Clinical Outcomes of Cardiac Shockwave Therapy in Severe Coronary Artery Disease Patients after Coronary Artery Bypass Grafting.

Cardiac shockwave therapy (CSWT) is a noninvasive treatment for patients with refractory angina or myocardial ischemia. This study aims to evaluate the potential beneficial effect and safety of CSWT in patients with severe coronary artery disease (CAD) who have undergone coronary artery bypass grafting (CABG).This was a single-arm prospective cohort study. A total of 30 patients with severe CAD who were not suitable for coronary revascularization and who had undergone CABG were enrolled. All patients received CSWT for nine sessions. Evaluation was performed before and after CSWT, including the Canadian Cardiovascular Society (CCS) classification, New York Heart Association (NYHA) classification, 6-minute walk test (6MWT), Seattle Angina Questionnaire (SAQ) score, nitroglycerin dosage, echocardiography, myocardial perfusion imaging (MPI), and safety parameters. All patients were followed up at both 1 month and 9 months after CSWT.After treatment, CSWT significantly improved CCS classification (P < 0.05), NYHA classification (P < 0.05), nitroglycerin dosage (P < 0.001), and 6MWT (P < 0.05) at 1 month and 9 months after CSWT. SAQ score (P < 0.05) and left ventricular ejection fraction (LVEF; P = 0.037) by echocardiography significantly improved at 1 month after CSWT. Significant decreases in summed stress score (SSS), summed difference score (SDS), ischemic area stress, and ischemic area difference by MPI were observed at 1 month and 9 months after CSWT (P < 0.01). There were no changes in safety parameters before and after CSWT.CSWT may have a beneficial effect on improving myocardial perfusion, clinical symptoms, exertional capacity, and quality of life and is a safe alternative treatment for patients with severe CAD who have undergone CABG.

A deep learning method for predicting molecular properties and compound-protein interactions.

Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep learning have demonstrated excellent performance in various challenges. It is imperative to develop efficient computational methods to predict accurately both molecular properties and CPIs in drug research using deep learning techniques. In this paper, we propose a deep learning method applicable to both molecular property prediction and CPI prediction based on the idea that both are generally influenced by chemical structure and sequence information of compounds and proteins. Molecular properties are inferred by integrating the molecular structure and sequence information of compounds, and CPIs are predicted by integrating protein sequence and compound structure. The method combines topological structure and sequence fingerprint information of molecules, extracts adequately raw data features, and generates highly representative features for prediction. Molecular property prediction experiments were conducted on BACE, P53 and hERG datasets, and CPI prediction experiments were conducted on Human, C. elegans and KIBA datasets. MG-S achieves outperformance in molecular property prediction on P53, the differences in AUC, Precision and MCC are 0.030, 0.050 and 0.100, respectively, over the suboptimal baseline model, and provides consistently good results on BACE and hERG.The model also achieves impressive performance in CPI prediction, the differences in AUC, Precision and MCC on KIBA are 0.141, 0.138, 0.090 and 0.082, respectively, compared with the state-of-the-art models. The comprehensive results show that the MG-S model has higher performance, better classification ability, and faster convergence. MG-S will serve as a useful method to predict compound properties and CPIs in the early stages of drug design and discovery.Our code and datasets are available at: https://github.com/happay-ending/cpi_cpp.

Two-Dimensional Speckle Tracking Echocardiography Identifies Coronary Artery Disease in 690 Patients: A Retrospective Study from a Single Center.

BACKGROUND Two-dimensional speckle tracking echocardiography (2D-STE) is a novel and non-invasive technique for the diagnosis of coronary artery disease (CAD). This retrospective study from a single center aimed to identify myocardial ischemia using 2D-STE in CAD patients identified by angiography. MATERIAL AND METHODS From March 1 to November 30, 2019, 690 patients in Beijing Hospital were enrolled. After angiography, 346 patients were diagnosed with CAD. Reduction in vessel diameter of ≥50% by stenosis in at least 1 major coronary artery or its main branch was considered CAD. Analysis of 2D-STE was performed using EchoPAC version 201. RESULTS The global strain was significantly impaired in CAD patients (P<0.01). Global longitudinal peak strain (GLPS) was analyzed in layers. For GLPS of the epicardium, the odds ratio (OR) was 1.297 (1.217-1.382; P=0.002), the area under the curve (AUC) was 0.727, and the cut-off value was -16.95; sensitivity and specificity were 73.7% and 63.0%, respectively. For GLPS of the middle layer, the OR was 1.260 (1.192-1.333; P<0.001), the AUC was 0.732, and the cut-off value was -20.95; sensitivity and specificity were 82.4% and 56.2%, respectively. For GLPS of the endocardium, the OR was 1.193 (1.137-1.251; P<0.001), the AUC was 0.708, and the cut-off value was -22.95; sensitivity and specificity were 82.9% and 52.9%, respectively. CONCLUSIONS The findings from this study support the clinical application of 2D-STE in patient populations with suspected myocardial ischemia due to CAD. Therefore, 2D-STE combined with ECG monitoring may have a future role for early screening of CAD patients.

Tumor-associated macrophage interleukin-ß promotes glycerol-3-phosphate dehydrogenase activation, glycolysis and tumorigenesis in glioma cells.

