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 identify the latent class characteristics of health literacy and related factors among junior high school students, so as to provide evidence for developing precise and systematic health literacy promotion strategies. Methods: In November 2024, a two stage random cluster sampling method was used to conduct a questionnaire survey among 8 933 junior high school students in Zhongshan. Health literacy was assessed across six dimensions: health behavior and lifestyle, disease prevention and control, mental health, growth development and puberty health, safety emergency and risk avoidance, and medical knowledge and appropriate healthcare utilization. Latent profile analysis was used to identify distinct health literacy classes, and multinomial Logistic regression was applied to analyze the related factors. Results: Three latent classes of health literacy among junior high school students were identified: the well balanced type(71.7%，6 406), the medical knowledge deficit type(22.3%，1 992), and the overall low literacy type(6.0%，537). Logistic regression analysis showed that girls had lower risks of belonging to the medical knowledge deficit type( OR =0.53, 95% CI =0.48-0.59) and the overall low literacy type( OR =0.27,95% CI =0.22-0.33) compared with boys(both P <0.05). Students in rural schools had the highest risks of belonging to these two profiles above [ OR (95% CI ) =1.89 (1.61-2.21), 3.18 (2.50-4.06),both P <0.05]. Junior high school students having ≥2 siblings were positively associated with belonging to these two profiles, with risks 1.60 (95% CI = 1.35-1.89) and 2.25 times (95% CI =1.66-3.05) higher than those of only children (both P <0.05). Junior high school students with parental education of bachelor s degree or above were associated with lower risk of belonging to the medical knowledge deficit type (father: OR =0.63, 95% CI =0.47-0.84; mother: OR =0.68, 95% CI = 0.52 -0.90,both P <0.05). Junior high school students with receiving health education courses ≥3 times per month were associated with lower risks of belonging to both the medical knowledge deficit type and overall low literacy type ( OR =0.51, 95% CI =0.43- 0.60 ; OR =0.33, 95% CI =0.25-0.42, both P <0.05). Conclusions Three latent classes of health literacy exist among junior high school students in Zhongshan. Targeted interventions should be implemented based on profile characteristics, with an emphasis on strengthening medical knowledge education and providing comprehensive support for vulnerable groups.

[摘 要] 目的：构建负载STING激动剂DMXAA的锰卟啉金属有机框架纳米颗粒（DPM），探讨其对三阴性乳腺癌（TNBC）细胞4T1及其小鼠移植瘤的治疗效果。方法：通过物理吸附法制备 DPM 纳米颗粒，利用透射电镜、扫描电镜及纳米粒度电位仪表征其形貌与理化性质。常规培养4T1细胞，细胞实验分为对照组、超声辐照组（US组）、DPM治疗组（DPM组）和DPM治疗联合超声辐照组（DPM + US组），用CCK-8法检测细胞活性，免疫荧光法检测高迁移率族蛋白B1（HMGB1）和钙网蛋白（CRT）的表达，WB法检测STING通路相关蛋白的表达。构建4T1细胞移植瘤小鼠模型，分为四组，处理同细胞实验，测量肿瘤体积，免疫荧光法检测移植瘤组织中Ki-67、HMGB1、CRT和缺氧诱导因子-1ɑ（HIF-1ɑ）蛋白的表达，TUNEL法检测细胞凋亡，流式细胞术检测免疫细胞活化情况，对主要器官进行H-E染色，以评估纳米材料的体内安全性。结果：DPM呈梭形，平均粒径（268 ± 3.302）nm，电位（33.1 ± 0.87）mV。细胞实验中，DPM联合超声辐照可明显抑制4T1细胞的增殖（P < 0.001），提高4T1细胞中ROS水平（P < 0.001），诱导4T1细胞CRT表达上调（P < 0.001），HMGB1从细胞核中移至细胞质，激活STING信号通路[p-STING、p-TBK1、p-IRF3蛋白表达均显著增加（均P < 0.001）]。体内实验中，DPM联合超声辐照可显著抑制4T1细胞移植瘤生长（P < 0.001）并促进免疫细胞表型转化（P < 0.001），抑制移植瘤组织中Ki-67、HIF-1α蛋白表达（均P < 0.01），谷胱甘肽（GSH）产生（P < 0.01），促进CRT、HMGB1蛋白表达、ROS产生（P < 0.001），对主要器官结构无明显影响。结论： DPM联合超声辐照可通过激活STING通路显著抑制4T1细胞及其移植瘤的生长，诱导抗肿瘤免疫应答，且对主要器官无明显毒性。

