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.

Background/Aims: Lenvatinib resistance remains a critical barrier in advanced hepatocellular carcinoma (HCC) therapy. However, the underlying mechanisms and strategies for reversing resistance remain incompletely understood. Methods: Integrated transcriptomics of lenvatinib-resistant patient tumors and an acquired-resistance murine model identified a novel macrophage subpopulation. Functional validation employed CRISPR-SAM screening, conditioned medium (CM) assays, subcutaneous/orthotopic xenografts, patient-derived organoids (PDOs), and patient-derived xenografts (PDXs). Mechanistic studies included ChIP-qPCR, co-immunoprecipitation, and pharmacologic targeting. Clinical relevance was assessed in a retrospective cohort. Results: Resistant HCC exhibited significant enrichment of a COLEC12high TAM subset , which correlated with poor survival and treatment response. These TAMs secreted neuregulin-1 (NRG1) , activating HER2/HER3-AKT signaling in tumor cells to drive cancer stemness and lenvatinib resistance. Mechanistically, in TAMs COLEC12 sequestered STAT1 in the cytoplasm, preventing its phosphorylation, and thereby derepressing STAT3-mediated NRG1 transcription. Depletion of NRG1 reversed the stemness phenotypes and resensitized tumors to lenvatinib both in vitro and in vivo. Clinically, high NRG1 expression predicted an inferior lenvatinib response and shorter survival. Crucially, the bispecific anti-HER2/HER3 antibody zenocutuzumab restored lenvatinib efficacy in PDOs, PDXs, and murine models. Conclusions Our work establishes the COLEC12high TAM/NRG1 axis as a master regulator of therapeutic resistance and identifies NRG1 as a predictive biomarker, providing a clinically actionable strategy to overcome lenvatinib resistance in HCC.

Objective To analyze the factors affecting the prognosis of patients with locally advanced or metastatic non-small cell lung cancer (NSCLC) undergoing immunotherapy and construct an individualized prognostic nomogram prediction model. Methods A retrospective analysis was conducted on the clinical data of 385 patients with driver gene-negative, locally advanced or metastatic NSCLC who received first-line immune checkpoint inhibitors. Univariate and multivariate Cox regression analyses were used to identify prognostic risk factors, and a prognostic nomogram model was established. The predictive performance of the model was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves and area under the curve (AUC), and calibration curves. The cutoff value of the nomogram was calculated to stratify patients by risk. Survival curves were calculated by Kaplan-Meier analysis. Results Age (HR=1.775, 95%CI: 1.265-2.490), degree of differentiation (HR=0.365, 95%CI: 0.257-0.519), low PD-L1 expression (HR=0.661, 95%CI: 0.455-0.960), high PD-L1 expression (HR=0.423, 95%CI: 0.297-0.603), SCC-Ag (HR=1.549, 95%CI: 1.109-2.163), and CONUT score (HR=2.527, 95%CI: 1.797-3.554) were independent risk factors affecting overall survival (OS) of patients with NSCLC undergoing immunotherapy. The nomogram prediction model constructed on the basis of these factors had a C-index of 0.767. Time-dependent ROC curves for survival showed that the AUCs for 1-, 2-, and 3-year OS were 0.830, 0.853, and 0.886, respectively. Calibration curves indicated that the nomogram-predicted survival rates were in good agreement with the actual outcomes. The cutoff value for the study’s nomogram prediction model was 136.60 points, and survival curves showed statistically significant differences between different risk groups (P<0.05). Conclusion The nomogram model established in this study can effectively predict the prognosis of patients with driver gene-negative locally advanced or metastatic NSCLC treated with first-line immunosuppressive therapy. It provides a new tool for assessing prognosis and aids clinicians in formulating individualized treatment plans.

