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 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.

Background The positive rate of work-related musculoskeletal disorders (WMSDs) among coal mine workers remains high, which seriously affects the quality of life of the workers. Objective To estimate the prevalence of WMSDs among coal miners in Shanxi Province and analyze their influencing factors. Methods From May to December 2023, 2315 coal miners from a coal mine enterprise inShanxi Province were selected by convenience sampling, and the self-administered Basic Situation Survey Scale, Musculoskeletal Disorders Questionnaire, Effort-Reward Imbalance Questionnaire, and Pittsburgh Sleep Quality Scale were used to evaluate general demographic characteristics, work organization characteristics, living habits, sleep quality, occupational stress, and occurrence of WMSDs symptoms and the corresponding absences (sickness absence) in past 1 year. The positive rate and absenteeism rate of WMSDs were caculated, and Pearson's chi-square test was used for identify influencing factors of WMSDs, and finally a binary logistic regression method was used to further studying the factors. Results A total of 2577 questionnaires were distributed in this survey, and 2315 valid questionnaires were recovered, with an effective recovery rate of 89.9%. The positive rate of WMSDs symptoms was 48.7% (1127/2315), the absenteeism rate due to WMSDs was 22.6% (523/2315), and the waist (17.5%), neck (11.7%), and knee (9.0%) were the most common areas presenting WMSDs symptoms. The results of logistic regression showed that compared with ≤10 years of service, the risk of WMSDs was higher for those with > 20 years of service (OR=2.167, 95%CI: 1.419, 3.311). Compared with the daily working time of ≤8 h, the risk of WMSDs was higher for those >8 h daily working time (OR=1.463, 95%CI: 1.211, 1.767). A higher risk of WMSDs was reported in workers with moderate labor intensity (OR=1.247, 95%CI: 1.009, 1.542) or heavy labor intensity (OR=1.570, 95%CI: 1.215, 2.027) than those with light labor intensity. The higher the education level (OR _{high school and technical secondary school}=1.866, 95%CI: 1.435, 2.425; OR _{junior college/bachelor degree or above}=1.953, 95%CI: 1.445, 2.639), the higher the risk of WMSDs. The risk of WMSDs was higher in workers reporting smoking (OR=1.225, 95%CI: 1.021, 1.470) or alcohol consumption (OR=1.345, 95%CI: 1.122, 1.612). The risk of WMSDs in coal miners with high occupational stress (OR=1.229, 95%CI:1.030, 1.466) or those with sleep disorders (OR=3.616, 95%CI:2.824, 4.629) was higher than that in workers with low occupational stress or no sleep disorders respectively. Conclusion The positive rate of WMSDs among coal mine personnel in Shanxi Province is high, and education level, length of service, daily working hours, smoking, alcohol consumption, labor intensity, sleep disorders, and occupational stress are influencing factors for WMSDs.

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 assess the professional knowledge proficiency of mainstream large language models (LLMs) in medical laboratory education and to explore their potential as educational aids for medical laboratory technology students.Methods:A comprehensive evaluation was conducted using 400 authentic questions from the 2023 Chinese National Clinical Medical Laboratory Technician Qualification Examination. Five LLMs (Copilot, Grok, Yuanbao, Doubao, and Kimi) were tested through two-round interactions using zero-shot prompting and interaction-optimized prompting strategies. The accuracy of answers and the quality of generated content were evaluated. Performance disparities were analyzed using Cochran's Q test. Content quality was scored through the CLEAR framework (completeness, lack of false information, evidence-based reasoning, appropriateness, relevance).Results:In the first-round test, Doubao achieved the highest overall accuracy rate (375/400). The overall accuracy rates of Doubao and Yuanbao significantly outperformed Copilot and Kimi ( P<0.001). After the second-round interactive optimization, the accuracy rate of Kimi significantly improved ( P<0.05), whereas other LLMs showed slight improvements ( P>0.05). Doubao still had the highest overall accuracy rate (380/400). The overall accuracy rates of Doubao and Yuanbao significantly outperformed Copilot ( P<0.005). Evaluation based on the CLEAR framework revealed that Yuanbao, Doubao, and Kimi significantly outperformed foreign models in the dimensions of evidence-based reasoning ( P<0.003) and completeness ( P<0.05), demonstrating standardized citation of authoritative evidence and superior content quality. Conclusions:The tested LLMs possess extensive medical laboratory knowledge. The accuracy of their answers and the quality of the generated content can be improved through single-question input, specifying evidence requirements, and enabling advanced reasoning functions. Domestic LLMs are comparable to foreign LLMs in terms of accuracy, and have significant advantages in the dimensions of evidence-based reasoning and completeness. LLMs can serve as auxiliary tools for learning professional knowledge in medical laboratory technology.

