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.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

To evaluate the clinical application value of magnetic bead sorting technology (MBS) using the T-cell select kit combined with an automatic cell sorter for isolating peripheral blood mononuclear cells (PBMCs) to realize automated specimen pre-processing and process optimization in T-SPOT.TB assay. A cross-sectional study was conducted involving 300 patients recruited from the first affiliated hospital of Air Force Medical University from March 2023 to July 2024. Among them, there were 167 males and 133 females. The age range of the patients was 18 to 65 years, with a median age of 49.0 (33.3, 59.0) years and a mean age of (46.4±13.9) years. 4 ml of anticoagulated whole blood samples in duplicate were collected from each patient. One fresh sample (0-4 h) was processed immediately using density gradient centrifugation (DGC) for PBMC isolation, while the other was processed using MBS method at either 0-4 h or 34-54 h post-collection. The cycles for cell enrichment step of the automatic cell sorter were adjusted from the conventional 4 cycles to 2 cycles. Statistical analysis was performed using Cohen′s Kappa test to evaluate the concordance of T-SPOT.TB results across all processing groups. The results showed that for fresh samples (0-4 h), the T-SPOT.TB results for samples processed with 4-cycle and 2-cycle MBS enrichment steps demonstrated positive concordance rates of 91.7% and 93.1%, negative concordance rates of 100% for both, overall concordance rates of 98.0% and 97.9%, and Kappa values of 0.94 and 0.95, respectively, compared with the reference results for paired samples processed using DGC. Correspondingly, the results for samples processed with the 4-cycle and 2-cycle MBS methods at 34-54 h post-collection yielded positive concordance rates of 90.0% and 81.3%, negative concordance rates of 95.1% and 100%, overall concordance rates of 93.0% and 82.9%, and Kappa values of 0.86 and 0.43, respectively, compared with the reference results for fresh samples processed using DGC. In conclusion,for 0-4 h samples, the T-SPOT.TB results from both 4-cycle and 2-cycle MBS enrichment protocols were highly consistent with the reference results from conventional DGC methods. However, for 34-54 h stored samples, results from the 4-cycle MBS method rather than the 2-cycle protocol exhibited significantly superior concordance with the reference results. The MBS method using T-Cell Select kit achieves automated sample pre-processing for T-SPOT.TB assay, reduces hands-on time, and provides a viable alternative with reliable results for long-stored specimens.

Objective To translate Evidence-Informed Decision-Making Competence Measure for Chinese nurses and test its validity and reliability.Methods A research group was set up to use the Brislin translation model to translate the original scale into Chinese,and the back translation,cross-cultural adaptation,pre-experiment and cognitive interview were conducted to finally form the Chinese version of the Evidence-Informed Decision-Making Competence Measure for nurses.A total of 1 247 nurses from 7 tertiary A hospitals in Beijing,Hubei,Hunan and Xinjiang were selected by convenience sampling method in April 2024 to test its reliability and validity.Results 1 026 effective question-naires were collected,with an effective recovery rate of 82.28％.The Chinese version of the Evidence-Informed Decision-Making Competence Measure included 25 items,including knowledge/skill,attitude and behavior.A total of 3 common factors were extracted from exploratory factor analysis,and the cumulative variance contribution rate was 91.725％.The content validity index at the item level was 0.83-1.00;the content validity index at the scale level was 0.988;the calibration association validity was 0.496.The Cronbach's α coefficient of the whole scale was 0.992;the half-point reliability was 0.930;the retest reliability was 0.927.Conclusion The Chinese version of Evidence-Informed Decision-Making Competence Measure for nurses has good reliability and validity,and it can be used to evaluate the evidence-informed decision-making competence of Chinese nurses,provide references for promoting evidence-based nursing practice and evidence-informed decision-making.

Objective To explore and identify the nutritional/inflammatory indicator with the highest predictive potential for overall survival(OS)in postoperative tumor patients so as to provide guidance for postoperative rehabilitation of tumor patients.Methods Data from 3 191 surgical patients were collected,including 15 nutritional/inflammatory indicators.The maximum selection rank statistic method was used to calculate the optimal cut-off values for continuous indicators.The Kaplan-Meier method was used to assess OS,and Cox proportional hazards models were used to analyze the association between the aforementioned 15 indicators and survival.The predictive value of these 15 indicators was evaluated with receiver operating characteristic(ROC)curves and C-index.Results Multivariate analysis showed that all 15 indicators were significantly associated with poorer OS in surgical patients(P＜0.05 for all).Time-dependent area under the curve(AUC)and C-index analysis indicated that 3 indicators with the highest predictive potential in OS in postoperative tumor patients were the nutritional risk index(NRI)(C-index:0.597),C-reactive protein-to-albumin ratio(CAR)(C-index:0.587),and C-reactive protein-to-lymphocyte ratio(CLR)(C-index:0.587).The optimal cut-off value for NRI was determined to be 104.31(i.e.,NRI＜104.31 suggests malnutrition)with the maximum selection rank statistic method,the optimal cut-off value for CAR to be 0.05(i.e.,CAR≥0.05 suggests a strong inflammatory response,often accompanied by malnutrition),and the optimal cut-off value for CLR to be 1.18(i.e.,CLR≥1.18 suggests a strong inflammatory response).Subgroup analysis indicated that NRI,CAR,and CLR had good correlation with tumor staging,and there were significant differences between tumor node metastasis(TNM)Ⅲ/Ⅳ stage patients and TNM Ⅰ/Ⅱ stage patients when there was a strong inflammatory response or malnutrition.Conclusion In postoperative tumor patients,NRI,CLR,and CAR have high prognostic value.Combining these with the patient's clinical stage,it enables more precise guidance for clinical diagnosis and treatment strategies.