Objective: To investigate the current status of latent tuberculosis infection (LTBI) among freshmen in junior and senior high schools in Lanzhou, so as to provide scientific basis for improving the tuberculosis prevention and control strategy in schools. Methods: The screening results of 74 516 freshmen in senior and boarding junior high schools in Lanzhou during 2023 and 2024 were collected. The Chi square test and multivariate Logistic regression model were applied to analyze LTBI level, strongly positive risk for tuberculin skin test (TST) and related factors of the freshmen. Results: During 2023 and 2024, the screening rate of tuberculosis among freshmen in senior and boarding junior high schools in Lanzhou was 93.45%, of which the positive rate for TST was 5.71%, the infection rate for LTBI was 3.80%, and the strongly positive rate for TST was 1.24%. There were statistically significant differences in the screening rate of tuberculosis among freshmen in different years, grades, regions, school types and districts ( χ 2=5.34, 2 463.88, 3 516.13, 132.34, 4 436.56, all P <0.05). Multivariate Logistic regression analysis showed that senior high schools ( OR =1.62, 2.18) and urban areas ( OR =2.08, 3.07 ) were all related factors for LTBI and strong positivity for TST among freshmen; schools located in Xigu District, Honggu District, Yongdeng County, Yuzhong County, and Lanzhou New Area ( OR =3.57, 5.67, 9.12, 3.70, 3.64) were related factors of strong positivity for TST among freshmen (all P <0.05). Conclusions The LTBI level among freshmen in senior and boarding junior high schools in Lanzhou is relatively low. Grades and regions are related factors for LTBI and strong positivity for TST.

Objective: To establish a real-time quality control scheme based on the median (MD-IC) of internal control cycle threshold value in negative samples (NEG-IC-CT), so as to monitor anomalies such as progressive drift in nucleic acid testing system not covered by conventional internal quality control (IQC) in blood center nucleic acid laboratories, and to verify its feasibility. Methods: The internal control CT values of 54 426 negative samples were retrospectively collected. These samples were from four reagent batches of the two new and old equipment sets during the operation of the Wantai nucleic acid testing system in our blood center. The daily median of NEG-IC-CT values was used as the research indicator. Control limits were calculated using median absolute deviation (MAD) to construct the Median-MAD quality control chart. The monitoring performance of this scheme for the operation status of the testing system was simultaneously evaluated. Results: Statistical analysis showed significant differences in NEG-IC-CT value distribution between the new and old equipment sets, as well as between the two different reagent batches of the old equipment (P<0.000 1). The NEG-IC-CT value performance of the two different reagent batches of the new equipment was no significant difference in distribution (P>0.05). This scheme identified three typies of distinct anomalies. The out-of-control events observed with the old equipment in both the O1 and O2 reagent batches suggested potential performance decay due to equipment aging. The unreported change of reagent batch in time of Phase B with new equipment caused a stepwise drift on the quality control chart. In the later stage of Phase A with the new equipment, an alert was triggered, indicating potential quality risks associated with practices such as the mixed use of the remaining reagents and extremely long operator working hours. Conclusion: The realtime quality control scheme based on NEG-IC-CT value established in this study has been preliminarily validated for its monitoring effectiveness in nucleic acid testing in our blood center. This scheme performed well in detecting differences among testing systems and reagent batches, serving as an effective supplement to routine internal quality control. It can provide an intuitive and effective evaluation method for monitoring the performance of the nucleic acid testing process at blood center.

The inflammatory microenvironment is closely associated with the initiation and progression of osteoarthritis （OA）， specifically manifesting as macrophage activation， dysregulation of inflammatory cytokines， and redox imbalance. Following an overview of the pathological characteristics of the OA inflammatory microenvironment， this paper reviews the research progress on the mechanism of traditional Chinese medicine （TCM） intervening in OA by modulating the inflammatory microenvironment. It has been found that TCM monomers/active ingredients （such as total alkaloids from Strychnos nux-vomica ， quercetin， triptolide， etc.）， herb pairs （e.g. Angelica pubescens - Gentiana macrophylla ， Carthami Flos-Lycopodii Herba）， and TCM formulas （such as Zhuanggu jianxi formula， Duhuo jisheng decoction and Rongjin niantong formula， etc.） can inhibit macrophage activation， reduce the release of proinflammatory cytokines and the generation of reactive oxygen species by inhibiting multiple signaling pathways， including nuclear factor-κB， Wnt/ β -catenin， and mitogen-activated protein kinase， thereby alleviating the articular inflammatory microenvironment， restoring local joint homeostasis， and slowing the progression of OA.

