ObjectiveTo explore the mechanism by which Sini San （SNS） inhibits ferroptosis， alleviates inflammation and myocardial injury， and improves myocardial infarction （MI）. MethodsThe active ingredients of SNS were obtained by searching the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） database， its target sites were predicted using the SwissTargetPrediction Database， and the core components were screened out using the CytoNCA plug-in. The targets of MI and ferroptosis were obtained by using GeneCards， Online Mendelian Inheritance in Man （OMIM） database， DrugBank， Therapeutic Target Database （TTD）， FerrDb database and literature review， respectively. The intersection of these targets of SNS-MI-ferroptosis was plotted as a Venn diagram. The protein-protein interaction （PPI） network was constructed using the STRING database， and the visualization graph was prepared using Cytoscape. The core targets were screened out using the CytoNCA plug-in， and the biological functions were clustered by the MCODE plug-in. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed using the David database. Molecular docking was performed using AutoDock and visualized with PyMOL2.5.2. The Kunming mice were randomly divided into the control group， the model group， the SNS group， and the trimetazidine （TMZ） group. The mice were subcutaneously injected with isoprenaline （ISO， 5 mg·kg^-1·d^-1） to establish an MI model. The drug was continuously intervened for 7 days. The ST-segment changes were recorded by electrocardiogram （ECG）， and the tissue morphology changes were observed by hematoxylin-eosin （HE） staining. Cardiomyocyte ferroptosis was investigated by transmission electron microscopy. Serum creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， reduced glutathione （GSH）， and malondialdehyde （MDA） levels were detected by biochemical assay. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of interleukin （IL）-6 and 4-hydroxynonenal （4-HNE）. Immunohistochemical staining was employed to detect IL-6 and phosphorylated signal transducer and transcription activator 3 （p-STAT3） in cardiac tissues. Western blot was used to detect STAT3 and p-STAT3 in cardiac tissues. Real-time PCR was used to detect the levels of IL-6， IL-18， solute carrier family 7 member 11 （SLC7A11）， arachidonic acid 15-lipoxygenase （ALOX15）， and glutathione peroxidase 4 （GPx4） in cardiac tissues. ResultsA total of 121 active ingredients of SNS were obtained， and 58 potential targets of SNS in the treatment of MI by regulating ferroptosis were screened. The three protein modules with a score5 were mainly related to the inflammatory response. The GO function was mainly related to inflammation， and KEGG enrichment analysis showed that SNS mainly regulated ferroptosis- and inflammation- related signaling pathways. Molecular docking indicated that the core component had a higher binding force to the target site. Animal experiments confirmed that SNS reduced the level of p-STAT3 （P0.01）， down-regulated the expression of ALOX15 mRNA （P0.01）， up-regulated the level of serum GSH， and the expressions of SLC7A11 and GPx4 mRNA， reduced MDA and 4-HNE levels （P0.05， P0.01）. Additionally， SNS improved the mitochondrial injury induced by cardiomyocyte ferroptosis， reduced the area of MI， alleviated inflammation and myocardial injury， lowered the levels of serum CK， CK-MB， LDH， IL-6， and the mRNA expression levels of IL-16 and IL-18 （P0.05）， and improved ST segment elevation. ConclusionSNS can reduce ISO-induced STAT3 phosphorylation levels， inhibit ferroptosis in cardiomyocytes， alleviate inflammation and myocardial injury， thereby improving MI.