Objective:To analyze the surveillance results of brucellosis in Shanxi Province, to gain a understanding of the epidemic characteristics and changing trend of brucellosis in Shanxi Province, and to provide scientific basis for formulating prevention and control strategies and measures for brucellosis.Methods:The surveillance data of brucellosis in Shanxi Province from Infectious Disease Reporting Information Management System of China Disease Prevention and Control Information System, and the active surveillance results of 5 national brucellosis surveillance sites from 2018 to 2023 were collected for descriptive analysis.Results:From 2018 to 2023, a total of 24 041 cases of human brucellosis were reported in Shanxi Province, with the number of cases increased from 2 800 in 2018 to 5 131 in 2023. The annual reported incidence was 11.22/100 000, ranging from 7.56/100 000 to 14.74/100 000, showing an overall upward trend (χ 2trend = 1 377.43, P < 0.001). The epidemic had affected all counties (cities and districts) in the province, mainly concentrated in Linfen City, Jinzhong City, Datong City, and Shuozhou City, with a total of 13 489 cases of human brucellosis reported, accounting for 56.11%. The population distribution was mainly composed of individuals aged 40 - 69 (17 682 cases), males (18 513 cases), farmers (20 295 cases), and household and unemployed individuals (1 017 cases). The onset of the disease showed obvious seasonality, with the peak time from March to August, accounting for 66.39% (15 961/24 041). A total of 14 130 occupational individuals were investigated at the surveillance sites, and 11 141 underwent serological tests, with a positive rate of 5.25% (585/11 141), including 299 new cases. The positive rate of serological tests was relatively high in Qinyuan County, at 8.51% (241/2 832). Etiological culture was conducted on 338 human blood samples, and 43 strains of Brucella were isolated, with a detection rate of 12.72%. All strains were Brucella melitensis, including 42 strains of Brucella melitensis biovar 3. Conclusions:The overall trend of human brucellosis epidemic in Shanxi Province from 2018 to 2023 is on the rise, and it is still at a high level. It is suggested to continue strengthening monitoring, study the epidemic situation in a timely manner, take comprehensive prevention and control measures, and effectively control the spread of brucellosis.

Objective:To investigate the effect of a blended teaching mode based on medical knowledge graph and artificial intelligence (AI) teaching assistant on students' learning effectiveness and systematic thinking ability in teaching of pathogens and immunology.Methods:A controlled experimental design was employed, involving 114 clinical medical students ("5+3" integrated) enrolled in 2023 at Tianjin Medical University. They were divided into experimental group ( n=57) and control group ( n=57) based on their classes. A blended online-offline teaching mode based on medical knowledge graph and AI teaching assistant was used in the experimental group, and a blended online-offline teaching mode based on the online resources of this course was used in control group. Teaching effectiveness was assessed by comparing the scores of four chapter quizzes and the final exam between the two groups, as well as by analyzing student questionnaire responses. Data were analyzed using t test and χ2 test. Results:The experimental group achieved significantly higher scores in all four chapter quizzes and the final exam compared to the control group [(78.77±19.65) vs. (69.47±22.95), (84.56±14.02) vs. (76.49±16.20), (81.89±13.60) vs. (73.13±16.52), (81.56±21.28) vs. (73.16±16.27), (69.75±13.30) vs. (64.10±14.93), all P<0.05]. The questionnaire survey showed that 74.07% students ( n=40) believed that this blended teaching model stimulated their learning interest, 74.07% ( n=40) students believed that this blended teaching model enhanced their learning initiative, 81.48% ( n=44) students believed that this blended teaching model could help them construct the relationship between the old and new knowledge and thus improved memory retention, and 81.48% students b( n=44) elieved that this blended teaching model helped them integrate and summarize knowledge and construct systematic knowledge framework to improve their learning effectiveness. Conclusions:The blended teaching mode based on medical knowledge graph and AI teaching assistant is beneficial for cultivating students' systematic thinking ability and is helpful to improve their learning efficiency and effectiveness.