Objective:To discuss the influencing factors of angina pectoris in the patients with diabetes mellitus(DM),to construct a Bayesian network model to explore the network relationships among the influencing factors,and to predict the risk of angina pectoris in the patients with DM.Methods:Based on the UK Biobank(UKB)database,the Logistic regression aralysis model was used to screen the influencing factors of angina pectoris in the patients with DM.The taboo search algorithm was used for structure learning,and the Bayesian parameter estimation method was used for parameter learning to construct the Bayesian network model.Results:A total of 22 712 DM patients were included.The influencing factors of angina pectoris in the patients with DM included 14 variables:gender,age,body mass index(BMI),triglycerides(TG),total cholesterol(TC),glycated hemoglobin(HbA1c),hypertension,maternal smoking around delivery,smoking status,alcohol consumption,regular exercise,insomnia,sleep duration,and childhood relative body size(P<0.05).A Bayesian network model was constructed with 15 nodes and 22 directed edges.Among them,age,HbA1c,hypertension,regular exercise,BMI,and sleep duration were directly associated with the occurrence of angina pectoris in the patients with DM,while gender,smoking status,alcohol consumption,TC,TG,insomnia,childhood relative body size,and maternal smoking around delivery were indirectly associated with the occurrence of angina pectoris in the patients with DM.Conclusion:Age,HbA1c,hypertension,regular exercise,BMI,and sleep duration are direct influencing factors of angina pectoris in the patients with DM.Controlling HbA1c,blood pressure,and BMI levels,engaging in regular exercise,and maintaining appropriate sleep duration are beneficial for reducing the risk of angina pectoris in the patients with DM.

Objective:To screen the Alzheimer's disease(AD)-related genes and construct its diagnostic model using bioinformatics technology and machine learning(ML)algorithms,to discuss the immunological characteristics of AD patients,and to provide novel biomarkers for AD diagnosis.Methods:The AD-related gene expression dataset GSE125583 was downloaded from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified through differential analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses were performed to explore the biological functions and signaling pathways of DEGs.A protein-protein interaction(PPI)network was constructed,and hub genes were screened using Cytoscape software combined with three ML algorithms:Least Absolute Shrinkage and Selection Operator(LASSO),eXtreme Gradient Boosting(XGBoost),and Random Forest(RF).The screened hub genes were utilized to build an AD diagnostic model via RF,followed by feature importance ranking.The model's efficacy and key genes were evaluated using a test set.Single-sample gene set enrichment analysis(ssGSEA)was used for immune cell infiltration analysis between AD group and control group.Results:Differential analysis identified 1 287 DEGs.The GO functional enrichment analysis results revealed that DEGs were primarily involved in biological functions related to neural signaling,synapses,and vesicles.KEGG signaling pathway enrichment analysis indicated significant enrichment of DEGs in ion transport,neurotransmitter,and ligand-gated channel pathways.Nine overlapping hub genes were screened by the three ML algorithms.In the AD diagnostic model,the top four key genes with highest diagnostic performance were adenylate cyclase-activating polypeptide 1(ADCYAP1),brain-derived neurotrophic factor(BDNF),platelet-derived growth factor receptor β(PDGFRB),and C-X-C motif chemokine receptor 4(CXCR4),with corresponding area under the curve(AUC)values of 0.852,0.795,0.820,and 0.756,respectively.The model achieved an AUC of 0.828,accuracy of 81.25%,sensitivity of 84.40%,and specificity of 71.43%.The immune cell infiltration analysis results demonstrated higher infiltration of macrophages,monocytes,natural killer(NK)cells,and lymphocytes in AD tissue.Among these,NK/natural killer T(NKT)cells and plasmacytoid dendritic cells showed significant correlations with the four key genes(P<0.05).Conclusion:The feature genes screened based on bioinformatics and ML exhibit diagnostic potential for AD.Genes such as ADCYAP1 may serve as potential biomarkers for AD diagnosis,offering significant implications for early prevention and treatment.

Skin diseases significantly affect the quality of life of approximately 190 million individuals worldwide.The complexity and diversity of their clinical manifestations are the major challenges for traditional diagnostic approaches,and exploring novel diagnostic strategies has become an urgent priority.In recent years,deep learning(DL)technology has been increasingly applied in the intelligent recognition of skin diseases,demonstrating substantial potential.This study provides a systematic review of the research progress of DL in dermatological diagnosis from three major dimensions.First,at the data input level,it focuses on the characterization and preprocessing of multimodal data,including dermoscopic images,ultrasound images,and histopathological slides.Second,at the algorithmic model level,it explores ensemble learning frameworks,multimodal data fusion strategies,multicenter collaborative training approaches,and interpretable model construction.Finally,at the task recognition level,it evaluates the performance of DL models in benign skin disease screening,malignant skin lesion differentiation,and binary as well as multiclass classification tasks.By comprehensively reviewing advancements in DL-based skin disease diagnostic models from multiple perspectives,this paper aims to provide valuable insights for the further optimization and clinical translation of intelligent diagnostic systems.

