Search

Objective To explore potential mechanisms involved in the reduction of podocyte apoptosis and oxidative stress induced by high glucose (HG) by tanshinone ⅡA (Tan ⅡA). Methods CCK-8 method was used to screen the appropriate interference concentration of Tan ⅡA. The MPC-5 cells were divided into NC group (5.5 mmol/L D-glucose), MA group(5.5 mmol/L D-glucose and 22.5 mmol/L mannitol), HG group(25 mmol/L D-glucose), HG+Tan ⅡA group(25 mmol/L D-glucose and 10 μmol/L Tan ⅡA), and HG+Tan ⅡA+SIRT1 inhibitors(sirtinol) group (25 mmol/L D-glucose, 10 μmol/L Tan ⅡA and 20 μmol/L sirtinol). Determina- tion of oxidative stress markers malondialdehyde(MDA), superoxide dismutase (SOD) and catalase (CAT) were made by microplate method, WST-1 method and visible light method. Flow cytometry was used to measure cell apoptosis. Western blot was used to evaluate the expression of apoptosis-related proteins and silent information regulator 1 (SIRT1)/endothelial nitric oxide synthase (eNOS) pathway proteins. Results Tan ⅡA improved the viability of MPC-5 cells induced by HG (P＜0.05). Compared with the MA group, the level of cell apoptosis in the HG group increased, the expression of B-cell lymphoma-2 gene (Bcl-2), phosphorylated SIRT1 (p-SIRT1) and phosphorylated eNOS(p-eNOS) in the cells, and the activity of SOD and of CAT were reduced, the expression of Bcl-2-associated X protein (Bax) and cleaved caspase-3, and the content of MDA in cells were increased(P＜0.05). Compared with the HG group, the level of cell apoptosis in the Tan ⅡA group reduced, the expressions of Bcl-2, p-SIRT1 and p-eNOS in the cells, and the activity of SOD and CAT were increased, the expression of Bax and cleaved caspase-3, and the content of MDA in cells were reduced(P＜0.05). The SIRT1 inhibitor sirtinol attenuated the inhibitory effect of Tan ⅡA on HG-induced podocyte apoptosis and oxidative stress (P＜0.05). Conclusions Tan ⅡA may reduce HG-induced apoptosis and oxidative stress in podocytes with a potential mechanism of activating the SIRT1/eNOS pathway.

Objective To analyze the risk factors for heart failure in hospitalized patients with coronary heart disease (CHD) after percutaneous coronary intervention (PCI) and establish a personalized risk prediction model for heart failure after PCI. Methods The clinical data of 640 patients with CHD who were hospitalized and underwent PCI in our department from January 2017 to December 2018 were collected and retrospectively analyzed. The subjects were divided into heart failure group (n=44) and non-heart failure group (n=596) according to the occurrence of postoperative heart failure. Logistic multivariate regression analysis and R software were used respectively to screen the independent predictors and plot the nomogram prediction model of heart failure risk. Then, consistency index (C-index), area under the receiver operating characteristic (ROC) curve and calibration curve were subsequently adopted to evaluate the prediction efficiency of the model. Results Multivariate analysis showed that left ventricular ejection fraction (LVEF), age, contrast agent dosage, concomitant cerebral infarction, and grade of New York Heart Association (NYHA) functional classification were independent predictors of heart failure after PCI (P < 0.05). The C-index of the nomogram for predicting heart failure risk was 0.796 (95%CI: 0.723~0.869), and the area under the ROC curve was 0.797 after internal validation (95%CI: 0.726~0.861). Hosmer-Lemeshow test and calibration curves indicated that the nomogram had good prediction consistency (Chi-square=12.360, P=0.135). Conclusion The nomogram model for predicting heart failure risk is successfully constructed, which can assist the medical staff to screen out the population with high risk of heart failure after PCI, and then to implement targeted intervention measures.

