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Heart failure represents a dynamic clinical challenge with the continuous rise of a multi‐morbid and ageing population. Yet, the evolving nature of mechanical circulatory support offers a variety of means to manage candidates who might benefit from such interventions. This narrative review focuses on the role of the main mechanical circulatory support devices, such as ventricular assist device, extracorporeal membrane oxygenation, Impella and TandemHeart, in the physiological process of ventricular unloading and remodelling in heart failure, highlighting their characteristics, mechanism and clinical outcomes. The outcome measures described include physiological changes (i.e., stroke volume or preload and afterload), intracardiac pressure (i.e., end‐diastolic pressure) and extracardiac pressure (i.e., pulmonary capillary wedge pressure). Overall, all the above mechanical circulatory support strategies can facilitate the unloading of the ventricular failure through different mechanisms, which subsequently affects the ventricular remodelling process. These physiological changes start immediately after ventricular assist device implantation. The devices are indicated in different but overlapping populations and operate in distinctive ways; yet, they have evidenced performance to a favourable standard to improve cardiac function in heart failure, although this proved variable for different devices, and further high‐quality trials are vital to assess their clinical outcomes further. Both Impella and TandemHeart are indicated mainly in cardiogenic shock and high‐risk percutaneous coronary intervention patients; at the time the literature was evaluated, both devices were found to yield a significant improvement in haemodynamics but not in survival. Nevertheless, the choice of device strategy should be based on individual patient factors, including indication, to optimize clinical outcomes. What is the topic of this review?This narrative review focuses on the role of the main mechanical circulatory support devices in the physiological process of ventricular unloading and remodelling in heart failure.What advances does it highlight?This narrative review presents a comprehensive overview of the main mechanical circulatory support devices in heart failure, highlighting their characteristics, mechanisms and clinical outcomes. These devices can facilitate the unloading of ventricular failure to varying extents and through different mechanisms, which subsequently affects the ventricular remodelling process. Nevertheless, the choice of device strategy should be based on individual patient factors, including indication, to optimize clinical outcomes. [ABSTRACT FROM AUTHOR]

Improving the performance of e-learning services to provide a scalable and effective e-learning system is a big challenge for the educational organizations. The e-learning system faces many challenges in pedagogical services (e.g. learning design and learning content problems), and technical services (e.g. resource provisioning and financial cost). This article presents an e-learning environment based on cloud computing (PCLE) in attempt to enhance the e-learning services' by customizing the contents of the course's material depending on the students' knowledge, experiences, and requirements. Also, the system focuses on supporting and achieving reusability, interoperability, adaptation and personalization in order to overcome the passive role of the student and transfer it into an interactive and useful participation. On the other hand, PCLE is built over the cloud computing environment trying to overcome the traditional web-hosting challenges as scalability, availability, and cost.

Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutional Network (DenseNet)-121 and Visual Geometry Group (VGG)-16 as feature extractor networks, bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction, and the Support Vector Machine (SVM) and Random Forest (RF) algorithms to perform classification. For improved performance, the selected pre-trained CNN hyperparameters have been optimized using a modified grey wolf optimization method. The experimental analysis for the presented model on the Mammographic Image Analysis Society (MIAS) dataset shows that the VGG16 model is powerful for BC classification with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) of 99.86%, 99.9%, 99.7%, 97.1%, and 1.0, respectively, on the MIAS dataset and 99.4%, 99.03%, 99.2%, 97.4%, and 1.0, respectively, on the INbreast dataset. [ABSTRACT FROM AUTHOR]

Introduction: Cardiogenic shock (CS) is a complex life-threatening condition that results from primary cardiac dysfunction, leading to persistent hypotension and systemic hypoperfusion. Among the therapeutic options for CS are various percutaneous mechanical circulatory support (MCS) devices that have emerged as an increasingly effective hemodynamic support option. Percutaneous therapies can act as short-term mechanical circulatory assistance and can be split into intra-aortic balloon pump (IABP) and non-IABP percutaneous mechanical devices. Areas covered: This review will evaluate the MCS value while considering the mortality rate improvements. We also aim to outline the function of pharmacotherapies and percutaneous hemodynamic MCS devices in managing CS patients to avoid the onset of end-organ dysfunction and improve both early and late outcomes. Expert opinion: Given the complexity, acuity and high mortality associated with CS, and despite the availability and efficacy of pharmacological management, MCS is required to achieve hemodynamic stability and improve survival. Various percutaneous MCS devices are available with varying indications and clinical outcomes. The rates of early mortality and complications were found to be comparable between the four devices, yet, IABP seemed to show the most optimal clinical profile whilst ECMO demonstrated its more long-term efficacy. [ABSTRACT FROM AUTHOR]

