Lung cancer is a leading cause of cancer-related morbidity and mortality worldwide. Coupled with the substantial workload, the clinical management of lung cancer is challenged by the critical need to efficiently and accurately process increasingly complex medical information. In recent years, large language models (LLMs) technology has undergone explosive development, demonstrating unique advantages in handling complex medical data by leveraging its powerful natural language processing capabilities, and its application value in the field of lung cancer diagnosis and treatment is continuously increasing. The paper systematically analyzes that the exceptional potential of LLMs in lung cancer auxiliary diagnosis, tumor feature extraction, automatic staging, progression/outcome analysis, treatment recommendations, medical documentation generation, and patient education. However, they face critical technical and ethical challenges including inconsistent performance in complex integrated decision-making (e.g., TNM staging, personalized treatment suggestions) and "black box" opacity issues, along with dilemmas such as training data biases, model hallucinations, data privacy concerns, and cross-lingual adaptation challenges ("data colonization"). Future directions should prioritize constructing high-quality multimodal corpora specific to lung cancer, developing interpretable and compliant specialized models, and achieving seamless integration with existing clinical workflows. Through dual drivers of technological innovation and ethical standardization, LLMs should be prudently advanced for holistic lung cancer management processes, ultimately promoting efficient, standardized, and personalized diagnosis and treatment practices.

Objective:To investigation the situation of water improvement projects in villages affected by drinking water-type endemic fluorosis in Qinghai Province and the prevalence of dental fluorosis among children, in order to provide a basis for consolidating the achievements in prevention and control of drinking water-type endemic fluorosis and adjusting prevention and control measures.Methods:The monitoring data on drinking water-type endemic fluorosis were collected from the disease prevention and control centers in various counties of Qinghai Province from 2021 to 2023, the situation of water improvement projects, the fluorine content of domestic drinking water and the prevalence of dental fluorosis in children aged 8 to 12 years old were retrospectively analyzed.Results:From 2021 to 2023, the numbers of villages affected by drinking water-type endemic fluorosis in Qinghai Province were 338, 335, and 328, respectively. The numbers of water improvement projects were 125, 127 and 124, respectively. The normal operation rates were 100%, 100% and 99.19% (123/124), respectively. The qualified rates of water fluoride level were 100%, 99.21% (126/127) and 99.19% (123/124), respectively. The detection rates of dental fluorosis among children aged 8 to 12 were 4.34% (515/11 877), 5.70% (646/11 331) and 4.48% (490/10 943), respectively. There was a statistically significant difference in the detection rate of dental fluorosis among children in different years (χ 2 = 22.79, P < 0.001). Conclusions:The overall operation status of water improvement project in villages affected by drinking water-type endemic fluorosis in Qinghai Province is generally good, but there has been some relaxation in management and maintenance in the later stage, and there is a phenomenon of project intermittency. The detection rate of dental fluorosis among children aged 8 to 12 remains low, and endemic fluorosis caused by drinking water is under continuous control.

Objective:To analyze the differences in determination of fluoride level in drinking water by ion-selective electrode method and high-throughput rapid determination method.Methods:The precision test was carried out by using the two methods to measure two kinds of fluoride standard substances, water samples of external quality control assessment from 2021 to 2023 (two kinds each year) and the fluoride level in three drinking water samples (for 5 times/each sample). Accuracy testing was conducted by measuring the external quality control assessment water samples and the spiked recovery rates drinking water, and water samples were grouped (water fluoride ≤1.00, > 1.00 mg/L) and analyzed according to the "Hygienic Standards for Drinking Water" (GB 5749-85). SPSS 23.0 software was used for statistical analysis of the measurement results.Results:(1) The correlation coefficients ( r) of the working curves of the two methods were both > 0.990, meeting the quality control requirements. (2) In the precision test, when comparing the results of the two methods for detecting two kinds of fluoride standard substances, there was no statistically significant difference ( F = 0.36, 0.15, P = 0.564, 0.707), and the coefficients of variation ( CV) were all < 5%. The CV of the detection results of the external quality control assessment water samples and drinking water samples were < 5%. (3) In the accuracy test, when the fluoride concentration in water was ≤1.00 mg/L, there was no statistically significant difference in the spiked recovery rates between the two methods ( F = 0.49, P = 0.504). When the fluoride concentration in water was > 1.00 mg/L, there was a statistically significant difference in the spiked recovery rates between the two methods ( F = 24.75, P = 0.003). Conclusions:The ion-selective electrode method has the advantages of wide detection range and wide adaptability, while the high-throughput rapid determination method has high accuracy. Testing personnel can weigh and choose the appropriate determination method based on the actual laboratory conditions and sample concentration range.

