Inspired by the holistic multi-regional assessment principle of the Five Wheels theory in traditional Chinese medicine (TCM) ophthalmology, the dual-branch multi-label network (DBMNet) was developed as a novel framework integrating complementary visual feature extraction with pathological correlation modeling. The architecture employed a TransNeXt backbone within a dual-branch design: one branch processed red-green-blue (RGB) images to capture color-dependent features, such as vascular patterns and lesion morphology, while the other processed grayscale-converted images to enhance subtle textural details and contrast variations. A feature interaction module (FIM) effectively integrated the multi-scale features from both branches. Comprehensive ablation studies were conducted to evaluate the contributions of the dual-branch architecture and the FIM. The performance of DBMNet was compared against four state-of-the-art methods, including EfficientNet Ensemble, transfer learning-based convolutional neural network (CNN), BFENet, and EyeDeep-Net, using mean average precision (mAP), F1-score, and Cohen's kappa coefficient. Results: The dual-branch architecture improved mAP by 15.44 percentage points over the single-branch TransNeXt baseline, increasing from 34.41% to 44.24%, and the addition of FIM further boosted mAP to 49.85%. On FMLC-12, DBMNet achieved an mAP of 49.85%, a Cohen’s kappa coefficient of 62.14%, and an F1-score of 70.21%. Compared with BFENet (mAP: 45.42%, kappa: 46.64%, F1-score: 71.34%), DBMNet outperformed it by 4.43 percentage points in mAP and 15.50 percentage points in kappa, while BFENet achieved a marginally higher F1-score. On ODIR, DBMNet achieved an F1-score of 85.50%, comparable to state-of-the-art methods. Conclusion DBMNet effectively integrates RGB and grayscale visual modalities through a dual-branch architecture, significantly improving multi-label fundus disease classification. The framework not only addresses the issue of insufficient feature fusion in existing methods but also demonstrates outstanding performance in balancing detection across both common and rare diseases, providing a promising and clinically applicable pathway for standardized, intelligent fundus disease classification.

Objective To investigate the prognostic value of preoperative plasma fibrinogen (FIB) combined with lymphocyte-to-monocyte ratio (LMR) in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC). Methods A retrospective analysis was conducted on ESCC patients who underwent esophagectomy in the Affiliated Lihuili Hospital of Ningbo University from 2015 to 2018. Based on the cut-off values of preoperative FIB and LMR, the F-LMR scoring system was constructed, and patients were divided into three groups according to the F-LMR score. Kaplan-Meier analysis was used to assess 5-year overall survival (OS) and 5-year progression free survival (PFS), and univariate and multivariate Cox regression analyses were performed to identify prognostic factors. Results Finally 260 patients were collected, including 237 males and 23 females, with a median age of 64 years (IQR: 59-70). The 5-year OS rates for patients with F-LMR score of 0, 1, and 2 were 24.44%, 51.69%, and 67.31%, respectively, and the 5-year PFS rates were 15.56%, 42.37%, and 57.62%, respectively. Lower preoperative F-LMR scores were associated with worse prognosis. Multivariate analysis showed that deeper tumor invasion, presence of lymph node metastasis, larger tumor maximum diameter, and lower preoperative F-LMR score were independent risk factors for OS. Conclusion The F-LMR scoring system based on the preoperative FIB and LMR may serve as an effective tool for predicting the prognosis of patients with ESCC.