[摘 要] 目的：探究外周血1型辅助性T细胞（Th1）/Th2/Th17细胞相关细胞因子IL-2、IL-4、IL-6、IL-10、IFN-γ、TNF-α、IL-17A对Ⅲ~Ⅳ期肺鳞状细胞癌（LSCC）患者一线免疫治疗联合化疗疗效和预后的预测价值及其动态变化的意义。方法：回顾性分析2020年1月至2023年12月在内蒙古医科大学附属医院接受一线免疫治疗联合化疗的58例Ⅲ~Ⅳ期LSCC患者的临床资料，采集基线及治疗2、4、6周期后和疾病进展时的外周血，用流式细胞术检测Th1/Th2/Th17细胞分泌的细胞因子水平，用受试者工作特性曲线（ROC）确定各细胞因子基线的最佳截断值，据此将患者分为高、低表达组；根据RECIST 1.1标准，将患者分为客观缓解（ORR）[完全缓解（CR） + 部分缓解（PR）]组、非ORR[（疾病稳定（SD） + 疾病进展（PD）]组、疾病控制（DCR）（CR + PR + SD）组和非DCR（即PD）组；根据PD-L1表达评分将患者分为PD-L1 ≥ 1%组和PD-L1 < 1%或未知组。比较组间疗效的差异；分析临床病理特征与疗效的相关性；用广义估计方程（GEE）评估细胞因子动态变化与疗效的关系；用Kaplan-Meier法绘制生存曲线，Log-Rank检验比较组间差异，COX比例风险回归模型进行单因素及多因素预后分析。结果：IL-2和IFN-γ高表达组患者的客观缓解率（ORR）显著高于低表达组患者（P < 0.001）。IL-2、IFN-γ高表达组和IL-10、TNF-α低表达组患者的疾病控制率（DCR）均显著高于对应低/高表达组（P < 0.001）。PD-L1 ≥ 1%组DCR显著高于PD-L1 < 1%或未知组（P < 0.001）。动态分析显示，在4周期及6周期时，有效组患者血清中IL-6表达水平显著低于无效组（P < 0.05），控制组IL-6表达水平显著低于未控制组（P < 0.001）；治疗前及6周期时有效组IFN-γ表达水平显著高于无效组（P < 0.05），治疗前控制组IFN-γ表达水平显著高于未控制组（P < 0.05）。生存分析显示，IL-2低表达组、IL-10高表达组、TNF-α高表达组和IFN-γ低表达组患者的中位PFS显著缩短（均P < 0.05）。COX多因素分析证实，治疗前IL-2 < 2.45 pg/mL和IL-10 ≥ 3.52 pg/mL 是PFS的独立危险因素。结论：外周血Th1/Th2/Th17细胞相关细胞因子的基线水平及动态变化对Ⅲ~Ⅳ期LSCC患者一线免疫治疗联合化疗的疗效和预后具有预测价值。

OBJECTIVE To investigate the effects and mechanisms of Lycium ruthenicum Murr. in improving sleep. METHODS Network pharmacology was employed to identify the active components of L. ruthenicum and their associated disease targets， followed by enrichment analysis. A caffeine‑induced zebrafish model of sleep deprivation was established ， and the zebrafish were treated with L. ruthenicum Murr. extract （LRME） at concentrations of 0.1， 0.2 and 0.4 mg/mL， respectively； 24 h later， behavioral changes of zebrafish and pathological alterations in brain neurons were subsequently observed. The levels of inflammatory factors [interleukin-6 （IL-6）， IL-1β， IL-10， tumor necrosis factor-α （TNF-α）]， oxidative stress markers [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， catalase （CAT）]， and neurotransmitters [5- hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， glutamic acid （Glu）， dopamine （DA）， and norepinephrine （NE）] were measured. The protein expression levels of protein kinase B1 （AKT1）， phosphorylated AKT1 （p-AKT1）， epidermal growth factor receptor （EGFR）， B-cell lymphoma 2 （Bcl-2）， sarcoma proto-oncogene，non-receptor tyrosine kinase （SRC）， and heat shock protein 90α family class A member 1 （HSP90AA1） in the zebrafish were also determined. RESULTS A total of 12 active components and 176 intersecting disease targets were identified through network pharmacology analysis. Among these， apigenin， naringenin and others were recognized as core active compounds， while AKT1， EGFR and others served as key targets； EGFR tyrosine kinase inhibitor resistance signaling pathway was identified as the critical pathway. The sleep improvement rates in zebrafish of LRME low-， medium-， and high-dose groups were 54.60%， 69.03% and 77.97%， 开发。E-mail：hjp_yft@163.com respectively， while the inhibition ratios of locomotor distance were 0.57， 0.83 and 0.95， respectively. Compared with the model group， the number of resting counts， resting time and resting distance were significantly increased/extended in LRME medium- and high-dose groups （P＜0.05）. Neuronal damage in the brain was alleviated. Additionally， the levels of IL-6， IL-1β， TNF-α， MDA， Glu， DA and NE， as well as the protein expression levels of AKT1， p-AKT1， EGFR， SRC and HSP90AA1， were markedly reduced （P＜0.05）， while the levels of IL-10， SOD， GSH-Px， CAT， 5-HT and GABA， as well as Bcl-2 protein expression， were significantly elevated （P＜0.05）. CONCLUSIONS L. ruthenicum Murr. demonstrates sleep-improving effects， and its specific mechanism may be related to the regulation of inflammatory responses， oxidative stress， neurotransmitter balance， and the EGFR tyrosine kinase inhibitor resistance signaling pathway.

