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.

Patients with lung cancer experience a heavy symptom burden during the perioperative period, which seriously affects their recovery and quality of life. Traditional symptom management models rely mainly on scheduled ward rounds during hospitalization and outpatient follow-up after discharge. However, they have limitations such as delayed symptom recognition, lack of post-discharge monitoring, and non-quantitative symptom assessment, which often lead to delayed interventions and low patient satisfaction. In recent years, the symptom management model based on electronic patient-reported outcomes (ePRO) has been increasingly valued in clinical practice. Existing high-level evidence from both domestic and international studies indicates that, through proactive monitoring, real-time alerts, and remote interventions, this model enables dynamic and continuous symptom management and helps improve patient recovery and healthcare experience. As a supplement to routine medical care, the ePRO-based symptom management model aims to enhance the quality of care rather than replace existing medical processes. To promote the standardized application of this model in perioperative lung cancer care, this consensus integrates domestic and international evidence. After multiple rounds of voting by more than 50 experts, it formulates 12 consensus statements covering the three core components, symptom monitoring, alerting, and intervention, to provide scientific and practical recommendations for clinical practice.

Objective:To investigate the epidemiological dynamics and spatiotemporal diffusion trend of brucellosis in China from 2010 to 2024.Methods:Data on reported human brucellosis cases in mainland China from January 1, 2010, to December 31, 2024, were collected via the"China Information System for Disease Control and Prevention", including detailed information on the date of onset, gender, age, occupation, and residential address of the cases. The Joinpoint regression and spatial interpolation techniques were used to investigate the spatiotemporal dynamics and population distribution characteristics of human brucellosis in pastoral/semi-pastoral areas and other regions, as well as urban and rural areas, and explore the epidemic trends of the disease.Results:From 2010 to 2024, pastoral/semi-pastoral regions reported 252 094 brucellosis cases, with a mean annual incidence rate of 36.57±7.28 per 100 000. In contrast, other regions cumulatively recorded 519 748 cases during the same period, demonstrating a significantly lower mean annual incidence rate of 2.54±0.74 per 100 000. The incidence rate of human brucellosis in pastoral/semi-pastoral regions exhibited a declining-rebounding-declining trend. Specifically, the incidence rate decreased significantly from 2010 to 2017 (APC=-7.20; P<0.001) and increased notably from 2017 to 2021 (APC=18.00; P=0.015) with a decline again from 2021 to 2024 (APC=-7.53; P=0.027). In other regions, the incidence rate showed a fluctuating upward trend. Specifically, the incidence rate increased significantly from 2010 to 2015 (APC=20.37; P<0.001) and decreased notably from 2015 to 2018 (APC=-21.78; P<0.001), followed by an increase again from 2018 to 2024, a significant upward trend in incidence rate from 2018 to 2021 (APC=26.73; P<0.001) and a non-significant decline from 2021 to 2024 (APC=-0.99; P=0.735), resulting in the maintenance of a relatively high incidence level. Rural areas demonstrated significantly higher brucellosis incidence rates than urban settings (all P<0.001). Brucellosis exhibited a diffusion trend from the northern epidemic areas of China to neighboring regions, along with sporadic diffusion in southern regions between 2010 and 2024. The age structure of patients in pastoral/semi-pastoral areas differed significantly from that in other regions. Specifically, in pastoral/semi-pastoral areas, the incidence rate was higher among the 35-49 age groups, while in other regions, the incidence rate was higher among those aged 55-64. Conclusion:There are notable disparities in the incidence of human brucellosis between pastoral/semi-pastoral areas and other regions in China. Human brucellosis exhibits a diffusion trend from the northern epidemic areas of China to neighboring regions, along with sporadic diffusion in southern regions.

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult

Objective:To rapidly and accurately detect volumetric modulated arc therapy (VMAT) plans with potentially inaccurate radiation doses.Methods:The measurement-based dosimetric verification result of 196 VMAT plans obtained using ArcCHECK phantoms were retrospectively collected. Independent dosimetric calculation and verification were conducted for these plans using the ArcherQA system based on a fast Monte Carlo algorithm. The gamma passing rates of dosimetric verification using ArcCHECK phantom and the ArcherQA system were compared, followed by their correlation analysis and linear regression fitting. The ArcherQA system′s gamma passing rate threshold used to detect positive dosimetric verification result obtained using ArcCHECK phantoms, as well as the specificity of the detection, were calculated. Based on this gamma passing rate threshold, another 50 VMAT plans were selected as a test set to assess the ArcherQA system′s ability to detect positive measurement-based dosimetric verification result.Results:The average gamma passing rates for the dosimetric verification of the VMAT plans using the ArcherQA system and ArcCHECK phantoms were 97.28% and 96.57% (3%/3 mm, TH=10%), respectively. Both rates had a correlation coefficient of 0.71 ( P < 0.01) and a linear fitting coefficient of 0.54 ( R2=0.51). When the gamma passing rate for dosimetric verification using ArcCHECK phantoms was set at 90% (3%/2 mm, TH=10%), the gamma passing rate threshold for dosimetric verification using the ArcherQA system should be adjusted to 94.8% to detect all VMAT plans with positive dosimetric verification result obtained using ArcCHECK phantoms, with a specificity of 67.8%. Using this threshold, the ArcherQA system detected all VMAT plans in the test set for which ArcCHECK phantom-based measurement yielded positive dosimetric verification result. Conclusions:By determining an appropriate gamma passing rate threshold, the ArcherQA system can rapidly and accurately detect VMAT plans with potentially inaccurate doses, thus ensuring treatment accuracy and improving work efficiency.