Deep learning (DL) plays a critical role in processing and converting data into knowledge and decisions. DL technologies have been applied in a variety of applications, including image, video, and genome sequence analysis. In deep learning the most widely utilized architecture is Convolutional Neural Networks (CNN) are taught discriminatory traits in a supervised environment. In comparison to other classic neural networks, CNN makes use of a limited number of artificial neurons, therefore it is ideal for the recognition and processing of wheat gene sequences. Wheat is an essential crop of cereals for people around the world. Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector. In quantitative genetics prediction of genetic values is a central issue. Wheat is an allohexaploid (AABBDD) with three distinct genomes. The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat, Therefore, genome sequence approaches based on techniques of Artificial Intelligence (AI) are necessary. This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources andmanaging genetic variation in their breeding program, as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints. In this paper, the hyperparameters of the network are optimized in the CNN to decrease the requirement formanual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks (CNN). [ABSTRACT FROM AUTHOR]

Mucosal-associated invariant T (MAIT) cells are innate-like T cells that recognize bacterial riboflavin–based metabolites as activating antigens. Although MAIT cells are found in tissues, it is unknown whether any host tissue–derived antigens exist. Here, we report that a sulfated bile acid, cholic acid 7-sulfate (CA7S), binds the nonclassical MHC class I protein MR1 and is recognized by MAIT cells. CA7S is a host-derived metabolite whose levels were reduced by more than 98% in germ-free mice. Deletion of the sulfotransferase 2a family of enzymes (Sult2a1-8) responsible for CA7S synthesis reduced the number of thymic MAIT cells in mice. Moreover, recognition of CA7S induced MAIT cell survival and the expression of a homeostatic gene signature. By contrast, recognition of a previously described foreign antigen, 5-(2-oxopropylideneamino)-6-d-ribitylaminouracil (5-OP-RU), drove MAIT cell proliferation and the expression of inflammatory genes. Thus, CA7S is an endogenous antigen for MAIT cells, which promotes their development and function. Editor's summary: Mucosal-associated invariant T (MAIT) cells are an innate-like T cell subset important for mucosal homeostasis, which recognize microbiota-derived vitamin B metabolites presented by the MHC-Ib molecule MR1. It is unclear, however, whether any host-derived ligands are also important for MAIT cell function and survival. Ito et al. report that the host-derived bile acid metabolite cholic acid 7-sulfate (CA7S) binds MR1 and is recognized by MAIT cells in mice. Moreover, genetic deletion of sulfotransferase 2a enzymes (Sult2a1-8) needed for its synthesis in mice reduced thymic MAIT cell numbers. A previously characterized vitamin B metabolite, 5-OP-RU, triggered human MAIT cell proliferation and the expression of inflammatory genes. By contrast, CA7S promoted human MAIT cell survival and a gene program associated with wound healing and immunoregulation. —Seth Thomas Scanlon [ABSTRACT FROM AUTHOR]

The extent to which patients with an abdominal aortic aneurysm (AAA) should exercise remains unclear, given theoretical concerns over the perceived risk of blood pressure‐induced rupture, which is often catastrophic. This is especially pertinent during cardiopulmonary exercise testing, when patients are required to perform incremental exercise to symptom‐limited exhaustion for the determination of cardiorespiratory fitness. This multimodal metric is being used increasingly as a complementary diagnostic tool to inform risk stratification and subsequent management of patients undergoing AAA surgery. In this review, we bring together a multidisciplinary group of physiologists, exercise scientists, anaesthetists, radiologists and surgeons to challenge the enduring 'myth' that AAA patients should be fearful of and avoid rigorous exercise. On the contrary, by appraising fundamental vascular mechanobiological forces associated with exercise, in conjunction with 'methodological' recommendations for risk mitigation specific to this patient population, we highlight that the benefits conferred by cardiopulmonary exercise testing and exercise training across the continuum of intensity far outweigh the short‐term risks posed by potential AAA rupture. [ABSTRACT FROM AUTHOR]