Objective To develop a nomogram prediction model based on residual inflammation risk (RIR) and evaluate its value in the prediction for the risk of major adverse cardiovascular events (MACE) in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI). Methods Clinical data of 297 AMI patients who underwent PCI in our department from June 2017 to March 2019 were collected and retrospectively analyzed. According to their levels of preoperative hypersensitive-CRP (hs-CRP) and low-density lipoprotein cholesterol (LDL-C), they were divided into RIR group (n=28) and non RIR group (n=269). They also were assigned into the MACE group (n=102) and non-MACE group (n=195) according to whether MACEs occurred during hospitalization. Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate logistic regression analysis were used to evaluate the correlation between the risk of RIR and the occurrence of MACE, and other independent risk predictors of MACE were screened out. The nomogram model based on the indicators of RIR was constructed and verified by the R language software. Results The incidences of pump failure, cardiogenic shock, malignant arrhythmia and death, and overall MACE were significantly higher in the RIR group than the non-RIR group (P < 0.05). The Results of LASSO regression and multivariate regression analyses showed that left ventricular ejection fraction (LVEF) and hemoglobin concentration were negatively correlated with the risk of MACE in hospital (P < 0.05), while residual inflammation risk, hemoglobinA1c (Hb1Ac), leukocyte count and N-terminal pro-B type natriuretic peptide (NT-proBNP) level were positively correlated with the risk of MACE (P < 0.05). The ROC curve indicated that RIR was not good in prediction of MACE (AUC=0.592, 95%CI: 0.551~0.634). Based on the above 6 indicators, the nomogram model was constructed. The Harrel's C-index of the nomogram was 0.872 (95%CI: 0.827~0.917), the AUC of the nomogram after re-sampling 1 000 times was 0.866 (95% CI: 0.818~0.907), and the Hosmer lemeshow deviation test showed that the prediction probability of nomogram was consistent with the actual frequency (Chi-square=8.420, P=0.393). The clinical decision curve showed that when the threshold probability of MACE occurrence is between 0.08 and 0.88, and the nomogram model could obtain the highest net benefit, which indicated that the model had good clinical applicability. Conclusion The RIR-based nomogram model and other 5 factors have good prediction efficiency and clinical applicability in the prediction of the risk of MACE in AMI patients.

Objective: Artificial hip arthroplasty (AHA) is widely accepted in elderly patients with femoral neck fractures, but it is associated with high risk of death and various postoperative complications due to old age and accompanying chronic diseases. Therefore, this study aimed to explore the risk factors for death in elderly patients with femoral neck fractures after AHA and to establish a nomogram risk prediction model, which is expected to reveal high‐risk patients and improve the postoperative quality of life and survival rate of patients. Methods: Elderly patients who underwent AHA for femoral neck fractures in our hospital from September 2014 to May 2021were retrospectively analyzed. These patients were divided into a survival group and a death group according to their clinical outcomes. The following clinical data were recorded for the patients in the two groups: sex, age, underlying diseases, smoking and drinking history, preoperative nutritional risk score (NRS) and American Society of Anesthesiologists (ASA) score, as well as relevant indicators about the operation. These data were subject to univariate analysis and then logistic analysis to determine the risk factors of death. Subsequently, a nomogram risk prediction model was established and further validated with the receiver operating characteristic curve (ROC) and the Hosmer–Lemeshow test. Finally, the effects of predictive risk factors were analyzed using the Kaplan–Meier survival curve. Results: Follow‐up was completed by 260 patients, including 206 patients in the survival group and 54 patients in the death group; the overall death rate was 20.77%, and the follow‐up time, age, postoperative 1, 3 and 5‐year death rates were 3.47 ± 1.93 years, 75.32 ± 9.12 years, 5.77%, 12.51%, and 25.61%, respectively. The top three causes of death in 54 patients were respiratory disease, cerebrocardiovascular disease, and digestive disease, respectively. The logistic analysis indicated that elderly patients with femoral neck fractures, the risk factors for death after AHA were age ≥ 80 years, preoperative NRS ≥ 4, HB ≤ 90 g/L, CR ≥ 110 umol/L, and ASA score ≥ 3, as well as postoperative albumin ≤ 35 g/L, the nomogram was established, and then its predictive performance was successfully validated using the ROC curve (AUC = 0.814, 95% confidence interval = 0.749–0.879) and the Hosmer–Lemeshow test (p = 0.840). Furthermore, Kaplan–Meier survival curve analysis revealed that the abovementioned six indicators were correlated with the post‐AHA survival time of elderly patients with femoral neck fractures (pLog Rank < 0.05). Conclusion: Old age, preoperatively high NRS and ASA score, anemia, poor renal function, and postoperative hypoproteinemia are the major risk factors for death in elderly patients with femoral neck fractures after AHA; they are also associated with postoperative survival. Early identification and effective interventions for optimization of modifiable risk factors are recommended to improve the postoperative quality of life and survival rates. [ABSTRACT FROM AUTHOR]