Objective:To screen the main characteristic variables affecting the incidence of cardiovascular and cerebrovascular diseases,and to construct the Bayesian network model of cardiovascular and cerebrovascular disease incidence risk based on the top 10 characteristic variables,and to provide the reference for predicting the risk of cardiovascular and cerebrovascular disease incidence.Methods:From the UK Biobank Database,315 896 participants and related variables were included.The feature selection was performed by categorical boosting(CatBoost)algorithm,and the participants were randomly divided into training set and test set in the ratio of 7∶3.A Bayesian network model was constructed based on the max-min hill-climbing(MMHC)algorithm.Results:The prevalence of cardiovascular and cerebrovascular diseases in this study was 28.8％.The top 10 variables selected by the CatBoost algorithm were age,body mass index(BMI),low-density lipoprotein cholesterol(LDL-C),total cholesterol(TC),the triglyceride-glucose(TyG)index,family history,apolipoprotein A/B ratio,high-density lipoprotein cholesterol(HDL-C),smoking status,and gender.The area under the receiver operating characteristic(ROC)curve(AUC)for the CatBoost training set model was 0.770,and the model accuracy was 0.764;the AUC of validation set model was 0.759 and the model accuracy was 0.763.The clinical efficacy analysis results showed that the threshold range for the training set was 0.06-0.85 and the threshold range for the validation set was 0.09-0.81.The Bayesian network model analysis results indicated that age,gender,smoking status,family history,BMI,and apolipoprotein A/B ratio were directly related to the incidence of cardiovascular and cerebrovascular diseases and they were the significant risk factors.TyG index,HDL-C,LDL-C,and TC indirectly affect the risk of cardiovascular and cerebrovascular diseases through their impact on BMI and apolipoprotein A/B ratio.Conclusion:Controlling BMI,apolipoprotein A/B ratio,and smoking behavior can reduce the incidence risk of cardiovascular and cerebrovascular diseases.The Bayesian network model can be used to predict the risk of cardiovascular and cerebrovascular disease incidence.

[Objective] To investigate the causal relationship between the relative intake of dietary components (including carbohydrate, added sugar, protein, and fat) and kidney stones using two sample Mendelian randomization (MR). [Methods] The genetic instrumental variables (IVs) from genome-wide association study (GWAS) statistics, exposure-related summary data from the SSGAC genome study, and kidney stone data from the Finn Gen Consortium were collected, which were then analyzed with inverse variance weighting (IVW). The MR results and sensitivity were assessed with MR-Egger, weighted median, MR-PRESSO test, Cochran's Q test, leave-one analysis, and Steiger filter. [Results] IVW results showed that the relative intake of carbohydrate (OR=2.01, 95%CI: 1.02-3.96, P=0.042) and added sugar (OR=2.07, 95%CI: 1.00-4.29, P=0.049) had a causal relationship and were risk factors for the development of kidney stones.Relative intake of protein (OR=0.70, 95%CI: 0.38-1.29, P=0.249) and fat (OR=0.79, 95%CI: 0.27-2.35, P=0.671) were not associated with kidney stones.Sensitivity analysis showed no heterogeneity or pleiotropy (P>0.05). [Conclusion] Relative intake of carbohydrate and added sugar are risk factors for kidney stones, suggesting that limiting carbohydrate and added sugar intake may prevent the development of kidney stones.

Chinese herbal piece is an important component of the traditional Chinese medicine （TCM） system， and identifying their quality and grading can promote the development and utilization of Chinese herbal pieces. Utilizing deep learning for intelligent identification of Chinese herbal pieces can save time， effort， and cost， while also reasonably avoiding the constraints of human subjectivity， providing a guarantee for efficient identification of Chinese herbal pieces. In this study， a dataset containing 108 kinds of Chinese herbal pieces （14 058 images） was constructed，the basic YOLOv4 algorithm was employed to identify the 108 kinds of Chinese herbal pieces of our database The mean average precision （mAP） of the developed basic YOLOv4 model reached 85.3%. In addition， the receptive field block was introduced into the neck network of YOLOv4 algorithm， and the improved YOLOv4 algorithm was used to identify Chinese herbal pieces. The mAPof the improved YOLOv4 model achieved 88.7%， the average precision of 80 kinds of decoction pieces exceeded 80%， the average precision of 48 kinds of decoction pieces exceeded 90%. These results indicate that adding the receptive field module can help to some extent in the identification of Chinese herbal medicine pieces with different sizes and small volumes. Finally， the average precision of each kind of Chinese herbal medicine piece by the improved YOLOv4 model was further analyzed. Through in-depth analysis of the original images of Chinese herbal medicine pieces with low prediction average precision， it was clarified that the quantity and quality of original images of Chinese herbal medicine pieces are key to performing intelligent object detection. The improved YOLOv4 model constructed in this study can be used for the rapid identification of Chinese herbal pieces， and also provide reference guidance for the manual authentication of Chinese herbal medicine decoction pieces.

