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 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.

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.

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 degree of intervertebral disc degeneration above and below the vertebral body in pilots with lumbar spondylolysis.Methods:The medical records of 66 pilots who underwent lumbar imaging examinations at the Air Force Medical Center between September 2011 and January 2025 were retrospectively analyzed. The degree of intervertebral disc degeneration was compared between 33 pilots with lumbar spondylolysis and another 33 age-matched pilots without spondylolysis. The spondylolysis group was divided into subgroups with/without spondylolisthesis and unilateral/bilateral subgroups. The degree of disc degeneration above and below the vertebral body was compared between these subgroups using the modified Pfirrmann grading system.Results:The modified Pfirrmann scores of the discs above and below the spondylolytic vertebral body in the spondylolysis group were significantly higher than those at the corresponding segments in the non-spondylolysis group ( Z=-2.39, -4.41, P=0.017,<0.001). In pilots with spondylolysis accompanied by spondylolisthesis, the modified Pfirrmann score of the disc below the slipped vertebral body was significantly higher than that in pilots without spondylolisthesis ( Z=-3.02, P=0.003). However, there was no statistically significant difference in the modified Pfirrmann score of the disc above the slipped vertebral body between pilots with and without spondylolisthesis ( P>0.05). No significant differences were observed in the modified Pfirrmann scores of the discs above and below the vertebral body between pilots with unilateral and bilateral spondylolysis (both P>0.05). Conclusions:Pilots with lumbar spondylolysis exhibit severe intervertebral disc degeneration above and below the affected vertebral body. Spondylolisthesis can continue to exacerbate degeneration in the disc inferior to the affected vertebra.

Generative Artificial Intelligence ishows a broad application prospect in the field of healthcare and has become an important technical means to promote the development of medical informatization.It addresses the multi-faceted challenges of medical record documentation,including efficiency,quality,and doctor-patient communica-tion.It analyzes the adaptability and feasibility of Generative Artificial Intelligence in different clinical scenarios of intelli-gent medical record generation.Additionally,it explores the issues present in current applications and proposes corre-sponding solutions,providing references for the effective application and continuous optimization of Generative Artifi-cial Intelligence in medical record documentation.This provides a theoretical foundation for further expanding the appli-cation scenarios of automatic medical record documentation in China's healthcare industry.

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.

Generative Artificial Intelligence ishows a broad application prospect in the field of healthcare and has become an important technical means to promote the development of medical informatization.It addresses the multi-faceted challenges of medical record documentation,including efficiency,quality,and doctor-patient communica-tion.It analyzes the adaptability and feasibility of Generative Artificial Intelligence in different clinical scenarios of intelli-gent medical record generation.Additionally,it explores the issues present in current applications and proposes corre-sponding solutions,providing references for the effective application and continuous optimization of Generative Artifi-cial Intelligence in medical record documentation.This provides a theoretical foundation for further expanding the appli-cation scenarios of automatic medical record documentation in China's healthcare industry.

Objective:To analyze the degree of intervertebral disc degeneration above and below the vertebral body in pilots with lumbar spondylolysis.Methods:The medical records of 66 pilots who underwent lumbar imaging examinations at the Air Force Medical Center between September 2011 and January 2025 were retrospectively analyzed. The degree of intervertebral disc degeneration was compared between 33 pilots with lumbar spondylolysis and another 33 age-matched pilots without spondylolysis. The spondylolysis group was divided into subgroups with/without spondylolisthesis and unilateral/bilateral subgroups. The degree of disc degeneration above and below the vertebral body was compared between these subgroups using the modified Pfirrmann grading system.Results:The modified Pfirrmann scores of the discs above and below the spondylolytic vertebral body in the spondylolysis group were significantly higher than those at the corresponding segments in the non-spondylolysis group ( Z=-2.39, -4.41, P=0.017,<0.001). In pilots with spondylolysis accompanied by spondylolisthesis, the modified Pfirrmann score of the disc below the slipped vertebral body was significantly higher than that in pilots without spondylolisthesis ( Z=-3.02, P=0.003). However, there was no statistically significant difference in the modified Pfirrmann score of the disc above the slipped vertebral body between pilots with and without spondylolisthesis ( P>0.05). No significant differences were observed in the modified Pfirrmann scores of the discs above and below the vertebral body between pilots with unilateral and bilateral spondylolysis (both P>0.05). Conclusions:Pilots with lumbar spondylolysis exhibit severe intervertebral disc degeneration above and below the affected vertebral body. Spondylolisthesis can continue to exacerbate degeneration in the disc inferior to the affected vertebra.

Objective To explore the role of PANoptosis in the pathogenesis of ulcerative colitis(UC),and to study the regulatory mechanism of Xianglian pills on panapoptotic pathway related proteins in UC mice.Methods Forty-eight C57BL/6 mice were randomly divided into 6 groups:normal group,model group,mesalazine group,low,medium and high dose of Xiangli-an pills group.Except the normal group,the other 5 groups were given 3％dextran sulfate(DSS)for 1 week,respectively,to pre-pare experimental UC mice model.At the same time,starting from the first day of modeling,the corresponding groups were giv-en mesalazine or Xianglian pills by intragastric administration,respectively,for continuous treatment for 1 week.On the 8th day,colon tissues of mice in each group were taken and partially fixed for HE staining and pathological observation.TUNEL method was used to detect the apoptosis of colon tissue cells,and the contents of IFN-γ,TNF-α,IL-1β and IL-6 in colon were detected by enzyme-linked immunosorbent assay(ELISA).Western blot was used to detect the expression of Bax,Bcl-2,Caspase-8,p-MLKL,p-RIPK3,Caspase-1,GSDMD-N and IL-1β in colon tissues.Results Compared with model group,disease activity index(DAI)and degree of colon tissue damage decreased in high-dose Xianglian pills group(both P＜0.01),and the levels of IFN-γ,TNF-α,IL-1β and IL-6 in UC mice decreased(all P＜0.01),and colon lengths and body weights increase(both P＜0.01).In ad-dition,apoptosis of UC mice was inhibited in the high-dose group by downregulating Bax/Bcl-2 ratio(P＜0.01),and necroptosis was inhibited by increasing Caspase-8 protein level and decreasing p-MLKL and p-RIPK3 protein levels(all P＜0.01).Pyropto-sis was inhibited by decreasing the expression levels of Caspase-1,GSDMD-N and IL-1β(all P＜0.01).Conclusion Xianglian pills can relieve DSS induced ulcerative colitis in mice by regulating apoptosis,necroptosis and pyroptosis.