Objectives:To investigate the predictive value of epicardial fat volume(EFV)and atrioventricular groove fat thickness(AVGT)—morphological biomarkers of epicardial adipose tissue—for major adverse cardiovascular events(MACE)in patients with dilated cardiomyopathy(DCM).Methods:This study enrolled 216 DCM patients.EFV and AVGT were obtained from cardiac magnetic resonance imaging(CMR).Patients were divided into event-free group(n=142)and event group(n=74)based on MACE occurrence during follow-up.Receiver operating characteristic(ROC)curve analysis was used to determine optimal cutoff values.Survival differences were assessed using Kaplan-Meier analysis,Cox proportional hazards regression analysis was used to identify independent risk factors,and restricted cubic spline(RCS)models were used to evaluate dose-response relationships.Results:AVGT and EFV were significantly higher in the event group than in event-free group(both P<0.05).ROC analysis identified optimal MACE-predicting cutoffs as follows:AVGT≥7.74 mm(area under the curve[AUC]=0.57)and EFV≥78.6 ml(AUC=0.62).Kaplan-Meier analysis revealed significantly lower MACE-free survival rates in patients with AVGT≥7.74 mm and EFV≥78.6 ml(both P<0.05).Cox regression analysis confirmed that AVGT(HR=2.18,95%CI:1.34-3.54)and EFV(HR=1.81,95%CI:1.11-2.96)were independent MACE risk factors(both P<0.05)in this patient cohort.RCS models demonstrated the significant linear associations between EFV/AVGT and MACE risk(bothoverall P<0.05).Conclusions:EFV and AVGT,the non-invasive imaging biomarkers quantifying and characterizing fat distribution,are independently correlated with elevated MACE risk in DCM patients.These metrics serve as potential prognostic indicators,enriching risk stratification indicators for early identification of high-risk patients and guiding personalized medication strategies.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

Objectives:To investigate the predictive value of epicardial fat volume(EFV)and atrioventricular groove fat thickness(AVGT)—morphological biomarkers of epicardial adipose tissue—for major adverse cardiovascular events(MACE)in patients with dilated cardiomyopathy(DCM).Methods:This study enrolled 216 DCM patients.EFV and AVGT were obtained from cardiac magnetic resonance imaging(CMR).Patients were divided into event-free group(n=142)and event group(n=74)based on MACE occurrence during follow-up.Receiver operating characteristic(ROC)curve analysis was used to determine optimal cutoff values.Survival differences were assessed using Kaplan-Meier analysis,Cox proportional hazards regression analysis was used to identify independent risk factors,and restricted cubic spline(RCS)models were used to evaluate dose-response relationships.Results:AVGT and EFV were significantly higher in the event group than in event-free group(both P<0.05).ROC analysis identified optimal MACE-predicting cutoffs as follows:AVGT≥7.74 mm(area under the curve[AUC]=0.57)and EFV≥78.6 ml(AUC=0.62).Kaplan-Meier analysis revealed significantly lower MACE-free survival rates in patients with AVGT≥7.74 mm and EFV≥78.6 ml(both P<0.05).Cox regression analysis confirmed that AVGT(HR=2.18,95%CI:1.34-3.54)and EFV(HR=1.81,95%CI:1.11-2.96)were independent MACE risk factors(both P<0.05)in this patient cohort.RCS models demonstrated the significant linear associations between EFV/AVGT and MACE risk(bothoverall P<0.05).Conclusions:EFV and AVGT,the non-invasive imaging biomarkers quantifying and characterizing fat distribution,are independently correlated with elevated MACE risk in DCM patients.These metrics serve as potential prognostic indicators,enriching risk stratification indicators for early identification of high-risk patients and guiding personalized medication strategies.