Objective:To evaluate the safety, feasibility and operational performance of self-developed medical disposable portable endoscopy (YunSendo) for upper gastrointestinal endoscopy examination in Ba-Ma mini-pigs.Methods:A total of 10 Guangxi Ba-Ma mini-pigs were used in the experiment, and mucosal injury models were established in advance by biopsy forceps in esophagus, stomach, and duodenum. Each experimental animal underwent medical disposable portable endoscopy and Olympus endoscopy (GIF-Q260J) performed by two endoscopists separately. The time when the endoscope reached the duodenum, the number of detected mucosal injuries and endoscopic pictures of different parts with standard image acquisition were recorded. Endoscopic operational performance and endoscopic image quality were evaluated. Different endoscopists recorded experimental results with blind method. The procedures of the two endoscopic examinations were performed by coin-tossing method. The paired t test was used for statistical analysis. Results:There were no statistically significant differences in the insertion time and total operation time between medical disposable portable endoscopy and Olympus endoscopy ( (171.00±9.96) s vs. (164.00±17.84) s, (285.00±33.94) s vs. (273.40±23.46) s; t=1.289 and 1.281, P=0.230 and 0.232). There were no statistically significant differences in the percentage of time of clear visual field during endoscopy insertion and total operation between medical disposable portable endoscopy and Olympus endoscopy ((91.83±1.85)% vs. (91.52±1.51)%, (93.07±3.10)% vs. (92.06±2.57)%; t=0.401 and 0.689, P=0.698 and 0.508). Moreover, there were no statistically significant differences in the score of comprehensive operation performance, score of clear image number, score of image color recognition, score of image illumination, comprehensive score of image quality and number of detected mucosal injuries ((9.66±0.30) points vs. (9.86±0.15) points, (39.50±0.71) points vs. (39.30±1.06) points, (39.70±0.48) points vs. (39.40±0.70) points, (39.40±0.70) points vs. (39.50±0.71) points, (9.88±0.09) points vs. (9.85±0.20) points, 9.80±0.42 vs. 9.90±0.32; t=2.176, 1.000, 1.152, 0.317, 0.629 and 0.557, all P>0.05). There were no adverse events after operation in medical disposable portable endoscopy group and Olympus endoscopy group. Conclusions:The medical disposable portable endoscopy is safe and feasible for endoscopy examination in live animal models. Different parts of upper gastrointestinal tract and mucosal lesions can be clearly detected. The operational performance and the image quality are excellent, which is similar to Olympus endoscopy (GIF-Q260J).

Objective To investigate the effect of sepsis on vecuronium-induced inhibition of acetylcholine release in neuromuscular junction in rats.Methods Thirty-six adult male SPF SpragueDawley rats,aged 2-3 months,weighing 200-220 g,were randomly divided into 3 groups (n=12 each) using a random number table:control group (group C),sham operation group (group S) and sepsis group (group Sep).Sepsis was induced by cecum ligation and puncture (CLP) in rats anesthetized with intraperitoneal chloral hydrate 350 mg/kg.At 12 h after CLP,the sciatic nerve-pretibial muscle was prepared.Vecuronium was added to the culture medium with the final concentration of 0.08 μg/ml,and the sciatic nerve-pretibial muscle was incubated for 15 min.Before and after administration,evoked endplate potentials (EPPs) and miniature endplate potentiais (MEPPs) were recorded by using intracellular microelectrode.EPP/MEPP ratio was calculated.Results Compared to C and S groups,EPPs,MEPPs and EPP/MEPP ratio were significantly increased before and after administration in group Sep.EPPs,MEPPs and EPP/MEPP ratio were significantly lower after administration than before administration in the three groups.Conclusion Sepsis can promote acetylcholine release in neuromuscular junction,thus weakening vecuronium-induced inhibition of acetylcholine release in neuromuscular junction in rats.