Objective To investigate the mechanism of Yiqi Huoxue Huazhuo Jiedu Prescription in regulating the endoplasmic reticulum stress PERK/ATF4 signaling pathway to improve cerebral ischemia reperfusion injury in rats.Methods A total of 48 male SD rats were randomly divided into sham-operation group,model group,TCM group and edaravone group,with 12 rats in each group.Cerebral ischemia reperfusion injury rat model was prepared using middle cerebral artery occlusion method,and administration 24 hours after modeling.The edaravone group was given intraperitoneal injection of 1.4 mg/mL edaravone injection,TCM group was given 55 g/(kg·d)of Yiqi Huoxue Huazhuo Jiedu Prescription for gavage,while the sham-operation group and model group were given equal volumes of normal saline for gavage,twice a day for 3 consecutive days.The neurological deficit scores of the rats in each group were observed,TTC staining was used to detect the volume of cerebral infarction,HE staining was used to observe the morphology of brain tissue in the ischemia-reperfusion area,immunohistochemistry staining was used to detect the positive expressions of glucose regulated protein 78(GRP78),protein kinase R-like endoplasmic reticulum kinase(PERK)and transcription activator factor(ATF)4 in the ischemia-reperfusion area brain tissue,RT-PCR was used to detect the mRNA expressions of GRP78,PERK and ATF4 in the ischemia-reperfusion area brain tissue,Western blot was used to detect the protein expressions of GRP78,PERK and ATF4 in the ischemia-reperfusion area brain tissue.Results Compared with the sham-operation group,the neurological deficit score of the model group rats increased,the volume of cerebral infarction increased,the number of neurons in the ischemia-reperfusion area decreased,the arrangement was loose,and the nuclei were condensed,the mRNA and protein expressions of GRP78,PERK and ATF4 increased,with statistical significance(P<0.05).Compared with the model group,the neurological deficit score of TCM group and edaravone group decreased,the cerebral infarction volume decreased,the number and arrangement of neurons of the brain tissue in the ischemia reperfusion area increased,and nuclear condensation decreased in rats,the mRNA and protein expressions of GRP78,PERK and ATF4 were all reduced,with statistical significance(P<0.05).There was no statistical significance in various indicators between TCM group and the edaravone group(P>0.05).Conclusion Yiqi Huoxue Huazhuo Jiedu Prescription can improve cerebral ischemia reperfusion injury,and its mechanism may be related to down-regulating the expression of PERK/ATF4 signaling pathway and alleviating endoplasmic reticulum stress.

Objective:To establish a stable rat model of sequelae of pelvic inflammatory disease(SPID)with clinical characteristics,and to provide a reliable experimental model for the study of the pharmcological effect and mechanism of SPID.Methods:Twenty-four 7-week-old SD rats were divided into sham operation group,model-A(108 cfu/mL mixed bacterial solution,0.2 mL),model-B(109 cfu/mL mixed bacterial solution 0.2 mL),and model-C(108 cfu/mL E.coli 0.2 mL).The weight of the rat's uterine was weighed and the uterine index was calculated.The automatic hematology analyzer was used to detect the blood routine;hematoxylin-eosin staining(HE)and masson staining were used to detect uterine pathlogical changes in rats.Enzyme-linked immunosorbent assay(ELISA)was used to detect interleukin-1β(IL-1β),interleukin-6(IL-6)and tumor necrosis factor-α(TNF-α)in rat uterine tissue homogenates.Western blot was used to detect the expression of proteins related to NF-κB signaling pathway.Results:Compared with the sham operation group,the uterine index of model-A,model-B,and model-C were significantly increased(P＜0.05,P＜0.01).The levels of WBC and NE in the model-A increased significantly(P＜0.01).The level of LY in model-B decreased significantly(P＜0.01).The levels of IL-1β,TNF-α in model-A,model-B,and model-C were significantly increased(P＜0.01).The levels of IL-6 in model-A and model-B were significantly increased(P＜0.05,P＜0.01).The collagen volume fraction of model-A and model-B were significantly increased(P＜0.01).Mechanism study indicates that the expression levels of p-IKKβ/IKKβ,p-IκBα/IκBα and p-p65/p65 in model-A were significantly increased(P＜0.01),and the expression levels of IκBα/β-actin were significantly decreased(P＜0.01).The expression level of p-IKKβ/IKKβ in model-B was significantly increased(P＜0.01).Conclusions:A stable rat model of SPID that conforms to clinical characteristics can be successfully constructed by combining 0.2 mL of mixed bacterial solution with a concentration of 108 cfu/mL and mechanical injury.This modeling method intervened in the expression of the NF-κB inflammatory signaling pathway.