Objective:To explore the value of the deep learning model based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI in preoperatively predicting vessels completely encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC).Methods:This study adopted a case-control design to retrospectively analyze 420 patients with HCC confirmed by postoperative pathology who underwent Gd-EOB-DTPA enhanced MRI between June 2016 and March 2023. A total of 420 patients were divided into a training set ( n=305) from the First Affiliated Hospital of Soochow University and an external validation set ( n=115) from Affiliated Nantong Hospital 3 of Nantong University. Based on postoperative pathological findings, patients were stratified into VETC-positive and VETC-negative groups. The training set comprised 161 VETC-positive cases and 144 VETC-negative cases, while the external validation set included 55 VETC-positive cases and 60 VETC-negative cases. Tumor regions of interest in arterial, portal venous, and hepatobiliary phases were manually delineated using ITK-SNAP software. Pre-trained Vgg19, Densenet121, and Vision Transformer (ViT) models were employed for transfer learning, extracting deep learning features from each image. Feature data were processed using FAE software, and 12 logistic regression models (arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase models) were constructed to select the optimal deep learning model. Independent predictors in clinical characteristics were identified through univariate and multivariate logistic analyses to establish a clinical model for predicting VETC pattern. Subsequently, a clinical-deep learning fusion model was developed by integrating these clinical predictors with the optimal deep learning features. Model performance in predicting VETC-positive HCC was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Results:In the external validation set, the area under the curve (AUC) of the Vgg19 model in the arterial phase, portal venous phase, hepatobiliary phase, and combined three-phase, respectively were 0.799,0.756,0.789,0.821, which were higher than those of Densenet121 (AUC: 0.544,0.581,0.544,0.583) and ViT (AUC: 0.740,0.752,0.785,0.767) model. The three-phase combined Vgg19 model achieved the highest AUC of 0.821 (95% CI 0.746-0.897). Multivariate logistic regression identified alpha-fetoprotein level ( OR=1.826,95% CI 1.069-3.120, P=0.028) and tumor diameter ( OR=1.329,95% CI 1.206-1.466, P<0.001) as independent predictors of VETC-positive HCC, forming the clinical model with an AUC of 0.789 (95% CI 0.703-0.859). The clinical-deep learning fusion model further achieved the AUC of 0.825 (95% CI 0.749-0.900). Calibration curves confirmed high concordance between predicted and actual probabilities for the three-phase Vgg19 model, while DCA revealed greater net clinical benefit for the combined Vgg19 and fusion models compared with the clinical model alone. Conclusions:The deep learning model based on Gd-EOB-DTPA enhanced MRI can be used to predict VETC of HCC preoperatively, among which the three-phase combined Vgg19 model and the clinical-deep learning model provide high predictive value.

OBJECTIVE: To explore the correlation between diabetic nephropathy (DN) and cognitive impairment through examining the cognitive function and the metabolism of the cerebrum in Type 2 diabetes mellitus patients at different stages of renal function.
 METHODS: Eighty six patients with Type 2 diabetes mellitus (T2DM) were enrolled for this study. According to the urinary albumin excretion rate (UAER), the patients were divided into a T2DM without DN group (DM group, n=33), an early DN group (DN-III group, n=26) and a clinical stage group (DN-IV group, n=27). Thirty healthy adults were selected as a control group (NC group). Biochemical indexes and UAER were measured, and glomerular filtration rate (GFR) was detected by single-photon emission computed tomography (SPECT). The cognitive function was measured by Montreal Cognitive Assessment (MoCA, Beijing version) and mini-mental state examination (MMSE). The peak areas of N-acetylasparte (NAA), creatine (Cr), choline-containing compounds (Cho) were detected by proton magnetic resonance spectroscopy (1H-MRS).
 RESULTS: 1) There was no statistical difference in MMSE scores between the DM group and the control group. The scores of MoCA in the DN-III group or in the DN-IV group were significant less than that in the NC group (F=3.66, P<0.05); 2) There was significant difference in left N-acetylaspartate (LNAA), left choline (LCho) among the diabetes groups. Compared with the DM group, the level of LNAA was decreased significantly (t=3.826, P<0.05) while the LCho was increased significantly (t=4.373, P<0.05) in the DN groups, with statistic difference between the 2 groups (t=3.693, P<0.05); 3) The MoCA scores of T2DM patients were negatively correlated with UAER (r=-0.285, P<0.05), while positively correlated with GFR (r=0.379, P<0.05); 4) Logistic regression analysis indicated that UAER and GFR were the major risky factors for diabetic cognitive impairment.
 CONCLUSION Diabetic cognitive impairment is closely correlated with the nephropathy in patients with Type 2 diabetes. With the decline in glomerular filtration function, the cognitive disorder tends to be aggravated. The hippocampal brain metabolism may have some changes in left side of Cho/Cr in patients with diabetic nephropathy.
Adult ; Aspartic Acid ; analogs & derivatives ; metabolism ; Case-Control Studies ; Cerebrum ; metabolism ; Choline ; metabolism ; Cognition ; Cognition Disorders ; epidemiology ; Creatine ; metabolism ; Diabetes Mellitus, Type 2 ; physiopathology ; Diabetic Nephropathies ; epidemiology ; Glomerular Filtration Rate ; Humans ; Neuropsychological Tests