ObjectiveTo explore the mechanism by which Sini San （SNS） inhibits ferroptosis， alleviates inflammation and myocardial injury， and improves myocardial infarction （MI）. MethodsThe active ingredients of SNS were obtained by searching the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） database， its target sites were predicted using the SwissTargetPrediction Database， and the core components were screened out using the CytoNCA plug-in. The targets of MI and ferroptosis were obtained by using GeneCards， Online Mendelian Inheritance in Man （OMIM） database， DrugBank， Therapeutic Target Database （TTD）， FerrDb database and literature review， respectively. The intersection of these targets of SNS-MI-ferroptosis was plotted as a Venn diagram. The protein-protein interaction （PPI） network was constructed using the STRING database， and the visualization graph was prepared using Cytoscape. The core targets were screened out using the CytoNCA plug-in， and the biological functions were clustered by the MCODE plug-in. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed using the David database. Molecular docking was performed using AutoDock and visualized with PyMOL2.5.2. The Kunming mice were randomly divided into the control group， the model group， the SNS group， and the trimetazidine （TMZ） group. The mice were subcutaneously injected with isoprenaline （ISO， 5 mg·kg^-1·d^-1） to establish an MI model. The drug was continuously intervened for 7 days. The ST-segment changes were recorded by electrocardiogram （ECG）， and the tissue morphology changes were observed by hematoxylin-eosin （HE） staining. Cardiomyocyte ferroptosis was investigated by transmission electron microscopy. Serum creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， reduced glutathione （GSH）， and malondialdehyde （MDA） levels were detected by biochemical assay. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of interleukin （IL）-6 and 4-hydroxynonenal （4-HNE）. Immunohistochemical staining was employed to detect IL-6 and phosphorylated signal transducer and transcription activator 3 （p-STAT3） in cardiac tissues. Western blot was used to detect STAT3 and p-STAT3 in cardiac tissues. Real-time PCR was used to detect the levels of IL-6， IL-18， solute carrier family 7 member 11 （SLC7A11）， arachidonic acid 15-lipoxygenase （ALOX15）， and glutathione peroxidase 4 （GPx4） in cardiac tissues. ResultsA total of 121 active ingredients of SNS were obtained， and 58 potential targets of SNS in the treatment of MI by regulating ferroptosis were screened. The three protein modules with a score5 were mainly related to the inflammatory response. The GO function was mainly related to inflammation， and KEGG enrichment analysis showed that SNS mainly regulated ferroptosis- and inflammation- related signaling pathways. Molecular docking indicated that the core component had a higher binding force to the target site. Animal experiments confirmed that SNS reduced the level of p-STAT3 （P0.01）， down-regulated the expression of ALOX15 mRNA （P0.01）， up-regulated the level of serum GSH， and the expressions of SLC7A11 and GPx4 mRNA， reduced MDA and 4-HNE levels （P0.05， P0.01）. Additionally， SNS improved the mitochondrial injury induced by cardiomyocyte ferroptosis， reduced the area of MI， alleviated inflammation and myocardial injury， lowered the levels of serum CK， CK-MB， LDH， IL-6， and the mRNA expression levels of IL-16 and IL-18 （P0.05）， and improved ST segment elevation. ConclusionSNS can reduce ISO-induced STAT3 phosphorylation levels， inhibit ferroptosis in cardiomyocytes， alleviate inflammation and myocardial injury， thereby improving MI.

ObjectiveTo systematically investigate the discrepancies between employment preferences and actual job placements among postgraduate students in rehabilitation medicine, explore the influencing factors, and elucidate the underlying mechanisms of their employment decisions. MethodsA cross-sectional questionnaire survey was conducted among 100 full-time postgraduate students who had graduated from Capital Medical University School of Rehabilitation in the past six years. A self-designed questionnaire was used to collect information on personal background, self-assessed competencies, employment preferences, and actual job placements. Logistic regression models were employed to analyze the factors influencing employment preferences and actual job placements, respectively. ResultsEmployment preferences among rehabilitation medicine postgraduates were highly concentrated on large tertiary hospitals (79.0%), yet the actual placement rate (61.0%) showed a significant gap. Preference for tertiary hospitals showed no significant association with individual factors such as gender, degree type or self-assessed competencies (P > 0.05). Univariate analysis revealed that those who entered tertiary hospitals scored significantly higher in self-assessed clinical skills, research ability, communication and coordination, and resource integration (|t| > 3.661, P < 0.001), and demonstrated clearer career planning (P = 0.012). Multivariate logistic regression analysis did not identify any independent predictors of actual employment within the context of this study sample (P > 0.05). ConclusionEmployment among rehabilitation medicine postgraduates is characterized by highly homogeneous preferences coexisting with complexity of factors influencing actual employment. It is recommended to optimize the training system, deepen personalized career education, and establish diversified employment guidance mechanisms to promote rational talent flow and facilitate the balanced development of the rehabilitation medical service system.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