Tumor-immune crosstalk within the tumor microenvironment (TME) occurs at all stages of tumorigenesis. Tumor-associated M2 macrophages play a central role in tumor development, but the molecular underpinnings have not been fully elucidated. We demonstrated that M2 macrophages produce interleukin 1ß (IL-1ß), which activates phosphorylation of the glycolytic enzyme glycerol-3-phosphate dehydrogenase (GPD2) at threonine 10 (GPD2 pT10) through phosphatidylinositol-3-kinase-mediated activation of protein kinase-delta (PKCδ) in glioma cells. GPD2 pT10 enhanced its substrate affinity and increased the catalytic rate of glycolysis in glioma cells. Inhibiting PKCδ or GPD2 pT10 in glioma cells or blocking IL-1ß generated by macrophages attenuated the glycolytic rate and proliferation of glioma cells. Furthermore, human glioblastoma tumor GPD2 pT10 levels were positively correlated with tumor p-PKCδ and IL-1ß levels as well as intratumoral macrophage recruitment, tumor grade and human glioblastoma patient survival. These results reveal a novel tumorigenic role for M2 macrophages in the TME. In addition, these findings suggest possible treatment strategies for glioma patients through blockade of cytokine crosstalk between M2 macrophages and glioma cells.

Is the Chronic Kidney Disease Epidemiology Collaboration four-level race equation better than the cystatin C equation?

AIM: To evaluate the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) four-level race equation in the assessment of glomerular filtration rate (GFR) in Chinese people with chronic kidney disease (CKD), which was published in 2011, compared with the cystatin C-based GFR estimation equation (CysC GFR) and the combination of CysC and serum creatinine equation (CysC-Scr GFR). METHODS: The CKD-EPI four-level race equation estimated GFR (CKD-EPI GFR) was compared with the CysC GFR and CysC-Scr GFR. Three equations were compared with body surface area (BSA) standardized GFR (sGFR), which was measured by (99m) Tc-DTPA renal dynamic imaging method in 111 CKD cases. RESULTS: A statistically significant correlation was found between sGFR and CKD-EPI GFR, CysC GFR and CysC-Scr GFR. Three estimated GFR (eGFR) equations of 30% accuracy were 58.6%, 56.8% and 63.5%, respectively. Average deviations of eGFR from sGFR were 2.34, 1.19, and 1.32 (mL/min per 1.73 m(2)) (P > 0.05), respectively. There was no significant deviation in the CKD from stages 1 to 5 in CKD-EPI GFR and CysC-Scr GFR. However, when estimated by CysC GFR, the deviation was increased, with the value of 12.41 mL/min per 1.73 m(2) (P= 0.002) in CKD stage 5. CONCLUSION: Our results showed that in a Chinese population with CKD, CKD-EPI GFR, CysC GFR and CysC-Scr GFR of bias and overall accuracy of 30% were very similar. There was little advantage in adding Asian coefficient to modifying the CKD-EPI equation. CysC GFR overestimated GFR in patients with CKD stages 4 and 5.

[Quantitative relationship between gas chromatographic retention time and structural parameters of alkylphenols].

Alkylphenols are a group of permanent pollutants in the environment and could adversely disturb the human endocrine system. It is therefore important to effectively separate and measure the alkylphenols. To guide the chromatographic analysis of these compounds in practice, the development of quantitative relationship between the molecular structure and the retention time of alkylphenols becomes necessary. In this study, topological, constitutional, geometrical, electrostatic and quantum-chemical descriptors of 44 alkylphenols were calculated using a software, CODESSA, and these descriptors were pre-selected using the heuristic method. As a result, three-descriptor linear model (LM) was developed to describe the relationship between the molecular structure and the retention time of alkylphenols. Meanwhile, the non-linear regression model was also developed based on support vector machine (SVM) using the same three descriptors. The correlation coefficient (R(2)) for the LM and SVM was 0.98 and 0. 92, and the corresponding root-mean-square error was 0. 99 and 2. 77, respectively. By comparing the stability and prediction ability of the two models, it was found that the linear model was a better method for describing the quantitative relationship between the retention time of alkylphenols and the molecular structure. The results obtained suggested that the linear model could be applied for the chromatographic analysis of alkylphenols with known molecular structural parameters.

Classification of the carcinogenicity of N-nitroso compounds based on support vector machines and linear discriminant analysis.

The support vector machine (SVM), as a novel type of learning machine, was used to develop a classification model of carcinogenic properties of 148 N-nitroso compounds. The seven descriptors calculated solely from the molecular structures of compounds selected by forward stepwise linear discriminant analysis (LDA) were used as inputs of the SVM model. The obtained results confirmed the discriminative capacity of the calculated descriptors. The result of SVM (total accuracy of 95.2%) is better than that of LDA (total accuracy of 89.8%).

Prediction of gas chromatographic retention indices by the use of radial basis function neural networks.

A new method for the prediction of retention indices for a diverse set of compounds from their physicochemical parameters has been proposed. The two used input parameters for representing molecular properties are boiling point and molar volume. Models relating relationships between physicochemical parameters and retention indices of compounds are constructed by means of radial basis function neural networks. To get the best prediction results, some strategies are also employed to optimize the topology and learning parameters of the RBFNNs. For the test set, a predictive correlation coefficient R=0.9910 and root mean squared error of 14.1 are obtained. Results show that radial basis function networks can give satisfactory prediction ability and its optimization is less-time consuming and easy to implement.

The prediction for gas chromatographic retention indices of saturated esters on stationary phases of different polarity.

A quantitative structure-retention relationship (QSRR) model has been developed for the gas chromatographic Kováts indices of 98 saturated esters on seven different polar stationary phases by multiple linear regression analysis (MLR). The seven stationary phases are: SE-30, OV-7, DC-710, OV-25, 100% phenyl, DC-230 and DC-530. Chemical descriptors were calculated from the molecular structures by PM3 of Hyperchem 4.0. Principal component analysis (PCA) was applied to extract the data structure. Multiple linear regression was made in forward stepwise manner to select suitable variables in the model. The proposed model had a high multiple square correlation coefficient R(2) and low standard error S.E. The result proved the strong predictive power of the model.