With the development of the Internet, the types and quantities of malware have grown rapidly, and how to identify unknown malware is becoming a new challenge. The traditional malware detection method based on fixed features is becoming more and more difficult. In order to improve detection accuracy and efficiency for mobile terminals, this paper proposed a malware detection method for mobile terminals based on application programming interface (API) call sequence, which was characterized by the API call sequence and used a series of feature preprocessing techniques to remove redundant processing of the API call sequence. Finally, the recurrent neural network method (RNN) was used to build the model and perform detection and verification. Furthermore, this paper constructed a malware detection model based on a two-way recurrent neural network and used the two-way long short-term memory network model (LSTM) to train the data set containing 5986 malware samples and 5065 benign software samples to obtain the final detection model and its parameters. Finally, the feature vector of the APK file to be detected was passed into the model and obtained the detection results. The experimental results indicated that the detection accuracy of this method can reach 93.68%. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) offers rich spectral and spatial data, beneficial for a variety of applications. However, challenges persist in HSI classification due to spectral variability, non-linearity, limited samples, and a dearth of spatial information in conventional spectral classifiers. While various spectral–spatial classifiers and dimension reduction techniques have been developed to mitigate these issues, they are often constrained by the utilization of handcrafted features. Deep learning has been introduced to HSI classification, with pixel- and patch-level deep learning (DL) classifiers gaining substantial attention. Yet, existing patch-level DL classifiers encounter difficulties in concentrating on long-distance dependencies and managing category areas of diverse sizes. The proposed Self-Adaptive 3D atrous spatial pyramid pooling (ASPP) Multi-Scale Feature Fusion Network (SAAFN) addresses these challenges by simultaneously preserving high-resolution spatial detail data and high-level semantic information. This method integrates a modified hyperspectral superpixel segmentation technique, a multi-scale 3D ASPP convolution block, and an end-to-end framework to extract and fuse multi-scale features at a self-adaptive rate for HSI classification. This method significantly enhances the classification accuracy of HSI with limited samples. [ABSTRACT FROM AUTHOR]

Environmental stress cracking(ESC) behavior of silicone coated polycarbonate (PC) in ethanol was studied. Stress relaxation of PC and PC/silicone coating under a combined action of ethanol and stress was measured by self-made three point bending equipment. After stress relaxation testing, crack morphology was observed by polarizing microscope. The results indicate that silicone coating is able to improve the stress cracking resistance of PC parts in ethanol. The coated PC shows slower stress relaxation rate and less number of cracks than pristine PC. It is mainly attributed to that the silicone coating can provide barrier effect to the absorption and diffusion of ethanol in PC substrate. Furthermore, the mechanical properties of flexible silicone coating matched fairly well with that of PC, so that the coating is uneasy to peel off from PC substrate during the ESC testing. The silicone coating has a favorable effect to protect PC substrate from ESC under the combined action of solvent and stress.