OBJECTIVE To construct and validate a prognostic prediction model for first-line immune checkpoint inhibitors （ICIs） therapy for advanced non-small cell lung cancer （NSCLC）.METHODS The clinicopathological data and peripheral blood parameters from 211 patients with advanced NSCLC who received first-line ICIs therapy at our hospital from January 2020 to December 2023 were retrospective collected. The patients were randomly divided into a training set （151 cases） and a validation set（60 cases） at a ratio of 7∶3. Using progression-free survival （PFS） as the outcome measure， LASSO regression was used to preliminarily screen predictive variables in the training set，and the preliminarily screened predictive variables were entered into a multivariable Cox regression model to construct Model 1. Meanwhile，restricted cubic splinec analysis was performed on the continuous variables preliminarily screened by LASSO regression. The variables with nonlinear relationships （ P ＜0.1） were analyzed by multivariable Cox regression in RCS form to construct Model 2. The concordance index，receiver operating characteristic curves，calibration curves，and decision curve analysis were used to compare the predictive performance of the two models； the optimal model was selected to construct a nomogram.Sensitivity analysis was performed by reconstructing the multivariable Cox regression model after excluding cases with unknown programmed death-ligand 1（PD-L1）status. Kaplan-Meier method was used to plot survival curves to analyze the survival differences among patients with different risk strata.RESULTS The variables used to construct Model 1 were sex， smoking history， PD-L1 expression status， bone metastasis， and carcinoembryonic antigen （CEA）；the variables used to construct Model 2 were sex， smoking history， PD-L1 expression status， bone metastasis， CEA， neutrophil（Neu）， and albumin（ALB）. The concordance indices of Model 2 were 0.710 in the training set and 0.672 in the validation set，both superior to those of Model 1 （0.649，0.634）. The areas under the curve （AUC） of Model 2 for 12， 18， and 24 months PFS were 0.737，0.782 and 0.788 in the training set，and 0.709，0.630 and 0.663 in the validation set，respectively，all outperforming Model 1 （0.653， 0.704， 0.697 in the training set；0.688， 0.605， 0.651 in the validation set）.The calibration curves of both models showed good consistency between the predicted probability and actual probability，and the clinical net benefit of Model 2 was higher than that of Model 1. Sensitivity analysis indicated that the prediction model was robust. The median PFS of patients in the low-risk group was significantly longer than that in the high-risk group（ P ＜0.05）.CONCLUSIONS Sex， smoking history， PD-L1 expression status， bone metastasis， Neu， ALB， and CEA were independent influencing factors for PFS in advanced NSCLC patients receiving first-line ICIs. The prediction model constructed based on the above factors has good predictive performance and can be used to predict the prognosis of advanced NSCLC patients.

The compilation explanation for the Expert Consensus on Clinical Application of Ruyi Zhenbao pills systematically expounds the formulation methods and evidence-based basis of this consensus. In response to the weak clinical application evidence and ambiguous indications of Ruyi Zhenbao pills， the Institute of Basic Research in Clinical Medicine of China Academy of Chinese Medical Sciences， Wangjing Hospital of China Academy of Chinese Medical Sciences， and Dongzhimen Hospital of Beijing University of Chinese Medicine invited 39 experts from 31 medical institutions across China to complete the compilation through multi-disciplinary collaboration. The compilation strictly adheres to the World Health Organization （WHO） guidelines for guideline development and the GRADE evidence grading system. The work process covered the construction of clinical questions （a total of 30 clinical questions were determined based on expert voting results）， evidence retrieval （covering Chinese and English databases and gray literature）， safety assessment （integrating drug monitoring data and clinical research）， and the formation of recommendation opinions and consensus suggestions （18 recommendation opinions were reached through the GRADE grid method， and 22 consensus suggestions were reached through majority voting rules）. The results show that the consensus clearly states that this medicine is suitable for knee osteoarthritis， gouty arthritis， rheumatoid arthritis， neuropathic pain， and sequelae of stroke. In terms of safety， the adverse reactions are mainly gastrointestinal reactions such as nausea， vomiting， and abdominal pain. Pregnant women and those allergic to the ingredients of this medicine are contraindicated. The compilation process underwent three rounds of expert reviews and adopted methods such as nominal group technique and Delphi method to ensure the rigor of the consensus compilation process. It provides a paradigm for the formulation of expert consensus on clinical application of Tibetan medicine through the academic promotion and dynamic revision.

