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.

ObjectiveBiochemistry and Molecular Biology, a discipline that elucidates life phenomena at the molecular level, serves as a core foundational course in medical education. It provides the theoretical basis for studying other basic and clinical medical subjects, as well as for understanding pathogenesis, disease diagnosis, and treatment. However, its complex content and highly abstract concepts have posed a dual challenge to traditional teaching models: “inefficient instruction” and “inadequate learning outcomes”. Within limited classroom hours, how to engage students and stimulate their intrinsic motivation, and how to help them recognize, understand, and develop a passion for biochemistry from the perspective of the discipline’s essence, have long been key focuses of curriculum research. MethodsUsing the lipid metabolism chapter as an example, this study employs “Rain Classroom”, a generative artificial intelligence (AI)-assisted platform, to support education in four dimensions: teaching, learning, evaluation, and research. In teaching, it assists instructors through virtual experiments, lesson preparation support, knowledge mapping, and assignment design. For learning, it serves as an intelligent study assistant for students, providing automated assignment review, enabling educational resource sharing, and facilitating personalized learning pathways. In evaluation, the platform automates assignment grading, analyzes student performance data, and offers diagnostic feedback and teaching recommendations. In research, it aids educators in collecting and analyzing teaching data, as well as searching for and summarizing relevant literature. ResultsThe results indicate that an educational model integrating teacher-led instruction, student-centered learning, and generative AI assistance significantly enhances teaching quality, students’ self-directed learning abilities, and knowledge mastery. Furthermore, with the support of generative AI, curriculum-based ideological education—focusing on cutting-edge disciplinary advances and topical medical issues—helps cultivate students’ medical spirit of “honoring life and healing the wounded”, thereby fostering the establishment of appropriate professional values. Finally, while generative AI presents both opportunities and challenges for higher education, this study also analyzes potential risks in its teaching applications, emphasizing the need for both instructors and students to avoid over-reliance and to ensure that technological tools consistently serve the fundamental goals of education. ConclusionThis study demonstrates that integrating generative AI, specifically via the “Rain Classroom” platform, can effectively enhance biochemistry education. By supporting teaching, learning, evaluation, and research, this approach improves both educational effectiveness and student outcomes. It also facilitates the incorporation of cutting-edge knowledge and professional ethics, nurturing a patient-centered mindset. Additionally, the study addresses potential implementation risks to ensure that such technological tools remain aligned with the core purpose of education.

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 explore the value of healthcare failure mode and effect analysis mode(HFMEA)com-bined with enhanced management of link quality in centralized processing of rigid endoscopic instruments.METHODS Totally 80 pieces of rigid endoscopic instruments that were recycled by the endoscopy room of otolar-yngology department of The First Medical Center of PLA General Hospital from Jan.2022 to Dec.2022 were as-signed as the control group,and 70 pieces of rigid endoscopic instruments that were recycled under the HFMEA model combined with enhanced management of link quality from Jan.2023 to Dec.2023 were assigned as the ex-perimental group.The effect of the management and the qualified rate of instruments were compared between the two groups of instruments,compare the satisfaction under the different intervention modes.RESULTS The scores of instrument disassembly and assembly,cleaning procedures,cleaning quality,environmental management and packaging quality were higher in the experimental group than in the control group(P＜0.05).The unqualified rate of instruments of the experimental group was 10.00％,lower than 23.75％of the control group(x2=4.926,P=0.026).The staff's satisfaction with the work of the experimental group was 91.67％,higher than 66.67％of the control group(Z=2.026,P=0.043).CONCLUSION For the centralized management of rigid endoscopic in-struments,the implementation of the enhanced management of link quality under the HFMEA mode can effective-ly improve the effect of management of the instruments and reduce the unqualified rate of recycled instruments,demonstrating a positive effect on raising the staff's satisfaction with the recycle process of instruments.