Objective Hospital material management system of sterile supply has its particularity which is in close relation with medical quality.The study was to greatly improve the quality and efficiency of sterile supply department with the application of material management system. Methods The material management system integrated with the hospital net was applied in the man-agement of material distribution, inventory and statistics. Results It provided exact and detailed data for sterile supply department and clinical departments. Conclusion The application of information system in hospital net can provide exact and overall cost ac-counting information for involved departments, greatly improving the efficiency of material management.

OBJECTIVETo investigate whether the antioxidation and the regulation on the Extracellular Regulated Protein Kinases (ERK) signaling pathway are involved in the protective effects of blueberry on central nervous system.

METHODS30 Senescence-accelerated mice prone 8 (SAMP8) mice were divided into three groups and treated with normal diet, blueberry extracts (200 mg/kg•bw/day) and cyaniding-3-O-galactoside (Cy-3-GAL) (50 mg/kg•bw/day) from blueberry for 8 weeks. 10 SAMR1 mice were set as control group. The capacity of spatial memory was assessed by Passive avoidance task and Morris water maze. Histological analyses on hippocampus were completed. Malondialdehyde (MDA) levels, Superoxide Dismutase (SOD) activity and the expression of ERK were detected.

RESULTSBoth Cy-3-GAL and blueberry extracts were shown effective functions to relieve cellular injury, improve hippocampal neurons survival and inhibit the pyramidal cell layer damage. Cy-3-GAL and blueberry extracts also increased SOD activity and reduced MDA content in brain tissues and plasma, and increased hippocampal phosphorylated ERK (p-ERK) expression in SAMP8 mice. Further more, the passive avoidance task test showed that both the latency time and the number of errors were improved by Cy-3-GAL treatment, and the Morris Water Maze test showed significant decreases of latency were detected by Cy-3-GAL and blueberry extracts treatment on day 4.

CONCLUSIONBlueberry extracts may reverse the declines of cognitive and behavioral function in the ageing process through several pathways, including enhancing the capacity of antioxidation, altering stress signaling. Cy-3-GAL may be an important active ingredient for these biological effects.

Aging ; drug effects ; Animals ; Anthocyanins ; pharmacology ; Avoidance Learning ; Blueberry Plants ; chemistry ; Dietary Supplements ; Galactosides ; pharmacology ; Hippocampus ; drug effects ; metabolism ; Malondialdehyde ; metabolism ; Maze Learning ; Memory ; drug effects ; Mice ; Mitogen-Activated Protein Kinase 3 ; metabolism ; Phosphorylation ; Plant Extracts ; pharmacology ; Superoxide Dismutase ; metabolism