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.

This study reports a new species of Culicoides(Avaritia)isolated from Xunke County, Heilongjiang Province, China. The new species, Culicoides(Avaritia)dongshanensis Liu et Wang, sp. nov., was identified and illustrated based on female adults. Its diagnostic characteristics are as follows: eyes contiguous over a short distance, with short interfacetal hair, antennal ratio(AR)of 1.20, palpal ratio(PR)of 2.16, wing length of 0.83 mm, mandible with 15 teeth, one pale spot in base cell M1, one pale spot in cell M4, two pale spots in cell A, and two unequal spermathecae. Type specimens were deposited at the Center for Disease Control and Prevention of the Northern Theater Command.

Objective:To investigate the clinical characteristics of patients with multiple myeloma(MM)complicated by bone lesions and the risk factors associated with bone lesions.Methods:The clinical data of 294 newly diagnosed MM patients in Gansu Provincial Hospital from January 2017 to June 2021 were retrospectively analyzed.The patients were divided into the bone lesion group(154 cases)and the non-bone lesions group(140 cases)based on the presence of absence of bone lesions at diagnosis.The general data and laboratory parameters were compared between the two groups.The risk factors for bone lesions in MM patients were analyzed by logistic regression analysis,and the characteristic(ROC)curves were plotted to assess the predictive value of each risk factor for the occurrence of bone lesions in MM patients.Results:Compared to the non-bone lesion group,the bone lesion group had significantly higher serum calcium levels and significantly greater proportions of patients with Durie-Salmon(DS)stage Ⅲ,and bone pain(all P＜0.05).Logistic regression analysis showed that elevated serum calcium(OR=5.135,95％CI:1.931-13.653,P=0.001),DS stage Ⅲ(OR=1.841,95％CI:1.019-3.328,P=0.043),and bone pain(OR=8.208,95％CI:4.761-14.151,P＜0.001)were independent risk factors for bone lesions in MM patients.ROC curve analysis showed that serum calcium(AUC=0.619,95％CI:0.555-0.683,P＜0.001)and bone pain(AUC=0.743,95％CI:0.692-0.793,P＜0.001)had predictive value for bone lesions in MM patients.Conclusion:MM patients have a high incidence of bone lesions,and active monitoring and management of risk factors may improve treatment outcomes and prognosis.

Psoriasis is a chronic inflammatory skin disease with worldwide prevalence,primarily characterized by epidermal hyperplasia,abnormal keratinization,and immune cell infiltration,with a significant negative impact on patients' quality of life and mental well-being.The onset of psoriasis is closely associated with genetic susceptibility,immune dysregulation,and environmental factors.Despite research progress into the pathogenesis of psoriasis,existing treatment method still face problems including limited efficacy and obvious side effects.There is thus an urgent need for an in-depth analysis of its pathological network and the development of novel interventional strategies.The imiquimod-induced psoriasis animal model has accordingly become a crucial tool for studying psoriasis owing to its high reproducibility and excellent pathological simulation.This review systematically summarizes the core mechanism of action of the imiquimod-induced psoriasis model,expounds on the molecular basis of its action via pathways such as the cascade reaction of the core immune-inflammatory axis,the multi-regulatory network of downstream synergistic mechanisms,and the interaction between host and environmental factors.Research based on this model has successfully verified the therapeutic effects of various targeted therapies and natural products on psoriasis,demonstrating its important application value in therapeutic interventional research.We also discuss the limitations of the imiquimod-induced psoriasis model,and indicate future research directions,with the aim of providing references for further in-depth research and the treatment of psoriasis.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective This study aimed to assess the clinical utility of combining the Controlling Nutri-tional Status(CONUT)score with the Prognostic Nutritional Index(PNI)for evaluating anemia risk in elderly colorectal cancer patients and to establish a risk prediction model.Methods A total of 661 elderly colorectal cancer patients treated at Xinjiang Uygur Autonomous Region People's Hospital from July 2018 to March 2025 were included in this retrospective study.Patients were categorized into anemic and non-anemic groups and randomly assigned to a training set and validation set at a 7:3 ratio.The XGBoost algorithm was applied to develop a predictive model for anemia risk,and its performance was assessed using the receiver operating characteristic(ROC)curve.SHAP value visualization,and other methods.Results Among the 661 patients,257(38.9％)were diagnosed with anemia.Compared with the non-anemic group,patients in the anemic group had significantly lower levels of PNI and albumin,but higher CONUT scores and blood urea nitrogen levels.Additionally,the anemic group had higher proportions of tumor diameter≥5 cm,poorly differentiated tumors,and stage Ⅲ-Ⅳ disease(all P<0.05).The XGBoost model demonstrated good discriminatory ability,with an AUC of 0.897(95％CI:0.868～0.925).SHAP value analysis identified PNI,CONUT score,albumin,blood urea nitrogen,TNM stage,tumor differentiation,and tumor size as major contributing variables.PNI and albumin were protective factors,whereas CONUT score,blood urea nitrogen,and tumor-related features were risk factors.Conclusion Nutritional indicators such as PNI and CONUT score,along with tumor characteristics,can effectively predict the risk of anemia in elderly patients with colorectal cancer.The XGBoost-based predictive model demonstrates high discriminatory power and good inter-pretability,providing valuable support for early screening of high-risk patients and guiding individualized nutri-tional interventions and anemia management.

