Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Aim To explore the effect of astragalosideⅣ(AS-Ⅳ)on lipopolysaccharide(LPS)-induced po-larization and migration of RAW 264.7 macrophages and the underlying mechanism.Methods 1 mg·L-1 LPS was used to construct cell migration model.Scratch assay was utilized to determine cell migration rate.Immunofluorescence staining was utilized to de-tect the expression and location of F4/80,iNOS and Arg-1.CCK-8 assay was used to determine the viabili-ty of RAW 264.7 cells.Griess assay was used to measure NO content.Molecular docking was used to analyze the interaction between AS-Ⅳ and the core tar-gets such as cGAS and STING protein.Western blot was employed to detect the expression of iNOS,Arg-1,cGAS,STING,NF-κB p65 and p-NF-κB p65 protein.Results AS-Ⅳ significantly inhibited the migration and M1 polarization of RAW 264.7 cells induced by LPS.Moreover,AS-Ⅳ could interact with cGAS and STING protein,especially cGAS.Further Western blot assay showed that AS-Ⅳ significantly downregulated the expression of iNOS,cGAS,STING and p-NF-κB p65 protein.Conclusions AS-Ⅳ could promote mac-rophage M1 to M2 polarization,thereby inhibited mac-rophage migration through restraining the cGAS/STING/NF-κB signaling pathway,which provides a new therapeutic target for AS-Ⅳ to improve the early inflammatory response of AS.

Objective To explore the application value of MRI diaphragmatic navigation technology combined with three dimensional liver acquisition with volume acceleration-flexible(3D LAVA-FLEX)sequence in abdominal enhanced imaging of infants and young children.Methods A retrospective analysis was conducted on imaging data of 84 infants and young children who underwent abdominal enhanced MRI examination in our hospital between January 2021 and December 2023.All 84 infants and young children initially underwent conventional dynamic contrast-enhanced 3D LAVA-FLEX sequence scanning;the delayed phase images obtained were included in the dynamic enhancement group.Subsequently,diaphragmatic navigation combined with 3D LAVA-FLEX sequence examination was implemented,and the obtained images were included in the diaphragm navigation group.Subjective scoring was performed for images in both groups,while the signal to noise ratio(SNR),contrast to noise ratio(CNR),and artifact quantification(AQ)were measured and compared between the two groups.Results The respiratory motion artifacts,the clarity of liver parenchyma enhancement,the clarity of liver vascular enhancement,the clarity of spleen parenchyma enhancement and the overall image quality score in the diaphragm navigation group were higher than those in the dynamic enhancement group,and the differences were statistically significant(P<0.05).There were statistically significant differences in SNR and AQ between the two groups of images(P<0.000 1),while there was no statistically significant difference in CNR between the two groups of images(P>0.05).Conclusion Diaphragmatic navigation technology combined with 3D LAVA-FLEX sequence imaging can improve the image quality of abdominal MRI enhanced imaging in infants and young children,and provide a reference for clinical diagnosis and treatment.

Abdominal war trauma is a common and high-risk type of injury in the modern battlefield,with rapid changes in condition and a high mortality rate.There is an urgent need for emerging medical technologies to improve the efficiency and success rate of first aid for military casualties.With the development of artificial intelligence(AI),5G,and other emerging technologies,the concept of intelligent medical treatment is gradually forming and can assist in the diagnosis and treatment of abdominal trauma.This paper reviews the characteristics of abdominal war trauma in modern wars,discusses the application of intelligent medical treatment for abdominal war trauma and its drawbacks to be solved,aiming to provide reference for research related to abdominal war trauma.

Objective To explore the correlation between gallbladder stones and small intestinal bacterial overgrowth(SIBO).Methods A retrospective analysis was conducted on the clinical data of 393 patients who attended the Department of Gastroenterology of the Sixth Medical Center of Chinese PLA General Hospital from January 2021 to September 2023.They were divided into gallbladder stones group(n=190)and control group(n=203)based on the presence of gallbladder stones.Their general clinical data,laboratory test results,and abdominal symptoms were compared.Multivariate logistic regression was used to analyze the risk factors for gallbladder stones.Additionally,the total population was divided into SIBO-positive group(n=239)and SIBO-negative group(n=154),and their clinical characteristics were analyzed by logistic regression to explore the risk factors for SIBO.Results Univariate analysis revealed that gallbladder stones group had a higher rate of age,body mass index(BMI),fasting plasma glucose(FPG),glutaminase levels,prevalence of hypertension,diabetes,coronary heart disease,non-alcoholic fatty liver disease,gallbladder polyps,and SIBO,as well as a higher prevalence of CH4-positive and H2-positive in SIBO group than control group(P＜0.05).In terms of abdominal symptoms,the incidence of bad breath(48.4%vs.35.5%),dyspepsia(38.4%vs.28.6%),abdominal pain(30.5%vs.14.8%),bloating(42.1%vs.28.6%),diarrhea(20.5%vs.7.4%),and more exhaustion(46.8%vs.34.5%)were significantly higher in gallbladder stones group than those in control group(P＜0.05).Multivariate logistic regression analysis showed that independent positive determinants for incident gallbladder stones were age,BMI,FPG,total bilirubin(TBIL),coronary heart disease,gallbladder polyps,and SIBO.Univariate analysis revealed that age,prevalence of gallbladder stones,proportion of single stones,triglycerides(TG),total cholesterol(TC),and low-density lipoprotein cholesterol(LDL-C)were significantly higher in SIBO-positive group than those in SIBO-negative group(P＜0.05).Multivariate logistic regression analysis showed that the risk factors for SIBO were age,coronary heart disease,and gallbladder stones,while the protective factor for SIBO was high-density lipoprotein cholesterol(HDL-C).Conclusion There is a significant correlation between gallbladder stones and small SIBO;interventions on related factors of gallbladder stones and small SIBO may help reduce their incidence.