Objective To analyze the influencing factors of plastic bronchitis in children with Mycoplasma pneumoniae infection and put forward targeted prevention suggestions. Methods The clinical data of children with Mycoplasma pneumoniae infection who were admitted to Chengdu Third People's Hospital from September 2022 to February 2024 were retrospectively analyzed . According to whether plastic bronchitis occurred, they were divided into plastic group (n=118) and non-plastic group (n=184), and the differences between the two groups were compared and analyzed. Univariate and multivariate logistics regression analysis equations were used to analyze the independent influencing factors of plastic bronchitis in children with mycoplasma pneumoniae infection. Results Among the 302 children with Mycoplasma pneumoniae infection , 118 cases were diagnosed with plastic bronchitis. Analysis showed that the children’s age, duration of fever, hospital stay, pleural effusion rate, number of bronchoscopic lavage, allergy history, endoscopic mucosal erosion rate, WBC, NE%, LY%, CRP, LDH, PCT and D-D were the single factors influencing the occurrence of plastic bronchitis in children with mycoplasma pneumoniae infection. Binary logistics regression analysis revealed that age (OR=2.137, P=0.033, 95% CI: 1.132-16.603), allergy history （OR=3.028, P=0.014, 95% CI: 1.261-864), NE% (OR=2.395, P=0.031, 95% CI: 1.087-5.274), CRP (OR=3.864, P=0.004, 95% CI: 1.563-3.864), PCT (OR=4.125, P=0.001, 95% CI: 1.793-3.864), and D-D (OR=3.920, P=0.002, 95% CI: 1.632-3.864) were independent risk factors for plastic bronchitis in children with mycoplasma pneumoniae infection (P<0.05). Conclusion Age, allergy history, NE%, CRP, PCT and D-D are independent risk factors for plastic bronchitis in children with mycoplasma pneumoniae infection . It is necessary to take clinical intervention measures to reduce the occurrence risk.

Objective: To analyze the epidemilogical and seasonal characteristics of foodborne Salmonella-associated diarrhea among children aged 0-6 years in Guizhou Province from 2016 to 2023, so as to provide a basis for the prevention and control of foodborne diseases. Methods: Data were extracted from the Foodborne Disease Survellance System for cases reported between January 1, 2016, and December 31, 2023. The incidence, seasonal characteristics, and peak periods were analyzed by the method of concentration and circular distribution. Results: A total of 6 434 cases of diarrhea in children aged 0-6 years were collected, and 455 cases of Salmonella were detected, with a positive detection rate of 7.07%. Salmonella typhimurium was the main serotype causing diarrhea (59.34%). The peak of the disease was from May 3 to September 30, with certain seasonal characteristics. The highest detection rate was found in children aged 1-3 years (8.66%). Among food types, the positive detection rates of Salmonella were relatively high in other foods (17.39%), fruits and their products (10.22%), infant and toddler foods (10.09%), and aquatic animals and their products (9.80%). The processing and packaging methods of food were mainly home-made (9.38%) and bulk food (7.54%). Conclusions The detection rate of Salmonella in children aged 0-6 years is high in Guizhou Province, with strong seasonal characteristics. The detection rates of other foods, fruits and their products, infant and toddler foods, and aquatic animals and their products are high. Enhanced pathogen surveillance for susceptible populations and high-risk foods, coupled with public health education during summer/autumn, is recommended.

