ObjectiveTo explore the relationship between acne vulgaris and gut microbiota. MethodsA total of 29 clinical cases diagnosed with moderate-to-severe acne vulgaris and 26 healthy individuals as control subjects were recruited. Fecal specimens were collected from all participants， and further analysis of gut microbial communities was performed by leveraging high-throughput sequencing techniques that target the hypervariable regions of 16S rRNA genes. ResultsAssociations between acne vulgaris and alterations in gut microbiota were identified. At the phylum level， the relative abundance of Bacteroidota exhibited a statistically significant elevation in the acne vulgaris cohort when compared with the healthy control group （P<0.01）， while Cyanobacteria was significantly lower in the acne group （P<0.01）. At the genus level， the top five different bacterial taxa in both groups were Bacteroides， Escherichia⁃Shigella， Klebsiella， Roseburia， and Parabacteroides. Among them， Bacteroides， Roseburia， and Parabacteroides were more abundant in acne patients. Linear discriminant analysis identified five biomarkers all belonging to the Bacteroidota phylum in the acne and control groups. These biomarkers belong to the phylum Bacteroidetes. ConclusionThere are significant differences in the composition of intestinal microbiota between acne patients and healthy people. Changes in the richness of specific bacterial genera may become new targets for the diagnosis and treatment of acne.

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 evaluate the efficiency of intra-tumor and peri-tumor ultrasound radiomics models based on machine learning algorithms for predicting benign and malignant Breast Imaging Reporting and Data System (BI-RADS) 4 breast lesions, and provide insights into early diagnosis of breast cancer. Methods A retrospective analysis was conducted based on the medical records of 450 female patients who underwent breast ultrasound examination in the Affiliated Cancer Hospital of Xinjiang Medical University from June 2020 to April 2022. The patients were divided into the benign (n = 199) and malignant (n = 195) groups according to pathological examination, and randomized into the training (n = 275) and validation (n = 119) sets at a ratio of 7∶3. Radiomics features were extracted and screened. Intra-tumor, peri-tumor, and intra-tumor + peri-tumor ultrasound radiomics models were constructed based on three machine learning algorithms, including logistic regression (LR), support vector machine (SVM), and multi-layer perceptron (MLP). Receiver operating characteristics (ROC) curves, calibration curves, and decision curves were plotted to evaluate the efficacy of the radiomics models for prediction of benign and malignant breast lesions. Results A total of 17 intra-tumor, 16 peri-tumor, and 17 intra-tumor + peri-tumor radiomics features were selected for model construction. Based on LR, MLP, and SVM algorithms, the intra-tumor + peri-tumor radiomics models showed higher predictive efficacy than intra-tumor and peri-tumor radiomics models. The predictive efficacy of intra-tumor, peri-tumor, and intra-tumor + peri-tumor radiomics models were higher based on the SVM algorithm than based on LR and MLP algorithms. For the intra-tumor radiomics model based on the SVM algorithm, the area under the ROC curve (AUC), accuracy, sensitivity, and a specificity were 0.909, 0.851, 0.860, and 0.842, respectively, in the training set and 0.866, 0.832, 0.847, and 0.817, respectively, in the validation set. For the peri-tumor radiomics model based on the SVM algorithm, these values were 0.899, 0.855, 0.882, and 0.827, respectively, in the training set and 0.844, 0.815, 0.847, and 0.783, respectively, in the validation set. For the intra-tumor + peri-tumor radiomics model based on the SVM algorithm, these values were 0.943, 0.876, 0.860, and 0.892, respectively, in the training set and 0.881, 0.849, 0.915, and 0.783, respectively, in the validation set. Conclusion The intra-tumor and peri-tumor ultrasound radiomics models based on machine learning algorithms are highly valuable for prediction of benign and malignant BI-RADS 4 breast lesions. The intra-tumor + peri-tumor ultrasound radiomics model based on the SVM algorithm has the optimal efficacy for prediction of benign and malignant BI-RADS 4 breast lesions.

Objective To prepare in-house coagulation factor Ⅷ(F Ⅷ)inhibitor-positive control material and evaluate its perform-ance.Methods Frozen plasma samples from hemophilia A patients with positive factor Ⅷ inhibitors were pooled,and diluted with Owren's Veronal Buffer(OVB)to 1 BU/mL of the inhibitor concentration in the mixture,then aliquoted and freeze-stored.The homo-geneity and stability of the in-house quality control material were verified,and its suitability was further assessed through intra-laborato-ry reproducibility among different technologists and inter-laboratory comparisons.Results Twenty-one aliquots were randomly tested for homogeneity assessment,yielding an average of 1.05 BU/mL(range 0.9-1.15 BU/mL),with a standard deviation(SD)of 0.083 and coefficient of variation(CV)of 7.90％.The freshly prepared inhibitor-positive control samples contained a concentration of 1.03 BU/mL.After storage at-80℃ for 24 hours,1 week,1 month,2 months,3 months,4 months,5 months,6 months,7 months,8 months,and 9 months,thawed the samples showed relative deviations of 9％,0％,10％,9％,14％,15％,6％,0％,-10％,-5％,and 2％,respectively.The intra-laboratory CV value from different technologists at this center was 7.28％,and the inter-labora-tory CV across different centers was 18.75％.Conclusion The prepared in-house positive control material of Factor Ⅷ inhibitor ex-hibited adequate uniformity and stability.