Objective: To analyze the school tuberculosis (TB) outbreaks and preventive treatment in Hefei from 2022 to 2024, so as to provide reference for TB prevention and control in schools. Methods: Data were collected on all school based TB outbreaks occurring during 2022-2024 in Hefei, defined as ≥2 epidemiologically linked TB cases within the same school during a single semester. Statistical analyses were performed using the Chi square test. Results: Close contacts exhibited significantly higher TB incidence (2.88%) and latent mycobacterium tuberculosis infection (LTBI) rates (13.80%) in the school TB outbreaks, compared to non close contacts (0.12% and 2.63%, respectively). Among close contacts, secondary school students showed lower TB incidence (0.48%) and LTBI prevalence (3.42%) than both primary school or younger children (0.68%, 6.95%) and college students ( 0.78% , 6.50%), with statistically significant differences ( χ 2=360.91, 6.37; 791.71, 102.03, all P <0.05). The proportion of LTBI individuals recommended for preventive therapy was higher in primary school or younger groups (98.59%) than in secondary (95.25%) or college students (86.34%) ( χ 2=25.86, P <0.01). However, among those recommended, close contacts had higher uptake (85.82%) and completion rates (87.25%) of preventive therapy than non close contacts (69.63% and 70.57%); similarly, secondary school students demonstrated higher uptake (91.21%) and completion rates (86.45%) compared to primary school or younger (88.57%, 83.87%) and college students (57.28%, 64.08%) ( χ 2=30.52, 26.72; 125.17, 38.84, all P <0.01). Subsequent TB incidence among LTBI close contacts (13.30%) and among those who did not complete preventive therapy (22.73%) were significantly higher than among non close contacts (2.80%, 2.41%), respectively ( χ 2=32.19, 13.87, both P <0.05). Conclusions In school TB outbreaks, close contacts face higher LTBI prevalence and subsequent TB risk than non close contacts. College students show notably low adherence to preventive therapy. It is necessary to take targeted measures to improve the compliance of preventive measures among students.

The development of the rare disease discipline is a crucial pathway for enhancing the diagnosis and treatment of rare diseases, cultivating specialized professionals, and fostering technological innovation. Currently, China' rare disease discipline is accelerating its development driven by both policy and demand. However, it still faces multi-dimensional challenges, including an incomplete clinical management mechanism, a shortage of interdisciplinary talents, a weak scientific research system, and limited outreach capacity. To address these challenges, this paper proposes and constructs an integrated development system with clinical diagnosis and treatment as the foundation, talent cultivation as the engine, scientific research as the support, and disciplinary outreach capacity as the extension. Specific strategies include: enhancing clinical management through artificial intelligence-assisted diagnosis systems and multidisciplinary collaboration platforms; strengthening the talent pool through textbooks, curricula, and hierarchical training mechanisms; bolstering research collaboration and translational outcomes by leveraging international data-sharing platforms, national rare disease medical centers, the State Key Laboratory of Complex Severe and Rare Diseases, and the National Key Scientific Infrastructure for Translational Medicine; and expanding grassroots outreach and public awareness through the National Rare Disease Diagnosis and Treatment Collaboration Network, the National Rare Disease Quality Control Center, and integrated media communication channels. In the future, the rare disease discipline should further deepen the integration of medicine and engineering, expand international cooperation, focus on the translational closed loop, improve the regional collaboration network, so as to build a more resilient and dynamic disciplinary ecosystem, and ultimately achieve a comprehensive improvement in the diagnosis and treatment of rare diseases.

To evaluate the application value of the Peyton four-step teaching method in the standardized training of intestinal ultrasound and compare it with traditional teaching methods, so as to provide an optimized approach for clinical ultrasound training.

OBJECTIVE To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in pediatric patients with malignant tumors， evaluate their modeling strategies， key predictors， and predictive performance， and provide evidence-based references for clinical decision-making and research. METHODS A literature search was conducted across 11 databases， including CNKI， Wanfang Data， and PubMed， for relevant studies published before April 2025. Two reviewers independently performed literature screening and data extraction， and the risk of bias and applicability of the models were evaluated using the PROBAST tool. RESULTS Ultimately， seven studies were selected， of which four were English articles and three were Chinese articles， involving 12 risk prediction models. Although model discrimination was good （AUC 0.748-0.981）， only two models underwent external validation； furthermore， calibration was inadequately reported in three studies. PROBAST indicated that all models exhibited a high risk of bias， with major issues including a predominance of retrospective designs， inadequate sample representativeness， and lack of blinding. However， in terms of applicability， all models received favorable evaluations. In terms of modeling methods， most studies employed traditional logistic regression approaches to construct models， while only a minority introduced machine learning algorithms and conducted systematic comparisons among multiple algorithms. Models developed using machine learning methods significantly outperformed those constructed with traditional statistical methods. CONCLUSIONS The existing risk prediction models for myelosuppression after chemotherapy in children with malignant tumors demonstrate potential in clinical risk early warning. However， they generally suffer from design and methodological limitations， such as a predominance of retrospective single-center designs， few events per variable， opaque handling of missing data， and inconsistent reporting of model coefficients. Future studies should adopt prospective designs， incorporate machine learning with key clinical predictors， and follow TRIPOD reporting guidelines to enhance scientific rigor and clinical utility.