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

OBJECTIVE To explore the value of healthcare failure mode and effect analysis mode(HFMEA)com-bined with enhanced management of link quality in centralized processing of rigid endoscopic instruments.METHODS Totally 80 pieces of rigid endoscopic instruments that were recycled by the endoscopy room of otolar-yngology department of The First Medical Center of PLA General Hospital from Jan.2022 to Dec.2022 were as-signed as the control group,and 70 pieces of rigid endoscopic instruments that were recycled under the HFMEA model combined with enhanced management of link quality from Jan.2023 to Dec.2023 were assigned as the ex-perimental group.The effect of the management and the qualified rate of instruments were compared between the two groups of instruments,compare the satisfaction under the different intervention modes.RESULTS The scores of instrument disassembly and assembly,cleaning procedures,cleaning quality,environmental management and packaging quality were higher in the experimental group than in the control group(P＜0.05).The unqualified rate of instruments of the experimental group was 10.00％,lower than 23.75％of the control group(x2=4.926,P=0.026).The staff's satisfaction with the work of the experimental group was 91.67％,higher than 66.67％of the control group(Z=2.026,P=0.043).CONCLUSION For the centralized management of rigid endoscopic in-struments,the implementation of the enhanced management of link quality under the HFMEA mode can effective-ly improve the effect of management of the instruments and reduce the unqualified rate of recycled instruments,demonstrating a positive effect on raising the staff's satisfaction with the recycle process of instruments.

Objective To evaluate the diagnostic performance of the minimum-cost-path-based CT angiography-derived fractional flow reserve(MCP-FFR)and the deep learning-driven CT angiography-derived fractional flow reserve(DeepCL-FFR),and to particularly explore the potential value of the DeepCL algorithm in improving diagnostic accuracy within the"gray zone."Methods A retrospective analysis was conducted on 151 coronary vessels from 109 patients with coronary artery disease,who were hospitalized at the General Hospital of the People's Liberation Army between January 2020 and June 2021.Pearson correlation and Bland-Altman plots were employed to assess the correlation and agreement of the two CT-FFR methods with invasive FFR.A CT-FFR range of 0.70-0.80 was defined as the diagnostic"gray zone."The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value for detecting hemodynamic abnormalities were calculated and analyzed.The DeLong test was used to compare the areas under the receiver operating characteristic curves(AUC)between the two CT-FFR calculation methods.Results Both CT-FFR methods exhibited a positive correlation with invasive FFR(MCP-FFR:r=0.75,P＜0.001;DeepCL-FFR:r=0.86,P＜0.001)and showed good agreement(MCP-FFR:mean difference=0.010,P=0.351;DeepCL-FFR:mean difference=-0.003,P=0.772).Both DeepCL-FFR(AUC 0.97,95％CI 0.94-0.99)and MCP-FFR(AUC 0.92,95％CI 0.88-0.97)demonstrated favorable diagnostic performance for detecting hemodynamic abnormalities(P=0.122).In the"gray zone"for hemodynamic abnormality,the diagnostic accuracy of MCP-FFR was 68.8％,whereas DeepCL-FFR increased it to 89.7％.DeepCL-FFR also exhibited superior diagnostic performance(AUC 0.89,95％CI 0.73-0.99)within the"gray zone,"which was significantly higher than that of MCP-FFR(AUC 0.71,95％CI 0.54-0.87)(P＜0.001).Conclusions The deep learning-driven coronary centerline extraction algorithm,DeepCL,demonstrates superior diagnostic performance in CT-FFR for detecting hemodynamic abnormalities,particularly by significantly improving diagnostic accuracy in the"gray zone."

