Speckle tracking echocardiography in hypokinetic non-dilated cardiomyopathy: comparison with dilated cardiomyopathy

Aims: Hypokinetic non-dilated cardiomyopathy (HNDC), which is determined by impaired left ventricular (LV) systolic function despite normal LV size, has been categorized as a subgroup of dilated cardiomyopathy (DCM) spectrum. Lack of data regarding advanced echocardiographic data in this population motivated us to design the present study in order to assess LV myocardial deformation properties of HNDC patients against the ones with dilated left ventricle. Methods and results: Thirty-one HNDC patients and 23 DCM patients were enrolled in the study consecutively. Myocardial deformation parameters including global longitudinal strain, global circumferential strain, LV basal and apical rotation, LV twist, and LV mechanical dispersion were obtained with the use of two-dimensional speckle tracking-based methods in all patients. Left cardiac chamber volume was also measured using three-dimensional HeartModel application. Patients with enlarged left ventricle tend to have lower LV ejection fraction. Comparing with HNDC group, DCM patients showed worse global circumferential strain (coefficient ± standard error 3.59 ± 0.94, P < 0.001) and LV mechanical dispersion (coefficient ± standard error 16.46 ± 7.09, P = 0.02) after regression analysis, while neither the global longitudinal strain nor the LV twist was not significantly different between two study population. Conclusions: Left ventricular enlargement has a substantial effect on the circumferential strain and mechanical dispersion more than other deformation parameters that may play a role in the assumed poor prognosis of heart failure patients with dilated left ventricle. © 2020 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of the European Society of Cardiolog

Myocardial strain analysis as a non-invasive screening test in the diagnosis of stable coronary artery disease

Background: Coronary artery disease (CAD) is one of the most prevalent diseases around the world; however, finding the best noninvasive, low-cost, and more easily accessible test for its screening has been a challenge for several years. Eighty-nine patients suspected of stable CAD underwent 2D-speckle-tracking echocardiography (2DSTE) at resting position and offline longitudinal myocardial strain analysis, followed by coronary angiography. The correlation of the global longitudinal strain (GLS) and territorial longitudinal strain (TLS) with significant CAD (70 and more stenosis in at least one coronary artery) was then evaluated. Results: The statistical analysis showed a significant correlation between low GLS and significant CAD (P=0.0001). The results also showed a significant correlation between low TLS and significant CAD in the left and right coronary artery territories. The optimal cut-off point of GLS for the detection of significant CAD was �19.25, with a sensitivity of 76.5 and specificity of 76.6. Conclusion: This study confirmed the usefulness of 2DSTE myocardial strain analysis in diagnosis of CAD for detecting the affected coronary arteries using GLS and SLS. © 2021, The Author(s)

Demand in the electricity market: analysis using big data

The traditional business model of energy companies is changing in recent years. The introduction of smart meters has led to an exponential increase in the volume of data available, and their analysis can help find consumption patterns among electric customers to reduce costs and protect the environment. Power plants generate electricity to cover peak consumption at specific times. A set of techniques called “demand response” tries to solve this problem using artificial intelligence proposals. This document proposes a method for processing large volumes of data such as those generated by smart meters. Both for the preprocessing and for the optimization and realization of this analysis big data techniques are used. Specifically, a distributed version of the k-means algorithm and several indices of internal validation of clustering for big data in Spark. The source data correspond to the consumption of electric customers in Bogota, Colombia during the year 2018. The analysis carried out in this study about consumers helps their characterization. This greater knowledge about consumer habits and types of customers can enhance the work of utilities

Data mining to identify risk factors associated with university students dropout

. This paper presents the identification of university students dropout patterns by means of data mining techniques. The database consists of a series of questionnaires and interviews to students from several universities in Colombia. The information was processed by the Weka software following the Knowledge Extraction Process methodology with the purpose of facilitating the interpretation of results and finding useful knowledge about the students. The partial results of data mining processing on the information about the generations of students of Industrial Engineering from 2016 to 2018 are analyzed and discussed, finding relationships between family, economic, and academic issues that indicate a probable desertion risk in students with common behaviors. These relationships provide enough and appropriate information for the decision-making process in the treatment of university dropout.Universidad Peruana de Ciencias Aplicadas, Universidad de la Costa, Universidad Libre Seccional Barranquilla, Corporación Universitaria Latinoamericana

