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.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

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.

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.

OBJECTIVE: To evaluate the clinical efficacy and safety of Yangxue Qingnao Pills (YXQNP) combined with amlodipine in treating patients with grade 1 hypertension. METHODS: This is a multicenter, randomized, double-blind, and placebo-controlled study. Adult patients with grade 1 hypertension of blood deficiency and Gan (Liver)-yang hyperactivity syndrome were randomly divided into the treatment or the control groups at a 1:1 ratio. The treatment group received YXQNP and amlodipine besylate, while the control group received YXQNP's placebo and amlodipine besylate. The treatment duration lasted for 180 days. Outcomes assessed included changes in blood pressure, Chinese medicine (CM) syndrome scores, symptoms and target organ functions before and after treatment in both groups. Additionally, adverse events, such as nausea, vomiting, rash, itching, and diarrhea, were recorded in both groups. RESULTS: A total of 662 subjects were enrolled, of whom 608 (91.8%) completed the trial (306 in the treatment and 302 in the control groups). After 180 days of treatment, the standard deviations and coefficients of variation of systolic and diastolic blood pressure levels were lower in the treatment group compared with the control group. The improvement rates of dizziness, headache, insomnia, and waist soreness were significantly higher in the treatment group compared with the control group (P<0.05). After 30 days of treatment, the overall therapeutic effects on CM clinical syndromes were significantly increased in the treatment group as compared with the control group (P<0.05). After 180 days of treatment, brachial-ankle pulse wave velocity, ankle brachial index and albumin-to-creatinine ratio were improved in both groups, with no statistically significant differences (P>0.05). No serious treatment-related adverse events occurred during the study period. CONCLUSIONS Combination therapy of YXQNP with amlodipine significantly improved symptoms such as dizziness and headache, reduced blood pressure variability, and showed a trend toward lowering urinary microalbumin in hypertensive patients. These findings suggest that this regimen has good clinical efficacy and safety. (Registration No. ChiCTR1900022470).
Humans ; Amlodipine/adverse effects* ; Drugs, Chinese Herbal/adverse effects* ; Male ; Female ; Hypertension/complications* ; Middle Aged ; Treatment Outcome ; Drug Therapy, Combination ; Adult ; Blood Pressure/drug effects* ; Double-Blind Method ; Aged ; Antihypertensive Agents/adverse effects*

AIM: To explore the relationship between hyperopia reserve and ocular biometric parameters in 4-14 year-old Uyghur students from Hotan County, Xinjiang, and to provide scientific evidence for myopia prevention.METHODS: From September 1 to October 31, 2023, a stratified random cluster sampling method was used to select 3 264 students(3 264 eyes)from 6 schools in Hotan County. Participants underwent uncorrected distance visual acuity testing, cycloplegic refraction, and ocular biometric measurements. The correlation between spherical equivalent(SE)and ocular biometric parameters was analyzed by multiple linear regression.RESULTS: A total of 1 998 non-myopic students(1 998 eyes)were included in the study, with 1 354 students(67.77%)showing insufficient hyperopia reserve. The detection rate of insufficient hyperopia reserve decreased with age, from 94.12% at age 4 to 18.13% at age 14(P<0.001). Multiple linear regression analysis showed that in the group with sufficient hyperopia reserve, age, gender, uncorrected distance visual acuity, axial length(AL), and keratometry(K)explained 66.5% of the variance in SE; while in the group with insufficient hyperopia reserve, these factors explained only 28.0% of the SE variance.CONCLUSION: In non-myopic Uyghur students aged 4-14 in Hotan County, Xinjiang, the detection rate of insufficient hyperopia reserve was 67.77%. In the group with insufficient hyperopia reserve, age, gender, AL, and K explained only a small portion of the SE variance, suggesting that the refractive status of this population may be influenced by more complex factors.