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.

ObjectiveTo analyze the potential risk of infection sources during the consolidation phase of schistosomiasis elimination in Lushan County (a mountainous region) of Sichuan Province, and to provide prevention and control strategies for the consolidation of schistosomiasis elimination achievements in mountainous areas. MethodsIn 2024, one to two villages with historically severe epidemics were selected in the heavily endemic townships of Lushan County as monitoring sites to carry out monitoring for wild animal feces, domestic animals, wild rats, free-roaming dogs, local residents, and mobile populations. Follow-up monitoring for previously infected individuals was carried out in Caoping Village of Siyan Town. Statistical analyses were performed to assess the risks associated with infection sources. ResultsA total of 115 fecal samples from 13 species of wildlife were incubated, with all negative. Blood tests were conducted on 492 cattle, 34 sheep, and 261 dogs, with all negative. A total of 2 520 rodent traps were set, capturing 67 wild rats with a capture rate of 2.66%, and no positive wild rats were found upon dissection. The activity track of 25 loose dogs was 2 212 meters at the farthest, 236 meters at the nearest, and 744 meters on average. a total of 68 wild droppings (all dog feces) were picked up along the way, with all negative. Blood tests were performed on 3 540 local population, yielding 39 blood-positive cases with a blood positive rate of 1.1%. Fecal examinations were conducted on 193 individuals, with all negative. A total of 315 blood tests were performed on the mobile population, identifying 1 positive case with a blood-positive rate of 0.32%. One person received a fecal examination, with zero positive. A follow-up survey of 505 previously infected individuals revealed 5 blood-positive cases, with a positive rate of 0.99%. Fecal examinations were conducted on 15 individuals, with all negative. ConclusionIn this investigation, no local or imported sources of schistosomiasis transmission were found. However, there was a wide variety and a large number of potential sources of infection in the local area. It is necessary to actively explore regional monitoring models for schistosomiasis elimination flexibly adjust prevention and control strategies according to local conditions, achieve precise prevention and control, and consolidate the achievements of elimination.

Sichuan Province is a hilly schistosomiasis-endemic province in China with the highest endemicity and largest coverage. During the period of more than 70 years of integrated control, there are five distinct phases of schistosomiasis control in Sichuan Province, including baseline investigation, Oncomelania hupensis snail control, chemotherapy for both humans and livestock, management of source of Schistosoma japonicum infections, and sustained control and intensified surveillance and early warning, and a package of key interventions are implemented, including environmental modification, snail control with chemical treatment, large-scale chemotherapy for humans and livestock, management of source of S. japonicum infections, intensified integrated control, establishment of an early warning and surveillance system. As a result, schistosomiasis has been eliminated across all 63 disease-endemic counties (cities, districts) in Sichuan Province by June 2025. Government-led multi-sectoral collaborations, science and technology-supported precision control strategies, and a sustainable control model tailored to the ecological and socio-economic conditions in endemic regions are essential to achieving the target of schistosomiasis elimination in Sichuan Province.

Objective To evaluate the performance of lightweight Chinese large language models (LLMs) in answering specialized lung cancer questions, and to explore the impact of retrieval-augmented generation (RAG) on model performance. Methods Eleven lightweight Chinese LLMs with parameter sizes ranging from 7B to 32B were included. A lung cancer-specific evaluation dataset consisting of 200 questions [100 A1-type (basic knowledge) and 100 A2-type (clinical case) questions], constructed based on clinical guidelines and thoracic surgery textbooks, was used for assessment. Model performance was evaluated under two conditions (with and without RAG). Accuracy was used to assess model performance, and response latency was recorded to reflect inference efficiency. An accuracy–latency scatter plot was constructed for descriptive analysis of overall model performance. Results All models successfully completed the evaluation. With the introduction of RAG, the overall average accuracy improved from 61.68% to 76.36%. Smaller models demonstrated the most significant improvement (e.g., the accuracy of DeepSeek-7B increased from 32.50% to 60.00%, P<0.001). The average response latency increased from 12.58 s to 13.80 s. The Qwen3 series showed the best overall performance, and Qwen3-32B achieved the highest accuracy under both conditions (76.50% and 84.00%, respectively). After RAG integration, performance differences among model families were markedly reduced. Based on the accuracy-latency trade-off, Qwen3-32B achieved the best balance between accuracy and response latency under the baseline condition, whereas Qwen3-14B demonstrated superior overall performance in terms of accuracy, latency, and computational cost after RAG integration. Conclusion The integration of RAG technology improves the ability of lightweight Chinese LLMs to answer specialized lung cancer questions. Under the dual practical constraints of limited computational resources and medical data security requirements, the "lightweight model+RAG" technical framework may represent a promising deployment solution.

This paper systematically analyzes schistosomiasis transmission risks during the elimination stage and reviews the evolution of the schistosomiasis surveillance system in China from a One Health perspective, with a special emphasis on schistosomiasis surveillance and response practices during the elimination stage from a One Health perspective. These practices include precise screening of human schistosomiasis under low-endemicity conditions, systematic monitoring of schistosomiasis in domestic and wild animals, ecological management of Oncomelania snails, and information-coordinated responses. Nevertheless, the current surveillance and response system for schistosomiasis suffers from challenges of insufficient cross-sectoral coordination and complex external environments, which constrain precise schistosomiasis surveillance and response in China. Therefore, it is urgently necessary to integrate coordination mechanisms, empower the surveillance system with technological innovation, improve capacity building, and actively promote diverse social participation in the One Health framework, so as to consolidate current schistosomiasis control achievements and achieve the target for schistosomiasis elimination across China, which may provide a replicable technical model for global zoonoses control.

