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.

Objective To explore the wearing status and actual noise reduction effect of hearing protectors among noise workers in a typical automobile manufacturing enterprise. Methods In April 2024, an occupational hazard factor testing was carried out in an automobile manufacturing industry, and at the same time, the hearing protection fit test was conducted for noise workers. Intervention and guidance were provided to those who did not pass the minimum standard of baseline PAR. The difference in PAR between baseline and post-intervention was compared, and the effectiveness of hearing protector wearing method training was evaluated. Results The exceeding rate of the company's noise operation post was 50.77% (66/130). The baseline PAR of the subjects with working experience of less than 15 years and wearing hearing protectors throughout noisy work was higher, and the differences were statistically significant (P<0.05). Compared with those with 80dB≤L_{EX, 8h}<85dB, more research subjects with L_{EX, 8h}≥85dB failed baseline PAR (39.13%). After intervention, the PAR of the subjects who did not pass the minimum standard of baseline PRA increased from 2.0 (0.0, 5.3) to 17.0 (14.8, 20.0), and the protection level was significantly improved, and the difference was statistically significant (P<0.01). Conclusion The individual fit test of hearing protector is an important means to evaluate the actual noise reduction level of hearing protector and guide the selection of hearing protection models. Corporate training can help improve the PAR of hearing protectors.

The clustered regularly interspaced short palindromic repeats (CRISPR)/associated protein 9 (CRISPR /Cas9) immune system is an adaptive immune system widely distributed in bacteria and archaea. It precisely defends against invasion by exogenous phages, viruses, and plasmids through sequence-specific endogenous immune response mechanisms. As the most prominent member of this family, the CRISPR/Cas9 system has evolved into the most widely applied, flexible, and efficient technical platform in the field of genome engineering due to its exceptional genome modification capabilities. Within the CRISPR/Cas9 system, the Cas9 protein, precisely guided by a single-stranded guide RNA (gRNA), can specifically recognize target DNA sequences and induce double-strand breaks. This activates the cell’s DNA repair mechanisms, enabling gene knockout, knock-in, or modification. Demonstrating significant advantages in specificity, flexibility, and operability, CRISPR/Cas9 technology has shown immense potential in the medical field, opening new avenues for modernizing traditional Chinese medicine (TCM) research. On one hand, this technology can be used to construct precise disease models and tailor personalized treatment plans. It enables in-depth elucidation of the molecular mechanisms underlying the action targets and signaling pathways of TCM formulas and active components, thereby unraveling the scientific secrets of their complex mechanisms of action. On the other hand, it demonstrates powerful tool value in improving TCM germplasm resources, identifying and screening superior varieties, evaluating the controllability of TCM quality, and producing innovative drugs, providing technical support for the standardization and precision of TCM. Simultaneously, the high-throughput omics data generated by CRISPR technology is driving artificial intelligence (AI) to construct virtual disease models and drug prediction systems. This empowers the intelligent screening of effective TCM components, the precise prediction of potential targets, and the exploration of “reducing toxicity while enhancing efficacy” through formula combinations. This synergistic innovation between CRISPR and AI aligns perfectly with precision medicine’s urgent demand for personalized, efficient drug development, injecting new momentum into the modernization and transformation of TCM. This paper first systematically reviews and explains the developmental trajectory, structural basis, and action mechanisms of the CRISPR/Cas9 system, tracing its scientific evolution from a bacterial immune system to a gene-editing tool. It then comprehensively outlines the current state of convergence between precision medicine concepts and modernization research in TCM, analyzing the synergistic points and potential spaces for their integration. Against the backdrop of rapid precision medicine advancement, this paper emphasizes how CRISPR/Cas9 gene editing technology empowers in-depth analysis of TCM mechanisms—including specific applications in disease model construction, therapeutic target validation, and multi-target network regulation studies. It further elaborates on its multidimensional practical contributions to modernizing TCM, spanning key domains such as germplasm resource innovation, bioactive compound biosynthesis, quality standardization control, and novel TCM drug development. Finally, this paper envisions the future landscape of deep integration between CRISPR technology and AI: from data-driven intelligent drug screening to high-throughput precision discovery of effective TCM components, and further to intelligent model construction based on “reducing toxicity while enhancing efficacy” mechanisms. The synergistic convergence of these multidimensional technologies will pioneer new scientific paradigms and translational pathways for TCM modernization, propelling TCM toward leapfrogging development in the era of precision medicine.