Objective The mechanism of Huazhuo xingxue decoction(HZXXD)in the treatment of ischemic stroke was explored through network pharmacology,molecular docking and cell validation.Methods TCMSP,TCMID,BATMAN-TCM database and literature search were used to get the chemical components and related target proteins of Huazhuo Xingxue Decoction,and the targets of dementia,stroke and amnesia were obtained from Genecards database and OMIM database.The traditional Chinese medicine-active components-target-network and protein interaction map were constructed by using Cytoscape,and the target was enriched by KEGG pathway by David database.Western blot was used to investigate the effect of HZXXD on inflammation-related core targets expression using oxygen and glucose deprivation/reoxygenation cell model.Finally,Autodock was used for molecular docking of key active ingredients and important targets to evaluate their binding activity.Results 76 active molecules and 33 common targets of herb-disease were screened out.KEGG bioaccumulation results involve multiple inflammatory signal pathways such as TNF,chemical carcinogenesis-reactive oxygen species and HIF-1.TNF-α was found to be the core target of HZXXD by oxygen glucose deprivation/reoxygenation cell experiments.Five compounds with the strongest binding ability to TNF-α,kaempferol,apigenin,aloe-emodin,baicalein and stigasterol,were screened by traditional Chinese medicine-active ingredient-target network map and molecular docking.Conclusion Huazhuo Xingxue Decoction may down regulate the expression of core target TNF-α,kaempferol,apigenin,aloe emodin,baicalein and stigasterol may be the main active substances for TNF-α binding.

Objective To study the effects from varying stenosis degrees of the mesencephalic aqueduct on intracranial cerebrospinal fluid(CSF)flow field.Methods Based on the clinical magnetic resonance image sequences of a male volunteer,a complete normal CSF circulation model was reconstructed by using semi-automated image segmentation technique.Subsequently,eight ideal models representing different stenosis degrees of the mesencephalic aqueduct were manually created.Computational fluid dynamics(CFD)was then performed to simulate the CSF flow field in the nine models.Results The stenosis degree of the mesencephalic aqueduct was positively correlated with the maximum pressure difference between the aqueduct upstream and downstream and the maximum velocity of CSF within the stenosed aqueduct.In the normal model,the maximum pressure difference was 0.84 Pa and the maximum velocity was 11.4 mm/s.While in the maximum stenosed model,the maximum pressure difference and velocity were 21.36 Pa and 60.3 mm/s,respectively.Compared to the normal model,the maximum pressure difference and velocity were approximately increased by 25 times and 5 times,respectively.Moreover,the maximum pressure difference was inversely proportional to the stenosis area square of the aqueduct,and there was a linear relationship between the pressure difference and the quadratic of the maximun CSF velocity.Conclusions The pressure difference and velocity of the stenosed mesencephalic aqueduct was not apparently increased with mild stenosis with respect to the normal aqueduct,while the great aqueductal stenosis increased the risk of hydrocephalus.This study provides a theoretical framework which contributes to understanding the development of obstructive hydrocephalus and intracranial hypertension.

BACKGROUND:The highest incidence of spinal fracture is in the thoracolumbar segment,and its symptoms are back pain,posterior convexity deformity,activity limitation,or with spinal cord nerve injury causing lower limb pain,numbness,and even paraplegia and other complications.The finite element method is a digital computer modeling technique,which can simulate the physical model and carry out force analysis realistically.OBJECTIVE:To review the application of finite element method in thoracolumbar spine fractures.METHODS:We searched the Chinese and English literature databases PubMed,Web of Science,and CNKI for relevant literature on the application of the finite element analysis method in spinal thoracolumbar fracture published before March 2024.The search terms in Chinese and English were:finite element analysis methods,biomechanical phenomena,stress analysis,thoracolumbar fractures,spinal fractures.Finally,55 papers were included.RESULTS AND CONCLUSION:(1)The exploration of thoracolumbar fractures caused by different etiologies(osteoporotic,traumatic,and pathological)through the finite element method is conducive to a deeper understanding of the biomechanics of various types of thoracolumbar fractures,and to improve the individualized and fine-tuned treatment of thoracolumbar fractures.(2)The finite element analysis of a single sample or a small number of samples has the chance,and a larger number of samples are required for the future finite element analysis to reduce the chance caused by the sample.(3)The rigid structure of bones alone cannot meet the biomechanical working conditions of the integrity of the physical object,and future finite element models need to incorporate all the structures of the physical object(e.g.,soft tissues,such as muscles and ligaments)as far as possible.(4)The finite element method has been used in more studies on osteoporotic and traumatic thoracolumbar spine fractures,which will need to be more in-depth in the future,and less in the field of pathologic thoracolumbar fractures,which has a wider scope for future research.