Aref Salehi

Background: Coronary artery disease (CAD) is recognized as an inflammatory condition and remains a leading cause of morbidity and mortality worldwide. Cardiovascular disease (CVD), more broadly, is a major contributor to global death and disability. This study aimed to compare the diagnostic performance of various noninvasive techniques for detecting CAD. Methods: A cross‐sectional study was conducted involving 758 participants, including 508 patients diagnosed with CAD and 250 without the disease. The diagnostic performance of two machine learning models—artificial neural networks (ANNs) and support vector machines (SVMs)—was evaluated. The classification models were assessed using receiver operating characteristic (ROC) curves, sensitivity, specificity, and overall accuracy. Results: The study included a total of 758 participants. Among them, 250 individuals (33.6% male and 66.4% female) were diagnosed as non‐CAD cases, while 508 participants (64% male, 36% female) were identified as having CAD. The area under the ROC curve (AUC) for CAD prediction was 0.752 (95% CI: 0.682–0.823) using the ANN model and 0.793 (95% CI: 0.733–0.853) using the SVM model. A statistically significant difference was observed between the performance of the two models in predicting CAD ( p = 0.03), with the SVM model demonstrating superior predictive performance (AUC = 0.793, 95% CI: 0.733–0.853). Conclusions: SVMs demonstrated superior performance compared to ANNs in predicting the risk of CAD using simple clinical predictors.

Purpose: Coronary artery disease (CAD) is one of the most significant cardiovascular diseases that requires accurate angiography to diagnose. Angiography is an invasive approach involving risks like death, heart attack, and stroke. An appropriate alternative for diagnosis of the disease is to use statistical or data mining methods. The purpose of the study was to predict CAD by using discriminant analysis and compared with the logistic regression.Materials and methods: This cross-sectional study included 758 cases admitted to Fatemeh Zahra Teaching Hospital (Sari, Iran) for examination and coronary angiography for evaluation of CAD in 2019. A logistics discriminant, Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) model and K-Nearest Neighbor (KNN) were fitted for prognosis of CAD with the help of clinical and laboratory information of patients.Results: Out of the 758 examined cases, 250 (32.98%) cases were non-CAD and 508 (67.22%) were diagnosed with CAD disease. The results indicated that the indices of accuracy, sensitivity, specificity and area under the ROC curve (AUC) in the linear discriminant analysis (LDA) were 78.6, 81.3, 71.3, and 81.9%, respectively. The results obtained by the quadratic discriminant analysis were respectively 64.6, 88.2, 47.9, and 81%. The values of the metrics in K-nearest neighbor method were 74, 77.5, 63.7, and 82%, respectively. Finally, the logistic regression reached 77, 87.6, 55.6, and 82%, respectively for the evaluation metrics.Conclusions: The LDA method is superior to the Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN) and Logistic Regression (LR) methods in differentiating CAD patients. Therefore, in addition to common non-invasive diagnostic methods, LDA technique is recommended as a predictive model with acceptable accuracy, sensitivity, and specificity for the diagnosis of CAD. However, given that the differences between the models are small, it is recommended to use each model to predict CAD disease.

Compositional simulation of oil reservoirs is necessary for accurate representation of the physics associated with near-miscible gas injection processes. Performing the simulations using the fine-scale geocellular model is computationally expensive; as a result, reliable upscaling methods for compositional flow are needed. Compared with black-oil models, the interactions between the thermodynamic phase behavior and the sub-grid heterogeneities that are associated with compositional displacements pose significant additional challenges to upscaling. We introduce a new framework to upscale multi-component, multi-phase compositional displacements with special attention to accurate representation of the fine-scale phase behavior on the coarse grid. We use a mass-conservative formulation and introduce an upscaled molar mobility for each phase. These upscaled flow functions account for the sub-scale absolute and relative permeability variations, as well as, compressibility effects. They also correct -somewhat- for numerical dispersion effects at the coarse-grid level. The upscaling of the thermodynamic phase behavior is performed as follows. We assume that instantaneous thermodynamical equilibrium is valid at the fine-scale, and we derive coarse-scale equations, in which the thermodynamic phase behavior is not necessarily at equilibrium. Deviation from local equilibrium may be due to different bypassing mechanisms, such as fingering and channelling. As a result, the fugacity of a component in the two phases may not be equal at the coarse scale, and this deviation is quantified by the coarse-scale thermodynamic functions. We demonstrate that these upscaled functions can be interpreted as a transformation of the equilibrium phase space on the fine scale to a modified region of similar shape, but with tilted tie-lines. We then describe how to convert non-equilibrium coarse-scale behavior into the widely used transport coefficients (alpha-factors). The proposed methodology is applied to various challenging gas injection problems. We compare our upscaling method with standard upcaling techniques for compositional simulation, and we show improvements, both in terms of accuracy and computational efficiency, of the new approach.

The Comparison of Acute Myocardial Infarction Serum Anticardiolipin Antibody with Healthy Subjects, in Gorgan (Northern Iran)

Treatment of supraventricular arrhythmia includes a wide range of medical interventions. Herbal remedies are suitable alternatives to synthetic drugs due to their availability, minimal side effects and lower price. Pharmacological studies and traditional medical literature point to the cardiovascular effects of Citrus aurantium L. (Rutaceae) in many instances. In the present study we used isolated perfused AV-node of rabbit as an experimental model to determine the effect of various concentrations of essential oil of C. aurantium (0.1-0.3 v/v) on the nodal conduction time and refractoriness of an isolated rabbit AV-nodal preparations. Selective stimulation protocols were used to independently quantify AV nodal recovery, facilitation and fatigue in 18 rabbits. Our results showed concentration-dependent and rate-independent suppressive effects of essence of C. aurantium on the Wenchebach cycle length (WBCL), AV Conduction Time (AVCT) and effective and functional refractory periods (ERP & FRP). Functional properties such as facilitation and fatigue were significantly increased by this plant. Citrus aurantium plays a protective role against the toxic effects of ouabaine by increasing AV nodal conduction time and refractoriness. The above results indicated differential effects of C. aurantium on slow and fast pathways, with a dominant role on fast pathways. This research has explained the protective role of C. aurantium on ouabaine toxicity. All results indicated the potential anti-arrhythmic effects of C. aurantium in treating supraventricular tachyarrhythmia.

Medication dosing errors are frequent in neonatal wards. In an Iranian neonatal ward, a 7.5 months study was designed in three periods to compare the effect of Computerized Physician Order Entry (CPOE) without and with decision support functionalities in reducing non-intercepted medication dosing errors in antibiotics and anticonvulsants. Before intervention (Period 1), error rate was 53%, which did not significantly change after the implementation of CPOE without decision support (Period 2). However, errors were significantly reduced to 34% after that the decision support was added to the CPOE (Period 3; P < 0.001). Dose errors were more often intercepted than frequency errors. Over-dose was the most frequent type of medication errors and curtailed-interval was the least. Transcription errors did not reduce after the CPOE implementation. Physicians ignored alerts when they could not understand why they appeared. A suggestion is to add explanations about these reasons to increase physicians’ compliance with the system’s recommendations.