Objective To explore the influencing factors of cognitive impairment in non-dialysis patients with chronic kidney disease(CKD).Methods A total of 60 hospitalized non-dialysis patients with CKD in the Department of Nephrology of Northern Jiangsu People's Hospital Affiliated to Yangzhou University from September 2022 to September 2023 were enrolled as research objects.According to the estimated glomerular filtration rate(eGFR),they were divided into stage 1 to 2 of CKD group[eGFR ≥60 mL/(min·1.73 m2)]with 23 cases,the stage 3 of CKD group[eGFR 30～＜60 mL/(min·1.73 m2)]with 20 cases,and stage 4 to 5 of CKD group[eGFR＜30 mL/(min·1.73 m2)]with 17 cases.The Montreal Cognitive Assessment Scale(MoCA)was used to evaluate the cognitive function of the patients.Basic data and common clinical laboratory in-dicators on hospital admission were collected to analyze the differences in cognitive function levels under different renal function statuses and to explore the influencing factors of cognitive impairment.Results The incidence rates of cognitive impairment in the stage 1 to 2 of CKD group,stage 3 of CKD group,and stage 4 to 5 of CKD group were 47.8％,85.0％,and 94.1％respectively,the median MoCA scored 26,24 and 20 respectively,with statistically significant between-group differ-ences(P＜0.05).Cognitive function was significantly negatively correlated with age(r=-0.634,P＜0.001),blood urea nitrogen(BUN)(r=-0.574,P＜0.001),serum creatinine(Cr)(r=-0.417,P＜0.001),cystatin C(Cys-C)(r=-0.327,P=0.011),serum β2-microglobulin(β2-MG)(r=-0.259,P=0.046),and N-terminal pro-brain natriuretic peptide(NT-proBNP)(r=-0.474,P＜0.001),and was significantly positively correlated with hemoglobin(HB)(r=0.401,P=0.001)and eGFR(r=0.485,P＜0.001).Multivariate Logistic regression analysis showed that age(P=0.006)and NT-proBNP(P=0.041)were influencing factors of cognitive im-pairment in non-dialysis patients with CKD.Receiver operating characteristic(ROC)curve analysis showed that the area under the curve(AUC),sensitivity,and specificity of age for prediction were 0.860,0.864 and 0.812 respectively,the AUC,sensitivity,and specificity of NT-proBNP for pre-diction were 0.808,0.795 and 0.875 respectively,and the combined prediction of age and NT-proBNP had an AUC,sensitivity,and specificity of 0.893,0.955,and 0.750,respectively.Conclusion As renal function deteriorates,the incidence rate and severity of cognitive impairment in non-dialysis patients with CKD tend to increase.Advanced age,renal function deterioration,high NT-proBNP level,and anemia are associated with the occurrence of cognitive impairment in non-di-alysis patients with CKD,among which age and NT-proBNP are influencing factors for cognitive im-pairment.

Objective:To develop and validate a clinical prediction model for assessing the risk of postoperative pulmonary complications (PPCs) in elderly patients undergoing surgery with general anesthesia.Methods:This prospective observational study enrolled patients aged ≥65 years who underwent general anesthesia with mechanical ventilation duration >3 hours across six tertiary hospitals between December 2022 and August 2023. Based on follow-up outcomes (until discharge or postoperative day 7), patients were categorized into a non-PPCs group and a PPCs group. Detailed records included baseline patient characteristics, preoperative comorbidities, surgical information (type, duration), and bedside lung ultrasound scores (LUS) assessed within 24 hours postoperatively using a standardized 12-zone protocol. Predictor selection was performed using LASSO regression. Significant predictors identified were incorporated into a multivariate logistic regression analysis to build the prediction model, visualized as a nomogram. Internal validation was conducted via bootstrap resampling (1 000 repetitions). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) for discrimination, calibration curves for calibration accuracy, and decision curve analysis (DCA) for clinical utility.Results:A total of 130 eligible elderly surgical patients were included. PPCs occurred in 17 patients (incidence rate: 13.1%). Multivariate analysis identified LUS ( OR=1.248, 95% CI: 1.099-1.417, P=0.001) and elective surgery type ( OR=0.206, 95% CI: 0.043-0.988, P=0.048) as independent predictors of PPCs. The nomogram model demonstrated an AUC of 0.867 (95% CI: 0.775-0.959) upon initial testing. Internal validation confirmed good discrimination (AUC=0.863, 95% CI: 0.778-0.972). Calibration curves indicated excellent agreement between predicted probabilities and observed outcomes. Decision curve analysis demonstrated significant clinical net benefit across a wide range of threshold probabilities (0.03-0.89). Conclusions:The clinical prediction model, developed using early postoperative LUS scores and surgical type, effectively predicts the risk of postoperative pulmonary complications in elderly patients following surgery under general anesthesia. The model exhibits strong discrimination, calibration, and clinical utility, providing clinicians with a reliable tool for individualized risk assessment to support clinical decision-making and potentially reduce PPC incidence.