Flash pulmonary edema in the cardiac catheterization laboratory: a case report

Flash pulmonary edema is a potentially fatal condition that can suddenly deteriorate a patient's status in a variety of settings, including the catheterization laboratory. We describe a 51-year-old woman with a history of hypertension who was admitted for a second valve operation for degenerated aortic bioprosthesis. Before undergoing coronary angiography, she looked a little worried, she experienced respiratory distress and a significant increase in blood pressure in favor of acute flash pulmonary edema, which was immediately and successfully managed by respiratory support and administration of high-dose intravenous nitroglycerine and loop diuretic therapy. The present scenario highlights the significance of being aware of the warning signs of acute flash pulmonary edema to make a prompt diagnosis and initiate the appropriate treatment to prevent catastrophic consequences

Geochemistry and zeolitization of tuffs in Zarrin Dasht mining area (Firuzkuh, Central Alborz)

The Zarrin Dasht region is located in Tehran province, between Damavand and Firuzkuh cities. This region with 10 Km2 area belongs to Central-Alborz zone. On the base of petrographic and geochemical studies, the volcaniclastic rocks of the Zarrin Dasht area are trachyte, dacite, rhyodacite and rhyolite. Petrographic evidence as well as XRD analyses point to the presence of glass and crystallization quartz, clinoptilolite, analcime, natrolite, heulandite, montmorillonite, kaolinite, illite and chlorite. Texture of tuffs is vitrophyric. Based on the geochemical data, these rocks are calk-alkaline and metaluminous to peraluminous composition. Primitive mantle-normalized and chondrite-normalized trace elements and rare earth elements patterns indicate enrichment in LREE and LILE and depletion in HREE and HFSE with pronounced negative anomalies in Eu, Ba, Nb, Ti, Sr and P in the Zarrin Dasht samples. Samples position on the various tectonic setting discrimination diagrams demonstrate that these rocks were formed in environment related to subduction in active continental margins. Chlorites, are present in the rock context and also relatively filled cavities as amygdaloidal, while analcime was mainly distributed in the rock context. It seems these minerals are the result of recrystallization of volcanic glasses such as shard in the rock. Secondary minerals can be considered as a burial diagenesis and low-grade metamorphism in the studied tuffs that occurred under the upper floor pressure

Validity and Diagnostic Performance of Computing Fractional Flow Reserve From 2-Dimensional Coronary Angiography Images

BACKGROUND: Measurement of fractional flow reserve (FFR) is the gold standard for determining the physiologic significance of coronary artery stenosis, but newer software programs can calculate the FFR from 2-dimensional angiography images. METHODS: A retrospective analysis was conducted using the records of patients with intermediate coronary stenoses who had undergone adenosine FFR (aFFR). To calculate the computed FFR, a software program used simulated coronary blood flow using computational geometry constructed using at least 2 patient-specific angiographic images. Two cardiologists reviewed the angiograms and determined the computational FFR independently. Intraobserver variability was measured using κ analysis and the intraclass correlation coefficient. The correlation coefficient and Bland-Altman plots were used to assess the agreement between the calculated FFR and the aFFR. RESULTS: A total of 146 patients were included, with 95 men and 51 women, with a mean (SD) age of 61.1 (9.5) y. The mean (SD) aFFR was 0.847 (0.072), and 41 patients (27.0%) had an aFFR of 0.80 or less. There was a strong intraobserver correlation between the computational FFRs (r = 0.808; P \u3c .001; κ = 0.806; P \u3c .001). There was also a strong correlation between aFFR and computational FFR (r = 0.820; P \u3c .001) and good agreement on the Bland-Altman plot. The computational FFR had a high sensitivity (95.1%) and specificity (90.1%) for detecting an aFFR of 0.80 or less. CONCLUSION: A novel software program provides a feasible method of calculating FFR from coronary angiography images without resorting to pharmacologically induced hyperemia