Objective: To explore the completion status and related factors of preventive anti tuberculosis therapy among close contacts of pulmonary tuberculosis cases in schools across Guizhou Province, so as to provide scientific evidence for optimizing preventive treatment strategies and raising the treatment completion rate among student close contacts. Methods: Demographic, epidemiological and preventive treatment related data were extracted from the Guizhou Disease Prevention and Control Cloud Platform-School Tuberculosis Management System. Totally 3 044 student subjects with latent tuberculosis infection (LTBI) who received the 3HR regimen and had final treatment outcomes between January 1, 2023 and December 31, 2025 were enrolled in the study. The χ 2 test, Mann Whitney U- test and binary Logistic regression model were adopted to analyze factors associated with the completion of preventive treatment among the student LTBI population. Results: The overall completion rate of preventive treatment was 74.24%. The prevention treatment completion rate declined with prolonged medication duration( χ 2 trend =252.09, P <0.01). Binary Logistic regression analysis indicated that students from private schools had higher odds of incomplete treatment compared with those from public schools ( OR =1.32, P <0.05). Compared with college students, students in primary school and below, junior high school, senior high school, secondary vocational school exhibited lower odds of incomplete treatment ( OR =0.35, 0.31, 0.59, 0.17,all P <0.05). Compared with contacts exposed in dormitories, classroom exposed and same floor or others exposed contacts showed higher odds of incomplete treatment ( OR =4.28, 8.59, both P <0.05). Participants with an interval exceeding 2 weeks from screening to treatment initiation were more likely to complete preventive treatment ( OR =0.80, P <0.05). Conclusions The overall completion rate of preventive treatment remains unsatisfactory among students with LTBI in Guizhou Province. Multiple interventions are recommended to improve treatment adherence and completion rates in the population, including implementing diversified medication supervision measures, strengthening public health management and supervision for private schools, establishing efficient referral and management mechanisms to enhance students treatment completion.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Objective To systematically evaluate the risk prediction models for anastomotic leakage (AL) in patients with esophageal cancer after surgery. Methods A computer-based search of PubMed, EMbase, Web of Science, Cochrane Library, Chinese Medical Journal Full-text Database, VIP, Wanfang, SinoMed and CNKI was conducted to collect studies on postoperative AL risk prediction model for esophageal cancer from their inception to October 1st, 2023. PROBAST tool was employed to evaluate the bias risk and applicability of the model, and Stata 15 software was utilized for meta-analysis. Results A total of 19 literatures were included covering 25 AL risk prediction models and 7373 patients. The area under the receiver operating characteristic curve (AUC) was 0.670-0.960. Among them, 23 prediction models had a good prediction performance (AUC>0.7); 13 models were tested for calibration of the model; 1 model was externally validated, and 10 models were internally validated. Meta-analysis showed that hypoproteinemia (OR=9.362), postoperative pulmonary complications (OR=7.427), poor incision healing (OR=5.330), anastomosis type (OR=2.965), preoperative history of thoracoabdominal surgery (OR=3.181), preoperative diabetes mellitus (OR=2.445), preoperative cardiovascular disease (OR=3.260), preoperative neoadjuvant therapy (OR=2.977), preoperative respiratory disease (OR=4.744), surgery method (OR=4.312), American Society of Anesthesiologists score (OR=2.424) were predictors for AL after esophageal cancer surgery. Conclusion At present, the prediction model of AL risk in patients with esophageal cancer after surgery is in the development stage, and the overall research quality needs to be improved.

Surgical treatment is one of the key approaches for non-small cell lung cancer (NSCLC). Regular postoperative follow-up is crucial for early detection and timely management of tumor recurrence, metastasis, or second primary tumors. A scientifically sound and reasonable follow-up strategy not only extends patient survival but also significantly improves quality of life, thereby enhancing overall prognosis. This consensus aims to build upon the previous version by incorporating the latest clinical research advancements and refining postoperative follow-up protocols for early-stage NSCLC patients based on different treatment modalities. It provides a scientific and practical reference for clinicians involved in the postoperative follow-up management of NSCLC. By optimizing follow-up strategies, this consensus seeks to promote the standardization and normalization of lung cancer diagnosis and treatment in China, helping more patients receive high-quality care and long-term management. Additionally, the release of this consensus is expected to provide insights for related research and clinical practice both domestically and internationally, driving continuous development and innovation in the field of postoperative management for NSCLC.

Acupuncture treatment of chronic pain combined with depression (CPDC) is the result of a multi-target, multi-pathway approach. Acupuncture can treat CPDC by inhibiting the activation of glial cells, regulating the release of inflammatory mediators, regulating the expressions of neurotransmitters, changing the plasticity of neural synapses, regulating related epigenetic effects, regulating the microbiota-brain-gut axis, inhibiting nerve cell apoptosis, and antagonizing oxidative stress. The mechanism of its effect mainly involves anti-inflammatory related signaling pathways, regulation of neural synapse-related signaling pathways, and exerts its therapeutic effect through hippocampus, cerebral cortex, and amygdala.