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.

Cryo-electron tomography (cryo-ET) enables the determination of high-resolution three-dimensional structures of macromolecular complexes within cells in a near-physiological state, providing crucial structural insights into fundamental life processes. Cryo-ET has achieved landmark successes in single-cell models. However, many critical biological processes do not occur in isolated cells but emerge from intercellular coordination within tissues. Furthermore, many research subjects, including neural tissues, tumor biopsies, plant tissues, and clinical pathological samples, cannot be obtained through single-cell culture and must be directly dissected from organisms or tissue blocks. Advancing cryo-ET from single-cell to tissue-level applications is therefore crucial for capturing the full complexity of biological activities in their native context. A major technical bottleneck for tissue cryo-ET lies in the preparation of sufficiently thin (<300 nm) lamellae from vitrified tissue specimens. Although high-pressure freezing can vitrify tissues up to 200 µm thick, these samples are far too thick for direct transmission electron microscopy imaging. Among the available thinning methods, cryo-focused ion beam (cryo-FIB) milling has emerged as the most promising approach, as it avoids the mechanical artifacts inherent to cryo-ultramicrotomy. However, conventional on-the-grid cryo-FIB milling is inefficient for thick tissues, requiring excessive milling time and discarding most of the sample. To overcome these limitations, cryo-lift-out has been developed—a technique in which a micromanipulator physically extracts a chunk of interest from deep within the tissue and transfers it to a dedicated grid for final thinning. This approach bypasses the thickness barrier and enables site-specific analysis of internal structures. This review systematically traces the evolution of cryo-lift-out from its origins in materials science to its adaptation for biological tissues. In room-temperature lift-out, reliable attachment is achieved by gas-injection system (GIS)-assisted metal deposition. Transferring this approach to cryogenic conditions proved challenging because precursor gases condense on all cold surfaces, leading to contamination and poor adhesion. The development of copper-assisted redeposition marked a critical turning point: instead of relying on gas deposition, this method uses ion-beam sputtering to deposit copper atoms at the needle-chunk interface, creating a strong, low-contamination bond. This innovation has enabled robust cryo-lift-out workflows and paved the way for serial lift-out, in which multiple consecutive lamellae are prepared from a single tissue chunk, substantially increasing throughput and enabling volumetric imaging. Despite these advances, several technical challenges remain. Curtaining effects caused by uneven chunk surfaces can introduce artifacts into tomograms, requiring careful optimization of milling parameters and protective coating. The cryo-adhesion step still demands precise control of beam angle, needle positioning, and milling depth, making the process highly operator-dependent. Additionally, the choice of grid geometry is critical. Custom-designed grids with double-sided attachment improves stability and offer better compatibility with cryo-ET tilt series. Automation, which has greatly improved room-temperature lift-out, has not yet been achieved for cryo-lift-out due to the complexity of handling heterogeneous biological tissues and the need for real-time adaptation. Future progress will likely focus on integrating cryo-lift-out with volume electron microscopy to correlate ultrastructure across scales, developing intelligent control systems to reduce user intervention, and extending the technology to challenging samples such as plant tissues and some material science samples for interface study. A systematic analysis of the cryo-lift-out technique clarifies the key limiting factors for its large-scale application and lays a foundation for methodological refinement and technological innovation. By consolidating recent advances and identifying remaining bottlenecks, this review aims to support the broader adoption of cryo-lift-out and accelerate the development of tissue-scale in situ structural biology.