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.

Objective:To analyze the gene chip related to dermatomyositis based on bioinformatics,to explore the immune in-filtration mechanism of key genes in dermatomyositis by CIBERSORT deconvolution algorithm,and to predict the therapeutic targets of dermatomyositis by network pharmacology.Methods:The gene microarray of dermatomyositis was searched in GEO database,and the differentially coexpressed genes were screened and analyzed.The differentially coexpressed genes were analyzed by GO analysis,KEGG analysis,protein interaction network construction(PPI)by R software package.Verify the expression levels of key genes,and the correlation of immune cell infiltration was analyzed by CIBERSORT deconvolution method.Through the medical ontology informa-tion retrieval platform Coremine medical database,the traditional Chinese medicine treatment targets of dermatomyositis were screened and summarized.Results:A total of 196 differentially expressed genes were screened.GO enrichment analysis showed that these differentially expressed genes were mainly concentrated in defense response to virus,blood particles,double-stranded RNA binding,polypeptide antigen binding,and so on.KEGG enrichment analysis showed that it was enriched in RIG-Ⅰ-like receptor sig-nal pathway,Toll-like receptor signal pathway and other signal pathways related to the pathogenesis of dermatomyositis.Finally,four key genes of dermatomyositis,STAT1,ISG15,IRF7 and IRF9 were obtained.Through CIBERSORT algorithm,M1 macrophages,M2 macrophages and CD8+T cells were the three kinds of cells with the highest average proportion and the most obvious immune infil-tration,and there was a significant positive correlation between activated natural killer cells and activated dendritic cells,while there was a significant negative correlation between resting mast cells and activated mast cells.The therapeutic targets of traditional Chinese medicine such as fish brain stone were predicted based on Coremine medical database;through channel analysis,it could be found that these traditional Chinese medicines are mainly attributed to liver meridian,lung meridian,spleen meridian;efficacy analysis is mainly focused on clearing heat,detoxification,promoting blood circulation and removing blood stasis,relieving cough and resolving phlegm and so on.Conclusion:Four key genes and some key signal pathways of dermatomyositis,STAT1,ISG15,IRF7 and IRF9 were obtained by bioinformatics method,the immune infiltration mechanism was analyzed by CIBERSORT algorithm,and the thera-peutic potential targets of traditional Chinese medicine were screened out to provide direction for the pathogenesis and treatment of der-matomyositis.

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:To investigate the impacts of nuclear receptor-interacting protein 1(NRIP1)on sepsis-evoked intesti-nal epithelial injury via transcriptional regulation of high mobility group box 1(HMGB1).Methods:The expression levels of NRIP1 and HMGB1 were detected by RT-qPCR and Western blot.The pathological changes of intestinal tissue were detected by HE staining.CCK-8 assay determined the optimal treatment time of LPS.Caco-2 cells were transfected with NRIP1 small interfering RNA(siRNA-NRIP1-1/2),and cell viability and apoptosis were detected by CCK-8 assay and flow cytometry,respectively.RT-qPCR and Western blot examined the expressions of inflammation-associated factors.Transepithelial resistance(TEER)was used to detect intestinal epi-thelial permeability.Western blot was used to detect the expressions of apoptosis and tight-junction related proteins.The binding rela-tionship between NRIP1 and HMGB1 was verified by luciferase reporting assay and chromatin immunoprecipitation assay(ChIP).After knocking down NRIP1 and overexpressing HMGB1 in LPS-treated Caco-2 cells,the functional experiment was performed again.Results:NRIP1 expression was fortified in the intestinal tissues of sepsis rats and LPS-treated Caco-2 cells.Interference with NRIP1 attenuated LPS-elicited Caco-2 cell viability injury,apoptosis,inflammatory response and barrier damage.Additionally,NRIP1 might activate HMGB1 expression at transcriptional level and HMGB1 elevation might reverse the impacts of NRIP1 absence on Caco-2 cell viability,apoptosis,inflammatory response as well as barrier function.Conclusion:NRIP1 may promote sepsis-elicited intestinal epi-thelial injury,which may be related to transcriptional activation of HMGB1.