Dropout-permanence analysis of university students using data mining

Dropout is a rejection method present in every educational system, related to the various selection processes, academic performance, and the efficiency of the system in general, that is, the result of the combination and effect of different variables. In this sense, the dropout of university students related to their academic performance is a matter of concern since several years ago. Academic information is analyzed in order to identify factors that influence students´ dropout at the University of Mumbai, India, by using a data mining technique. The data source contains information provided to the entrance (personal and educational background) and that is generated during the study period. The data selection and cleansing are made using different criteria of representation and implementation of classification algorithms such as decision trees, Bayesian networks, and rules. the following factors are identified as influential variables in the desertion: approved courses, quantity and results of attended courses, origin and age of entry of the student. Through this process, it was possible to identify the attributes that characterize the dropout cases and their relationship with the academic performance, especially in the first year of the career

An early warning method for agricultural products price spike based on artificial neural networks prediction

In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differentiate the agricultural sector from other economic sectors. On the other hand, the volatility of prices payed by producers, the high cost of raw materials, and the instability of both domestic and international markets are factors which have eroded the competitiveness and profitability of the agricultural sector. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in agricultural products, considering macro and micro economic variables and factors related to the seasons of the year

Intelligent and Distributed Data Warehouse for Student’s Academic Performance Analysis

In the academic world, a large amount of data is handled each day, ranging from student’s assessments to their socio-economic data. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. However, Data Warehouses are not able to perform predictive analysis by themselves, so machine intelligence techniques can be used for sorting, grouping, and predicting based on historical information to improve the analysis quality. This work describes a Data Warehouse architecture to carry out an academic performance analysis of students

Ellagic Acid Prevents Oxidative Stress, Inflammation, and Histopathological Alterations in Acrylamide-Induced Hepatotoxicity in Wistar Rats

The present study was designed to investigate the changes in rat liver tissue after administration of acrylamide (ACR) and ellagic acid (EA). The latter compound was applied for its strong antioxidant and anti-inflammatory properties. In the present study, 35 male Wistar rats were randomly divided into five equal groups. These groups were normal saline (NS), ACR (20 mg/kg), ACR + EA (10 and 30 mg/kg EA), and EA (30 mg/kg). At the end of the experiment, the rats were decapitated. Biochemical and histopathological studies were conducted on liver and serum samples. ACR administration significantly decreased hepatic GSH level, SOD, GPx, and CAT activity when compared to the NS group. Aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), nitric oxide (NO), protein carbonyl (PC), malondialdehyde (MDA), tumor necrosis factor alpha (TNF-α), and interleukin 1 beta (IL-1β) levels increased as a result of ACR administration. Administration of EA (more potently at a dose of 30 mg/kg) resulted in a significant reversal of biochemical, inflammatory, and hepatic markers in ACR-intoxicated rats. These biochemical and inflammatory disturbances were supported by histopathological observations of the liver. Our results indicate that EA might be useful for the treatment of the hepatotoxicity induced by ACR via ameliorative effects on biochemical, oxidative stress, and inflammatory indices. © 2019 Taylor & Francis Group, LLC

Neuroprotective effects of Ellagic acid against acrylamide-induced neurotoxicity in rats

Acrylamide (ACR) is an environmental contaminant and a well-known neurotoxin. Ellagic acid (EA), a natural plant polyphenol, has shown a variety of beneficial effects. The present study was designed to explore whether EA could attenuate ACR-induced neurotoxicity in rats and to explore the underlying mechanisms. Animals were divided into five groups. Group 1 was treated with normal saline (2 mL/kg) for 30 days. Group 2 was treated with ACR (20 mg/kg, orally) for 30 days. Groups 3 and 4 were treated with ACR and EA (10 and 30 mg/kg, orally) for 30 days. Group 5 was treated with EA (30 mg/kg, orally) for 30 days. Open field, rotarod and passive avoidance test were conducted to evaluate behavioral changes, respectively. The brain cortex was used for histological examination. Different oxidative parameters and inflammatory biomarkers were assessed in the brain cortex. ACR-administered rats showed a considerable impairment in exploratory behavior, motor performance as well as cognition. Our data also showed that ACR administration significantly increases malondialdehyde, nitric oxide, interleukin-1β and tumor necrosis factor-α levels. Moreover, it decreases brain glutathione level, superoxide dismutase, glutathione peroxidase, catalase activity. Co-administration of EA (especially 30 mg/kg, p.o.) prevented these changes; however, it did not affect the glutathione peroxidase activity. These results were supported by histopathological observations of the brain. Our results suggest that EA can be useful for protecting brain tissue against ACR-induced neurotoxicity through ameliorative effects on inflammatory indices and oxidative stress parameters. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group