ObjectiveBased on the theory of "spleen governing transportation and transformation"， this study investigates the efficacy of Atractylodis Macrocephalae Rhizoma processed with Aurantii Fructus Immaturus juice（AMR-AFI） in improving slow-transit constipation（STC）， as well as the synergistic regulatory mechanism involving the microbiota-metabolism axis， thereby elucidating the scientific basis of its processing theory. MethodsAnimals were randomly divided into the control group， model group， positive drug（mosapride） group（3 mg·kg^-1）， and low-， medium-， and high-dose groups of AMR-AFI（3.9， 7.8， 15.6 g·kg^-1）. Except for the control group， the remaining five groups were induced with STC using loperamide hydrochloride. Following modeling， interventions were administered. All groups received continuous administration for 15 d， during which fecal samples， colon tissue， and serum were collected. Constipation improvement was assessed by measuring fecal moisture content and small intestinal propulsion rate， histological morphology of colonic tissue was observed via hematoxylin-eosin（HE） staining， and the levels of interleukin（IL）-6， tumor necrosis factor（TNF）-α， and IL-2 in serum were detected using enzyme-linked immunosorbent assay（ELISA）. Furthermore， the microbial community structure in mouse feces was analyzed by 16S rRNA sequencing， while transcriptomic sequencing was employed to screen differentially expressed genes in colonic tissue， followed by gene ontology（GO） and Kyoto Encyclopedia of Genes and Genomes（KEGG） enrichment analyses. Finally， Spearman correlation analysis was conducted to explore the association between differential microbiota and differential genes. ResultsCompared with the control group， the intestinal propulsion rate and fecal moisture content in the model group were significantly decreased（P<0.01）， while serum levels of IL-6， TNF-α， and IL-2 were significantly elevated（P<0.01）. HE staining showed damage and shedding of colonic mucosal epithelial cells， along with a reduction in goblet cells in the model group. In comparison with the model group， all treatment groups improved the pathological state of the colonic mucosa to varying degrees and reduced serum levels of IL-6， TNF-α， and IL-2（P<0.01）. Among these， the high-dose group of AMR-AFI significantly increased the intestinal propulsion rate and fecal moisture content of rats（P<0.05， P<0.01）. Further transcriptomic analysis revealed that a total of 104 differentially expressed genes were identified from comparisons between the model group and the control group， as well as between the model group and the high-dose group of AMR-AFI. These genes were mainly enriched in pathways closely related to STC pathogenesis， such as arachidonic acid metabolism and aldosterone-regulated sodium reabsorption. 16S rRNA sequencing results indicated that AMR-AFI reversed the structural imbalance of the gut microbiota in model mice， increased species richness， downregulated the relative abundance of pro-inflammatory bacteria such as Parasutterella， and enriched beneficial and butyrate-producing bacteria， including Lachnospiraceae_NK4A136_group， Ruminococcaceae， and Lachnospiraceae. Spearman correlation analysis further showed that the beneficial bacteria enriched in the AMR-AFI group were negatively correlated with genes involved in the arachidonic acid metabolic pathway and positively correlated with genes in the aldosterone-regulated sodium reabsorption pathway. In contrast， pro-inflammatory bacteria in the model group exhibited the opposite correlation trends. ConclusionAMR-AFI can effectively exert synergistic therapeutic effects on STC by regulating intestinal microbiota， arachidonic acid-mediated inflammatory metabolism， and aldosterone-regulated water-salt balance pathways.