Objective:To investigate the epidemiological dynamics and spatiotemporal diffusion trend of brucellosis in China from 2010 to 2024.Methods:Data on reported human brucellosis cases in mainland China from January 1, 2010, to December 31, 2024, were collected via the"China Information System for Disease Control and Prevention", including detailed information on the date of onset, gender, age, occupation, and residential address of the cases. The Joinpoint regression and spatial interpolation techniques were used to investigate the spatiotemporal dynamics and population distribution characteristics of human brucellosis in pastoral/semi-pastoral areas and other regions, as well as urban and rural areas, and explore the epidemic trends of the disease.Results:From 2010 to 2024, pastoral/semi-pastoral regions reported 252 094 brucellosis cases, with a mean annual incidence rate of 36.57±7.28 per 100 000. In contrast, other regions cumulatively recorded 519 748 cases during the same period, demonstrating a significantly lower mean annual incidence rate of 2.54±0.74 per 100 000. The incidence rate of human brucellosis in pastoral/semi-pastoral regions exhibited a declining-rebounding-declining trend. Specifically, the incidence rate decreased significantly from 2010 to 2017 (APC=-7.20; P<0.001) and increased notably from 2017 to 2021 (APC=18.00; P=0.015) with a decline again from 2021 to 2024 (APC=-7.53; P=0.027). In other regions, the incidence rate showed a fluctuating upward trend. Specifically, the incidence rate increased significantly from 2010 to 2015 (APC=20.37; P<0.001) and decreased notably from 2015 to 2018 (APC=-21.78; P<0.001), followed by an increase again from 2018 to 2024, a significant upward trend in incidence rate from 2018 to 2021 (APC=26.73; P<0.001) and a non-significant decline from 2021 to 2024 (APC=-0.99; P=0.735), resulting in the maintenance of a relatively high incidence level. Rural areas demonstrated significantly higher brucellosis incidence rates than urban settings (all P<0.001). Brucellosis exhibited a diffusion trend from the northern epidemic areas of China to neighboring regions, along with sporadic diffusion in southern regions between 2010 and 2024. The age structure of patients in pastoral/semi-pastoral areas differed significantly from that in other regions. Specifically, in pastoral/semi-pastoral areas, the incidence rate was higher among the 35-49 age groups, while in other regions, the incidence rate was higher among those aged 55-64. Conclusion:There are notable disparities in the incidence of human brucellosis between pastoral/semi-pastoral areas and other regions in China. Human brucellosis exhibits a diffusion trend from the northern epidemic areas of China to neighboring regions, along with sporadic diffusion in southern regions.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To explore the structural equation model to explore the levels of work-related musculoskeletal disorders (WMSDs) and various risk factors in the feet and ankle of China's occupational population, providing scientific basis for for preventing WMSDs in feet and ankles.Methods:Data of 73497 national occupational epidemiological cases were selected from June 2018 to December 2023 used the Chinese version of the Electronic Questionnaire on Musculoskeletal Disorders. The adverse ergonomic factors and their source classification standard and confirmatory factor analysis were used to investigate foot and ankle WMSDs and their related risk factors (including individual factors, work organization, work posture, work type, fatigue, etc.) in key occupational groups in China, and structural equation model hypothesis, fitting, verification, and path and intermediary effect analysis were carried out. The model fit evaluation indexes included Chi-square specific degrees of freedom ( χ2/ df), gauge fit index (NFI), Tucker Lewis index (TLI), goodness of Fit index (GFI), adjusted Goodness of Fit index (AGFI) and approximate root mean square error (RMSEA) . Results:A total of 73497 occupational workers were surveyed, with local muscle fatigue and WMSDs incidence rates in the feet and ankles being 17.17% and 12.06%, respectively. The fitting index of the adjusted structural equation model basically meets the standard (GFI=1, AGFI=1, RMESA=0.042, NFI=0.716, TLI=0.663). The top three factors affecting feet and ankle WMSDs are feet and ankle muscle fatigue, work type, and work organization, with standardized path coefficients of 0.221, 0.105, and 0.095, respectively. The top two factors affecting feet and ankle muscle fatigue are work organization and work type, with standardized path coefficients of 0.548 and 0.383, respectively. Feet and ankle muscle fatigue, work type, work organization, and work posture have a direct effect on feet and ankle WMSDs, with effect values of 0.221, 0.105, 0.095, and 0.077, respectively. The organization and type of work can also have indirect effects through feet and ankle muscle fatigue, with effect values of 0.121 and 0.084, respectively.Conclusion:Feet and ankle muscle fatigue has a direct impact on WMSDs, and plays a mediating role between ankle and ankle WMSDs caused by work organization and work type. Feet and ankle muscle fatigue is an important pathway leading to feet and ankle WMSDs. It is recommended that employers and managers detect job fatigue early and take corresponding prevention and intervention measures, which can play a key role in preventing feet and ankle WMSDs.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

The 2024 World Conference on Lung Cancer (WCLC) and the European Society for Medical Oncology (ESMO) Annual Meeting, two of the most prestigious events in oncology, have concluded sequentially. As the most authoritative annual gatherings in lung cancer and the entire oncology field, the WCLC and ESMO conferences brought together top oncology experts and scientists from around the world to share, discuss, and publish the latest cutting-edge advancements in oncology. In both conferences, lung cancer immunotherapy remained a hot topic of considerable interest. This article aims to summarize and discuss the important research progress on perioperative immunotherapy for non-small cell lung cancer reported at the two conferences.