BACKGROUND:Although previous studies have reported associations between lipids and the risk of osteoporotic pathological fractures,the specific causal relationships between lipid level and osteoporotic pathological fractures remain unclear.OBJECTIVE:To elucidate the causal relationship between lipids and osteoporotic pathological fractures using a two-sample bidirectional Mendelian randomization analysis.METHODS:The data for 178 lipid metabolites were obtained from the IEU-GWAS database(developed by the MRC Integrative Epidemiology Unit at the University of Bristol,UK,which provides extensive summary data from genome-wide association studies),while osteoporotic pathological fracture data(from 173 619 European participants)were acquired from the FinnGen database(constructed by the Finnish national gene research program,focusing on investigating relationships between genomics and health/disease in the Finnish population).Osteoporotic pathological fracture data were used as the outcome variable,with lipids serving as exposures,for the bidirectional Mendelian randomization study to evaluate the causal effects of different lipids on osteoporotic pathological fractures.The UK Biobank database was employed as a validation set by switching the outcome variable to verify the findings horizontally.RESULTS AND CONCLUSION:(1)The inverse variance weighted analysis indicated that each unit increase in sterol ester(27∶1/20∶2)levels was associated with a 25.55％increase in the risk of osteoporotic pathological fractures(odds ratio=1.256,95％confidence interval:1.001-1.575,P=0.049),suggesting a significant positive correlation between elevated sterol ester levels and increased fracture risk.Reverse Mendelian randomization analysis revealed a significant negative association between osteoporotic pathological fractures and three types of phosphatidylcholine.Horizontal validation yielded consistent results,confirming sterol ester as a risk factor for osteoporotic pathological fractures.(2)The results indicate that sterol ester is a risk factor for osteoporotic pathological fractures,while phosphatidylcholine serves as a protective factor.These findings strengthen the evidence supporting the effect of lipids on the risk of osteoporotic pathological fractures.Although the GWAS data used in this study were derived from European populations,given the broad commonality of human genetics,the results provide valuable reference significance for improving osteoporosis in Chinese populations through lipid regulation.

BACKGROUND:Although previous studies have reported associations between lipids and the risk of osteoporotic pathological fractures,the specific causal relationships between lipid level and osteoporotic pathological fractures remain unclear.OBJECTIVE:To elucidate the causal relationship between lipids and osteoporotic pathological fractures using a two-sample bidirectional Mendelian randomization analysis.METHODS:The data for 178 lipid metabolites were obtained from the IEU-GWAS database(developed by the MRC Integrative Epidemiology Unit at the University of Bristol,UK,which provides extensive summary data from genome-wide association studies),while osteoporotic pathological fracture data(from 173 619 European participants)were acquired from the FinnGen database(constructed by the Finnish national gene research program,focusing on investigating relationships between genomics and health/disease in the Finnish population).Osteoporotic pathological fracture data were used as the outcome variable,with lipids serving as exposures,for the bidirectional Mendelian randomization study to evaluate the causal effects of different lipids on osteoporotic pathological fractures.The UK Biobank database was employed as a validation set by switching the outcome variable to verify the findings horizontally.RESULTS AND CONCLUSION:(1)The inverse variance weighted analysis indicated that each unit increase in sterol ester(27∶1/20∶2)levels was associated with a 25.55％increase in the risk of osteoporotic pathological fractures(odds ratio=1.256,95％confidence interval:1.001-1.575,P=0.049),suggesting a significant positive correlation between elevated sterol ester levels and increased fracture risk.Reverse Mendelian randomization analysis revealed a significant negative association between osteoporotic pathological fractures and three types of phosphatidylcholine.Horizontal validation yielded consistent results,confirming sterol ester as a risk factor for osteoporotic pathological fractures.(2)The results indicate that sterol ester is a risk factor for osteoporotic pathological fractures,while phosphatidylcholine serves as a protective factor.These findings strengthen the evidence supporting the effect of lipids on the risk of osteoporotic pathological fractures.Although the GWAS data used in this study were derived from European populations,given the broad commonality of human genetics,the results provide valuable reference significance for improving osteoporosis in Chinese populations through lipid regulation.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

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.

Objective To investigate the effects and mechanisms of metformin-induced metabolic intervention on the proliferation and differentiation of myoblasts.Methods C2C12 myoblasts cultured in growth or differentiation media were treated with 1 mmol/L metformin,with DMSO as the control.Cells in growth media were treated for 48 hours,with samples collected every 12 hours for metabolomic analysis,protein expression,mitochondrial function,and proliferation assays.Cells in differentiation media were induced to differentiate for 72 hours,and myotube formation was assessed via embryonic myosin heavy chain immunofluorescence staining.Results Metformin treatment increased intracellular levels of metabolites such as lactate,pyruvate,and adenosine monophosphate,while reducing adenosine triphosphate and thiamine pyrophosphate.Phosphorylation of AMP-activated protein kinase(AMPK)and related protein expression were significantly upregulated(P<0.05).Mitochondrial biogenesis was enhanced,and membrane potential markedly decreased(P<0.05).The proportion of S-phase cells and EdU-positive cells gradually increased(P<0.01),though overall proliferation rate slowed.During differentiation,the myoblast fusion index significantly decreased(P<0.01).Conclusion Metformin induces metabolic reprogramming in myoblasts and suppresses their proliferation and differentiation,with activation of AMPK-related signaling pathways serving as a key molecular mechanism.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.