Objective To establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

Objective To explore the diagnostic value of electrocardiogram parameters combined with serum microRNA(miR)-322 and miR-568 levels in patients with chronic heart failure(CHF)complicated with ventricular arrhythmias(VA).Methods A total of 230 CHF patients admitted to Zhangjiakou First Hospital from April 2020 to April 2023 were selected as the study objects,including 120 VA patients(VA group)and 110 non VA patients(non VA group),and 110 patients who underwent health examinations in Zhangjiakou First Hospital during the same period as the control group.Compared three sets of general information,electrocardiogram parameters,left ventricular ejection fraction(LVEF),cardiac function grade and serum miR-322 and miR-568 levels.Logistic regression analyzed the influencing factors of VA in CHF patients.Receiver operating characteristic curve(ROC)analyzed electrocardiogram parameters combined with serum miR-322 and miR-568 for the diagnostic value of VA in CHF patients.Results The three groups showed statistically obvious differences in classification of nyha heart function(NYHA)and left ventricular ejection fraction(LVEF)(F=6.033,691.853,all P<0.05).The electrocardiogram parameters of CHF patients,including QT interval dispersion(QTD),QRS wave duration and corrected QT systolic time(QTc)were obviously higher than those in the control group(t=16.539,17.709,14.414),and the VA group were obviously higher than the non VA group(q=10.984,7.794,10.174),and the differences were statistically significan(all P<0.05).The serum levels of miR-322 and miR-568 were obviously lower than those in the control group(t=23.719,17.359).and the VA group were obviously lower than the non VA group(q=10.345,9.941),the differences were statistically significant(all P<0.05).Logistic regression analysis showed that NYHA grading,LVEF,QTD,QRS,wave duration,QTc,serum miR-322 and miR-568 levels were all influencing factors in the complication of VA in patients with CHF(Wald χ2=4.267～9.839,all P<0.05).The combination of electrocardiogram parameters and serum miR-322,miR-568 was better diagnosing CHF patients with concurrent VA that QTD,QRS duration,QTc and serum miR-332,miR-568 were measured separately.Conclusion Serum levels of miR-322 and miR-568 are obviously reduced,and the combination of electrocardiogram parameters with serum miR-322 and miR-568 has high diagnostic value for CHF patients with VA.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Stent entrapment is a rare complication of percutaneous coronary intervention.In recent years,with the development of distal radial artery puncture technology,the rare complications related to distal radial artery have been gradually understood.This article describes a patient who underwent coronary intervention through a distal radial approach,and the stent was dislodged and trapped in the far radial artery.The patient came to our hospital for stent implantation because of acute extensive anterolateral myocardial infarction.During the intervention,the balloon could not be filled when the stent was released from the left anterior descending artery,and the retracting stent could not be used to remove the guide catheter.The stent was dislodged and embedded in the distal vessel.The sheath was inserted through the proximal radial reverse puncture,and the stent was captured with a snare and removed.

Objective To predict the risk of coronary atherosclerosis using cerebral small vessel disease and cerebral artery stenosis in order to assist early identification of coronary artery disease(CHD).Methods A retrospective analysis was conducted on 130 patients(aged≥65 years)who underwent cerebral MRI,magnetic resonance angiography(MRA),and coronary computed tomo-graphy angiography in the Chinese PLA General Hospital between January 2019 and December 2024.Based on coronary computed tomography angiography results,the patients were categorized into an asymptomatic CHD group(56 cases)and a non-CHD group(74 cases).Cerebral MRI and MRA were used to assess white matter hyperintensity,enlarged perivascular spaces,lacunar infarcts,cerebral microbleeds(CMB)scores,total score of cerebral small vessel disease and degree of cerebral artery stenosis.Multivariate logistic regression analysis was employed to identify the clinical and radiological indicators related to CHD.Then a predictive model was constructed,and then its performance in early predicting CHD was evaluated.Results Blood glucose,low-density lipoprotein cholesterol,CMB score≥ 1,and≥50％stenosis of the posterior cerebral artery were independent risk factors for CHD(P＜0.05).The combined predictive model integrating clinical indicators,cerebral small vessel disease,and cerebral artery stenosis demonstrated the best performance,with an AUC value of 0.867(95％CI:0.807-0.927),outperforming the clinical model and the clinical-cerebral small vessel disease model.Conclusion cerebral small vessel dis-ease and cerebral artery stenosis are closely related to coronary atherosclerosis.The predictive model integrating clinical features and cerebrovascular disease imaging markers provides impor-tant reference for early screening of CHD.