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 assess the risk of hearing loss caused by occupational noise exposure in workers in an automobile manufacturing plant in Tianjin, China, and to perform risk management. Methods Occupational health field investigation and noise exposure measurements were conducted from July to December 2023, and physical examination data were collected. ISO 1999:2013(E) Acoustics-Estimation of Noise-Induced Hearing Loss and WS/T 754-2016 ^“Guidelines for Risk Management of Occupational Disease Hazards Caused by Noise^” were used to predict the risk of high-frequency hearing loss and occupational noise induced deafness for operational workers and make a risk classification. Results The noise intensity of each workshop was 79.4 to 95.5 dB(A), and the maximum noise intensity of welding and stamping exceeded the standard. The results of the assessment showed that the noise level remained unchanged, and the risk of HFHL and ONID in workers increased as the predicted age and length of service increased. It was predicted that after the age of 40, the maximum risk of hearing loss in welding workers would be high risk, and the risk of stamping workers would be at higher risk, suggesting that welding and stamping were the key control posts of noise hazards in the enterprise. The N50 prediction values of permanent hearing threshold displacement caused by potential noise at all frequencies for final assembly and painting workers were lower than the measured values. Conclusion The consequences of hearing loss for workers in the welding and stamping shop noise operations at this automobile manufacturing plant are relatively serious and require risk management.

Objective To analyze the influencing factors of malaria infection of labor service exported to overseas in Langfang City, in order to establish a visualization tool to assist clinicians in predicting the risk of malaria. Methods A total of 4 774 expatriate employees of the Nibei Pipeline Project of the Pipeline Bureau from October 2021 to August 2023 were taken as the subjects, and the gender, age, overseas residence area and Knowledge of malaria controlscores of the study subjects were investigated by questionnaire survey, and the possible risk factors of malaria were screened by logistic regression model. At the same time, the nomogram prediction model was established, and the subjects were divided into the training group and the validation group at a ratio of 2:1, and the area under the curve (ROC) and the decision curve were plotted to evaluate the prediction ability and practicability of the prediction model in this study. Results Among the 4 774 study subjects, 96 cases of malaria occurred, and the detection rate was 2.01%. Junior school （OR=1.723，95% CI:1.361-2.173）， and residence in rural areas（OR=2.091，95%CI:1.760 -3.100）were risk factors (OR>1), while protective measures（OR=0.826，95% CI : 0.781 - 0.901） and high malaria education scores （OR=0.872，95% CI : 0.621 - 0.899）were protective factors.The nomogram prediction model results showed that the area under the curve of the nomogram prediction model in the training group was 0.94 (95% CI : 0.85 - 1.00), while the validation group was 0.93 (95% CI : 0.80 - 1.00). The results of the decision curve showed that when the threshold probability of the population was 0-0.9, the nomogram model was used to predict the risk of malaria occurrence with the highest net income. Conclusion The nomogram prediction model (including gender, education, region, protection and malaria education score) established and validated in this study is of great value for clinicians to screen high-risk patients with malaria.

The lacrimal gland organoids are innovative in vitro cultured tissue model that mimics the lacrimal gland, retaining its original histological and molecular biological properties. This model can more accurately reproduce the physiological environment of the lacrimal gland, including its ductal system and tear film protein secretion. It offers a new platform for studying the physiopathological basis of the lacrimal gland, establishing disease models, conducting regenerative medicine applications, and performing drug screening. Currently, organoids technology is continuously evolving, with ongoing updates to the methods for in vitro culturing of the lacrimal gland. These advancements gradually address challenges related to cultivation complexity, cost, and time, demonstrating a wide range of application potential. In this paper, we summarize the latest progress in lacrimal gland organoids research both domestically and internationally, exploring the development of lacrimal gland organoids, 3D construction technologies, and their potential for clinical applications, in order to provide new insights for clinical research on lacrimal gland-related diseases and to promote broader application of lacrimal gland organoids in drug development and personalized diagnosis and treatment.

The lacrimal gland organoids are innovative in vitro cultured tissue model that mimics the lacrimal gland, retaining its original histological and molecular biological properties. This model can more accurately reproduce the physiological environment of the lacrimal gland, including its ductal system and tear film protein secretion. It offers a new platform for studying the physiopathological basis of the lacrimal gland, establishing disease models, conducting regenerative medicine applications, and performing drug screening. Currently, organoids technology is continuously evolving, with ongoing updates to the methods for in vitro culturing of the lacrimal gland. These advancements gradually address challenges related to cultivation complexity, cost, and time, demonstrating a wide range of application potential. In this paper, we summarize the latest progress in lacrimal gland organoids research both domestically and internationally, exploring the development of lacrimal gland organoids, 3D construction technologies, and their potential for clinical applications, in order to provide new insights for clinical research on lacrimal gland-related diseases and to promote broader application of lacrimal gland organoids in drug development and personalized diagnosis and treatment.