Objective: To analyze the epidemiological characteristics and influencing factors of severe fever with thrombocytopenia syndrome (SFTS) in Zhejiang Province from 2019 to 2023, so as to provide the reference for strengthening SFTS prevention and control. Methods: Data on laboratory-confirmed SFTS cases in Zhejiang Province from 2019 to 2023 were collected through the Infectious Disease Reporting Information System of Chinese Disease Prevention and Control Information System. Meteorological data, geographic environment and socioeconomic factors during the same period were collected from the fifth-generation European Centre for Medium-Range Weather Forecasts, Geospatial Data Cloud, and Zhejiang Statistical Yearbook, respectively. Descriptive epidemiological methods were used to analyze the epidemiological characteristics of SFTS from 2019 to 2023, and a Bayesian spatio-temporal model was constructed to analyze the influencing factors of SFTS incidence. Results: A total of 578 SFTS cases were reported in Zhejiang Province from 2019 to 2023, with an annual average incidence of 0.23/105. The peak period was from May to July, accounting for 52.60%. There were 309 males and 269 females, with a male-to-female ratio of 1.15∶1. The cases were mainly aged 50-<80 years, farmers, and in rural areas, accounting for 82.53%, 77.34%, and 75.43%, respectively. Taizhou City and Shaoxing City reported more SFTS cases, while Shaoxing City and Zhoushan City had higher annual average incidences of SFTS. The Bayesian spatio-temporal interaction model showed good goodness of fit. The results showed that mean temperature (RR=1.626, 95%CI: 1.111-2.378) and mean wind speed (RR=1.814, 95%CI: 1.321-2.492) were positively correlated with SFTS risk, while altitude (RR=0.432, 95%CI: 0.230-0.829) and population density (RR=0.443, 95%CI: 0.207-0.964) were negatively correlated with SFTS risk. Conclusions SFTS in Zhejiang Province peaks from May to July. Middle-aged and elderly people and farmers are high-risk populations. Taizhou City, Shaoxing City, and Zhoushan City are high-incidence areas. Mean temperature, mean wind speed, altitude, and population density can all affect the risk of SFTS incidence.

The Pharmacovigilance Guidelines for Clinical Application of Oral Chinese Patent Medicines （hereinafter referred to as the Guidelines） is first specialized in the field of drug safety for oral Chinese patent medicines （OCPMs） in China. Rooted in China's healthcare context， the Guidelines address the unique usage patterns and risk characteristics of OCPMs， filling a regulatory gap in the pharmacovigilance framework specific to this category. To facilitate accurate understanding and effective implementation of the Guidelines， and to promote the standardized development of pharmacovigilance practices for OCPMs， this study offered a systematic interpretation based on its three core components. In the domain of risk monitoring and reporting， the paper analyzed the rationale for multi-source information integration and clarified the criteria for identifying key products and target populations for intensive monitoring. Regarding risk assessment， the Guidelines were examined from three dimensions of formulation components， medication behaviors， and population to address complex safety issues arising from medicinal constituents， irrational use， and individual susceptibility. In the area of risk control， the analysis focused on context-based interventions and dynamic closed-loop management strategies， exploring practical pathways to shift from passive response to proactive risk mitigation. Furthermore， this paper evaluated the applied value of the Guidelines and identified implementation challenges， such as insufficient capacity at the primary-care level and limited digital infrastructure. In response， the study proposed optimization strategies including establishing a dynamic updating mechanism， strengthening training at the grassroots level， and incorporating artificial intelligence to enhance pharmacovigilance capacity. This interpretation aims to provide actionable insights for marketing authorization holders （including manufacturers）， pharmaceutical distributors， healthcare institutions， and research organizations， ultimately supporting the establishment and refinement of a full lifecycle pharmacovigilance system for OCPMs.