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases

Complexities of information sources

Calculating the entropy for complex systems is a significant problem in science and engineering problems. However, this calculation is usually computationally expensive when the entropy is computed directly. This paper introduces three classes of information sources that for all members of each class, the entropy value is the same. These classes are characterized according to special dynamics created by three kinds of self-mappings on Ω, and A, where Ω is a probability space and A is a finite set. An approximation of rank variables of the product of information sources is made, and it is proved that the topological entropy of the product of two information sources is equal to the summation of their topological entropies.</p

Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine

Different variables affect the performance of the Stirling engine and are considered in optimization and designing activities. Among these factors, torque and power have the greatest effect on the robustness of the Stirling engine, so they need to be determined with low uncertainty and high precision. In this article, the distribution of torque and power are determined using experimental data. Specifically, a novel polynomial approach is proposed to specify torque and power, on the basis of previous experimental work. This research addresses the question of whether GMDH (group method of data handling)-type neural networks can be utilized to predict the torque and power based on determined parameters

Stochastic Comparisons of Largest-Order Statistics and Ranges from Marshall–Olkin Bivariate Exponential and Independent Exponential Variables

Sample range and the associated functions such as survival function and mean residual life function have found many important applications in the reliability field. In this work, we establish some results that are in two different directions. In the first part, we establish some conditions for comparing the largest-order statistics (in the sense of mean residual life order) arising from bivariate Marshall–Olkin exponential distribution. Then, in the second part, we present some sufficient conditions for comparing sample ranges (in the sense of usual stochastic order and reversed hazard rate order) arising from independent exponential random variables

A novel framework based on the multi-label classification for dynamic selection of classifiers

Multi-classifier systems (MCSs) are some kind of predictive models that classify instances by combining the output of an ensemble of classifiers given in a pool. With the aim of enhancing the performance of MCSs, dynamic selection (DS) techniques have been introduced and applied to MCSs. Dealing with each test sample classification, DS methods seek to perform the task of classifier selection so that only the most competent classifiers are selected. The principal subject regarding DS techniques is how the competence of classifiers corresponding to every new test sample classification task can be estimated. In traditional dynamic selection methods, for classifying an unknown test sample x, first, a local region of data that is similar to x is detected. Then, those classifiers that efficiently classify the data in the local region are also selected so as to perform the classification task for x. Therefore, the main effort of these methods is focused on one of the two following tasks: (i) to provide a measure for identifying a local region, or (ii) to provide a criterion for measuring the efficiency of classifiers in the local region (competence measure). This paper proposes a new version of dynamic selection techniques that does not follow the aforementioned approach. Our proposed method uses a multi-label classifier in the training phase to determine the appropriate set of classifiers directly (without applying any criterion such as a competence measure). In the generalization phase, the suggested method is employed efficiently so as to predict the appropriate set of classifiers for classifying the test sample x. It is remarkable that the suggested multi-label-based framework is the first method that uses multi-label classification concepts for dynamic classifier selection. Unlike the existing meta-learning methods for dynamic ensemble selection in the literature, our proposed method is very simple to implement and does not need meta-features. As the experimental results indicate, the suggested technique produces a good performance in terms of both classification accuracy and simplicity which is fairly comparable with that of the benchmark DS techniques. The results of conducting the Quade non-parametric statistical test corroborate the clear dominance of the proposed method over the other benchmark methods.</p

Optimal design of an Otto cycle based on thermal criteria

In recent years, numerous analyses have been performed on Otto cycles and Otto engines, but these have often yielded different output powers and engine thermal efficiencies. In the present study, output power and engine thermal efficiency are optimized and entropy generation is minimized using a NSGA algorithm and thermodynamic analysis. The Pareto optimal frontier is obtained and a final optimal solution is selected using various decision-making approaches, including fuzzy Bellman-Zadeh, LINMAP and TOPSIS methods. The results enhance understanding of the performances of Otto cycles and of their optimization