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.

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.

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.

Aim To study the renal protective effect of balanophora polysaccharide(BPS)on diabetic nephrop-athy(DN)mice and explore the related mechanisms.Methods A DN mouse model was induced using a high-fat diet combined with intraperitoneal injection of streptozotocin(STZ),which was indicated by fasting blood glucose higher than 11.1 mmol·L-1,accompa-nied by diabetic symptoms such as polydipsia,polydia-gia,polyuria and weight loss,then BPS intervention was performed.Body weight and fasting blood glucose of each group mice were detected;automatic biochemical analyzer was used to detect blood creatinine(SCr),blood urea nitrogen(BUN),24 h urinary protein(24 h UP),triglycerides(TG),total cholesterol(TC),alanine aminotransferase(ALT)content;ELISA was applied to determine serum inflammatory factor interleukin-6(IL-6)and tumor necrosis factor-α(TNF-α)level;HE and Masson staining were employed to observe renal his-topathological morphology;Western blot was used to de-tect Toll-like receptor 4(TLR4),myeloid differentiation factor 88(MyD88),nuclear factor κB(NF-κB)for pro-tein expression.Results Compared with the model group,after BPS,body weight and fasting blood glucose decreased(P＜0.01 or P＜0.05);SCr,BUN,24 h UP,TC,TG and ALT significantly decreased(P＜0.01 or P＜0.05);the levels of the proinflammatory factors TNF-α and IL-6 were significantly reduced(P＜0.01 or P＜0.05);renal tissue injury and fibrosis decreased;TLR4,MyD88,NF-κB protein expression significantly decreased(P＜0.01 or P＜0.05).Conclusion BPS has a protective effect on the kidneys of DN mice,re-ducing the blood glucose level,improving liver and kid-ney function,alleviating renal tissue damage and renal fibrosis,and reducing inflammation response.Its mecha-nism may be related to the regulation of TLR4/MyD88/NF-κB signaling pathway.

Aim To study the renal protective effect of balanophora polysaccharide(BPS)on diabetic nephrop-athy(DN)mice and explore the related mechanisms.Methods A DN mouse model was induced using a high-fat diet combined with intraperitoneal injection of streptozotocin(STZ),which was indicated by fasting blood glucose higher than 11.1 mmol·L-1,accompa-nied by diabetic symptoms such as polydipsia,polydia-gia,polyuria and weight loss,then BPS intervention was performed.Body weight and fasting blood glucose of each group mice were detected;automatic biochemical analyzer was used to detect blood creatinine(SCr),blood urea nitrogen(BUN),24 h urinary protein(24 h UP),triglycerides(TG),total cholesterol(TC),alanine aminotransferase(ALT)content;ELISA was applied to determine serum inflammatory factor interleukin-6(IL-6)and tumor necrosis factor-α(TNF-α)level;HE and Masson staining were employed to observe renal his-topathological morphology;Western blot was used to de-tect Toll-like receptor 4(TLR4),myeloid differentiation factor 88(MyD88),nuclear factor κB(NF-κB)for pro-tein expression.Results Compared with the model group,after BPS,body weight and fasting blood glucose decreased(P＜0.01 or P＜0.05);SCr,BUN,24 h UP,TC,TG and ALT significantly decreased(P＜0.01 or P＜0.05);the levels of the proinflammatory factors TNF-α and IL-6 were significantly reduced(P＜0.01 or P＜0.05);renal tissue injury and fibrosis decreased;TLR4,MyD88,NF-κB protein expression significantly decreased(P＜0.01 or P＜0.05).Conclusion BPS has a protective effect on the kidneys of DN mice,re-ducing the blood glucose level,improving liver and kid-ney function,alleviating renal tissue damage and renal fibrosis,and reducing inflammation response.Its mecha-nism may be related to the regulation of TLR4/MyD88/NF-κB signaling pathway.

Objective To establish a backpack-based field emergency medical rescue equipment system to enhance emergency rescue efficiency.Methods A backpack-based field emergency medical rescue equipment system was preliminarily constructed with the concept of medical treatment in echelons and prolonged field care(PFC)and the method of capability-based equipment need analysis;with the Delphi method 15 experts from relevant fields were invited to execute two rounds of questionnaire consultations,and the final equipment system was determined after the equipment varieties and quantities were revised based on the expert opinions.Results The response rate for the two rounds of expert questionnaire surveys was 100%.The experts'authority coefficients were 0.8734 and 0.87,respectively;Kendall coefficients were 0.232 and 0.345(both P<0.001),indicating statistically significant results.Ultimately,a backpack-based field emer-gency medical rescue equipment system comprising 15 backpacks and 158 individual pieces of equipment was established.Conclusion The established system demonstrates a certain degree of specificity and practicability,providing references for the equipment allocation and utilization of emergency medical rescue teams.[Chinese Medical Equipment Journal,2025,46(10):17-22]

Aim To investigate the effect of Zhenwu decoction on inflammation,oxidative stress and apopto-sis of human glomerular epithelial cells(HGEC)in-duced by lipopolysaccharide(LPS)based on Nrf2/HO-1 signaling pathway,and to explore the underlying mechanism.Methods HGEC were treated with LPS(1.0 mg·L-1)for 24 h to construct an oxidative damage model.On this basis,2.5％,5％and 10％Zhenwu decoction-containing serum were added to the low,medium and high dose groups of Zhenwu decoc-tion,and a normal group was set up.The changes of cell activity were assessed by MTT method and LDH method.The contents of TNF-α,IL-6,IL-10,SOD,CAT,GSH-Px,ROS and MDA in each group were de-tected by ELISA.The apoptosis of each group was de-tected by flow cytometry.The mRNA and protein ex-pressions of Bax,Bcl-2,caspase-3,caspase-9 and Nrf2/HO-1 pathway were detected by RT-qPCR and Western blot,respectively.Results Compared to the normal group,the model group of HGEC exhibited increased levels of inflammatory cytokines,enhanced oxidative stress response and aggravated apoptosis;after inter-vention with various doses of Zhenwu decoction,the in-flammatory levels in HGEC were reduced,oxidative damage and apoptosis were effectively ameliorated,and the mRNA and protein expression levels of the Nrf2/HO-1 signaling pathway were upregulated.Conclu-sions Zhenwu decoction can protect HGEC from LPS-induced inflammation and oxidative damage and im-prove apoptosis.The mechanism may be related to the activation of Nrf2/HO-1 signaling pathway.