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.

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.

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 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.

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 investigate the distribution and hantavirus (HV) carrying state in host animals of hemorrhagic fever with renal syndrome (HFRS) in Henan Province from 2019 to 2022.Methods:Host animal monitoring was carried out at the monitoring sites of HFRS in Henan Province. The real-time fluorescence quantitative PCR was used to detect hantavirus in rat lungs. The types of hantavirus were analyzed. The positive samples were sequenced and then sequence homology and variation were analyzed.Results:A total of 1 308 rodents were captured from 2019 to 2022, 16 specimens of rat lungs tested positive for hantavirus nucleic acid. The positive rate of HV was 1.22% (16/1 308). According to type, the positive rate of HV in Apodius agrarius was the highest (68.75%, 11/16). According to distribution, the positive rate of HV in field samples was the highest (2.50%, 12/480), and the positive rate of HV in residential samples was 0.53% (4/759). The typing results of 16 positive samples showed that all viruses were hantavirus type Ⅰ (hantaan virus). The positive samples were sequenced and eight S gene fragments (GenBank number: OQ681444-OQ681451) and six M gene fragments (OQ681438-OQ681443) were obtained. The S and M gene fragments were similar to the Shaanxi 84FLi strain and Sichuan SN7 strain. Phylogenetic analysis of S and M gene fragments showed that they all belonged to the hantaan virus-H5 subtype. Amino acid sequence analysis revealed that, compared with the hantaan virus vaccine strain 84FLi, the 74th amino acid encoded by eight S fragments was replaced by aspartamide with serine. Tryptophan was replaced by glycine at the 14th position of Gn region in XC2022047, and isoleucine was replaced by alanine at the 359 position of XC2022022 and XC2022024.Conclusion:The hantavirus carried by host animals in Henan Province from 2019 to 2022 belongs to the type Ⅰ (hantaan virus), and Apodemus agrarius is still the dominant host animal of the hantaan virus. Compared with the vaccine strains, amino acid sites are replaced in the immune epitopes of the S and M gene fragments.