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 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 effect of dexmedetomidine combined with ropivacaine for quadratus lumborum block at the lateral supra-arcuate ligament(QLB-LSAL) in open hepatocellular carcinoma resection.Methods A prospective study was conducted in 60 patients who underwent elective open hepatocellular carcinoma resection at Jining First People's Hospital. The patients were randomly divided into the compound group and the control group,with 30 cases in each group. Patients in the compound group received QLB-LSAL combined general anesthesia,and patients in the control group received simple general anesthesia. All patients underwent patient controlled intravenous analgesia (PCIA)postoperatively. The mean arterial pressure (MAP),heart rate (HR) and visual analogue scale (VAS) scores during rest and coughing at different time points were observed and compared between the two groups. The number of postoperative PCIA compressions,the dosage of sufentanil,the first postoperative exhaust time,the first postoperative ambulation time,the hospital stay and the occurrence of adverse reactions of the two groups were recorded. Results In the compound group,the HR and MAP were significantly lower than those of the control group at the time of skin incision (T2) and at the end of surgery (T3);the VAS scores during rest and coughing were significantly lower than those of the control group at the time of exiting the anesthesia recovery room and 6 hours and 12 hours after surgery;and the PCIA compression times were significantly less than those of the control group;the dosage of sufentanil was significantly lower than that in the control group 0 to 24 hours after surgery,and the dosage of sufentanil was higher than that in the control group 25 to 48 hours after surgery;the first postoperative ambulation time and the first postoperative exhaust time were significantly earlier than those in the control group;and the above differences were statistically significant(P<0.05). There was no significant difference in the HR or MAP at 5 minutes into the operating room (T0) and 5 minutes before skin incision (T1),VAS scores during rest and coughing 24 hours and 48 hours after surgery,hospital stay and incidence of adverse reactions between the two groups (P>0.05).Conclusion For patients with open hepatocellular carcinoma resection,dexmedetomidine combined with ropivazine for QLB-LSAL can provide more ideal postoperative analgesia,reduce perioperative opioid consumption,and have less impact on circulatory system,which is conducive to rapid postoperative recovery.

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.

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 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.