OBJECTIVE To provide standardized whole-process guidance on drug traceability codes for medical institutions in Sichuan province， ensuring medication safety and compliance with medical insurance supervision requirements. METHODS Based on evidence-based principles and expert consensus， Expert Consensus on Whole-process Management of Drug Traceability Codes in Medical Institutions of Sichuan Province （hereinafter referred to as the Consensus） was formulated through systematic literature review， field investigations， establishment of a multidisciplinary expert committee and multiple rounds of questionnare consultation via the modified Delphi method， and finalized through consensus meetings. RESULTS & CONCLUSIONS The Consensus clarifies key operating procedures for code verification， code assignment and code return， whole-process operational standards for drug warehouse acceptance and storage， drug warehouse outbound delivery and pharmacy acceptance check， drug distribution and dispensing in pharmacy and intravenous admixture center， medication administration in nursing units and examination departments， as well as drug return process. Key recommendations are proposed such as improving the core functions of the drug traceability system， unifying the hospital-wide traceability code database， strengthening the management of traceability codes for backup medications， establishing a management organization and institutional framework， and optimizing the architectural design and data governance requirements of the drug traceability system. The release of the Consensus will provide scientific， standardized and implementable practical guidelines for medical institutions of Sichuan province， helping to improve closed-loop management of the drug traceability system， strengthen medication safety and fulfil medical insurance fund supervision.

Intestinal microecological changes are closely related to liver cirrhosis and cirrhosis-related sarcopenia.Studies has demonstrated that interventions targeting the intestinal microbiota could contribute to the treatment of cirrhosis and cirrhosis-related sarcopenia.Here we reviewed the research progress on the alterations in intestinal microbiota during liver cirrhosis and the underlying mechanisms by which these changes impacted the development of the disease.The potential of microbiota-targeted interventions in both preventing and treating liver cirrhosis and related sarcopenia was also discussed,which might provide valuable insights into clinical diagnosis and management of the disease.

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 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 investigation the situation of water improvement projects in villages affected by drinking water-type endemic fluorosis in Qinghai Province and the prevalence of dental fluorosis among children, in order to provide a basis for consolidating the achievements in prevention and control of drinking water-type endemic fluorosis and adjusting prevention and control measures.Methods:The monitoring data on drinking water-type endemic fluorosis were collected from the disease prevention and control centers in various counties of Qinghai Province from 2021 to 2023, the situation of water improvement projects, the fluorine content of domestic drinking water and the prevalence of dental fluorosis in children aged 8 to 12 years old were retrospectively analyzed.Results:From 2021 to 2023, the numbers of villages affected by drinking water-type endemic fluorosis in Qinghai Province were 338, 335, and 328, respectively. The numbers of water improvement projects were 125, 127 and 124, respectively. The normal operation rates were 100%, 100% and 99.19% (123/124), respectively. The qualified rates of water fluoride level were 100%, 99.21% (126/127) and 99.19% (123/124), respectively. The detection rates of dental fluorosis among children aged 8 to 12 were 4.34% (515/11 877), 5.70% (646/11 331) and 4.48% (490/10 943), respectively. There was a statistically significant difference in the detection rate of dental fluorosis among children in different years (χ 2 = 22.79, P < 0.001). Conclusions:The overall operation status of water improvement project in villages affected by drinking water-type endemic fluorosis in Qinghai Province is generally good, but there has been some relaxation in management and maintenance in the later stage, and there is a phenomenon of project intermittency. The detection rate of dental fluorosis among children aged 8 to 12 remains low, and endemic fluorosis caused by drinking water is under continuous control.

Objective:To analyze the differences in determination of fluoride level in drinking water by ion-selective electrode method and high-throughput rapid determination method.Methods:The precision test was carried out by using the two methods to measure two kinds of fluoride standard substances, water samples of external quality control assessment from 2021 to 2023 (two kinds each year) and the fluoride level in three drinking water samples (for 5 times/each sample). Accuracy testing was conducted by measuring the external quality control assessment water samples and the spiked recovery rates drinking water, and water samples were grouped (water fluoride ≤1.00, > 1.00 mg/L) and analyzed according to the "Hygienic Standards for Drinking Water" (GB 5749-85). SPSS 23.0 software was used for statistical analysis of the measurement results.Results:(1) The correlation coefficients ( r) of the working curves of the two methods were both > 0.990, meeting the quality control requirements. (2) In the precision test, when comparing the results of the two methods for detecting two kinds of fluoride standard substances, there was no statistically significant difference ( F = 0.36, 0.15, P = 0.564, 0.707), and the coefficients of variation ( CV) were all < 5%. The CV of the detection results of the external quality control assessment water samples and drinking water samples were < 5%. (3) In the accuracy test, when the fluoride concentration in water was ≤1.00 mg/L, there was no statistically significant difference in the spiked recovery rates between the two methods ( F = 0.49, P = 0.504). When the fluoride concentration in water was > 1.00 mg/L, there was a statistically significant difference in the spiked recovery rates between the two methods ( F = 24.75, P = 0.003). Conclusions:The ion-selective electrode method has the advantages of wide detection range and wide adaptability, while the high-throughput rapid determination method has high accuracy. Testing personnel can weigh and choose the appropriate determination method based on the actual laboratory conditions and sample concentration range.

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."