Objective This study aimed to assess the clinical utility of combining the Controlling Nutri-tional Status(CONUT)score with the Prognostic Nutritional Index(PNI)for evaluating anemia risk in elderly colorectal cancer patients and to establish a risk prediction model.Methods A total of 661 elderly colorectal cancer patients treated at Xinjiang Uygur Autonomous Region People's Hospital from July 2018 to March 2025 were included in this retrospective study.Patients were categorized into anemic and non-anemic groups and randomly assigned to a training set and validation set at a 7:3 ratio.The XGBoost algorithm was applied to develop a predictive model for anemia risk,and its performance was assessed using the receiver operating characteristic(ROC)curve.SHAP value visualization,and other methods.Results Among the 661 patients,257(38.9％)were diagnosed with anemia.Compared with the non-anemic group,patients in the anemic group had significantly lower levels of PNI and albumin,but higher CONUT scores and blood urea nitrogen levels.Additionally,the anemic group had higher proportions of tumor diameter≥5 cm,poorly differentiated tumors,and stage Ⅲ-Ⅳ disease(all P<0.05).The XGBoost model demonstrated good discriminatory ability,with an AUC of 0.897(95％CI:0.868～0.925).SHAP value analysis identified PNI,CONUT score,albumin,blood urea nitrogen,TNM stage,tumor differentiation,and tumor size as major contributing variables.PNI and albumin were protective factors,whereas CONUT score,blood urea nitrogen,and tumor-related features were risk factors.Conclusion Nutritional indicators such as PNI and CONUT score,along with tumor characteristics,can effectively predict the risk of anemia in elderly patients with colorectal cancer.The XGBoost-based predictive model demonstrates high discriminatory power and good inter-pretability,providing valuable support for early screening of high-risk patients and guiding individualized nutri-tional interventions and anemia management.

Aim To investigate the effect of oroxylin A(OA)on apoptosis in Ishikawa cell line of endometrial cancer and the underlying mechanism through the phosphatidylinositol-3 kinase/protein kinase B(PI3K/AKT)signaling pathway.Methods Ishikawa cells were treated with different concentrations of OA(0,4,8,10,12,and 20 μmol·L-1)for 24 h-72 h,the cell viability was detected by CCK-8 assay,apoptosis was detected by flow cytometry,and the protein ex-pression levels of B-cell lymphoma-2(Bcl-2),Bcl-2-associated X protein(Bax),PI3K/AKT,recombinant cytochrome P450 1B1(CYP1B1),and catechol-O-methyltransferase(COMT)were detected by Western blot technique.Results OA inhibited the prolifera-tion of Ishikawa cells in a concentration-and time-de-pendent manner.Compared with the blank control group,the expression of Bax protein increased signifi-cantly,while the expression of Bcl-2 protein decreased significantly with the increase of OA concentration.The expression of COMT protein increased significant-ly,while the expression of CYP1B1 protein decreased significantly.PI3K/AKT:IGF-1(PI3 K agonist)sup-plementation reversed the effect,the expression of COMT protein significantly decreased,and the expres-sion of CYP1B1 protein significantly increased.Con-clusions OA exerts anti-tumor effects in Ishikawa cells of endometrial cancer,which may be related to cell apoptosis mediated by the inhibition of the PI3K/AKT signaling pathway.

Objective:Bidirectional causal associations of 41 cytokines with atopic dermatitis(AD)were explored based on a Mendelian randomization(MR)approach.Methods:Pooled data from genome wide association study(GWAS)of 41 cytokines and AD were utilized for instrumental variable(IV)screening,and single nucleotide polymorphism(SNP)affecting the results of MR analyses was excluded by the MR-PRESSO outlier test as well as by the MR Steiger filtering method.Two-sample bidirectional MR analyses were performed using inverse variance weighting(IVW),MR-Egger regression,and weighted median methods(WM).MR-Egger intercept term test and Cochran's Q test were performed to test the pleiotropy and heterogeneity of IV,and MR results were visu-alized by scatterplots,funnel plots,and leave-one-out plots.Results:Forward MR analysis showed that MIG(IVW:OR=0.89;95%CI:0.81～0.97;P=0.006)reduced the risk of AD development.In contrast,IL-5(IVW:OR=1.17;95%CI:1.01～1.36;P=0.042)and IL-18(MR Egger:OR=1.17;95%CI:1.03～1.33;P=0.030)increased the risk of AD development.Inverse MR analysis showed a potential causal association between AD and increased MIG(IVW:Beta=0.10;95%CI:0.02～0.17;P=0.014).None of the sensitivity analyses indicated pleiotropy and heterogeneity of the included IV.Conclusion:MIG may be an important marker in the progression of AD with a potential bidirectional causal association with risk of morbidity.IL-5 and IL-18 have a potential positive causal association for AD.