Clinical features, socioeconomic status, management, short and long-term outcomes of patients with acute myocardial infarction: Phase I results of PEACE MENA registry.

Background: The Program for the Evaluation and Management of Cardiac Events in the Middle East and North Africa (PEACE MENA) is a prospective registry program in Arabian countries that involves in patients with acute myocardial infarction (AMI) or acute heart failure (AHF).

Methods: This prospective, multi-center, multi-country study is the first report of the baseline characteristics and outcomes of inpatients with AMI who were enrolled during the first 14-month recruitment phase. We report the clinical characteristics, socioeconomic, educational levels, and management, in-hospital, one month and one-year outcomes.

Clinical Features, Socioeconomic Status, Management, and Outcomes of Acute Heart Failure: PEACE MENA Registry Phase I Results.

Introduction: PEACE MENA (Program for the Evaluation and Management of Cardiac Events in the Middle East and North Africa) is a prospective registry in Arab countries for in-patients with acute myocardial infarction (AMI) or acute heart failure (AHF). Here, we report the baseline characteristics and outcomes of in-patients with AHF who were enrolled during the first 14 months of the recruitment phase.

Methods: A prospective, multi-centre, multi-country study including patients hospitalized with AHF was conducted.

Intrinsic Dimension Estimation-Based Feature Selection and Multinomial Logistic Regression for Classification of Bearing Faults Using Compressively Sampled Vibration Signals.

As failures of rolling bearings lead to major failures in rotating machines, recent vibration-based rolling bearing fault diagnosis techniques are focused on obtaining useful fault features from the huge collection of raw data. However, too many features reduce the classification accuracy and increase the computation time. This paper proposes an effective feature selection technique based on intrinsic dimension estimation of compressively sampled vibration signals.

The Role of Coronary Computed Tomography Angiography in Evaluation of High Troponin Patients with Narrow-Complex Supraventricular Tachycardia.

Aim: The aim of this study was to investigate the relation of high-sensitive cardiac troponin T (hs-cTnT) elevation with characteristics of supraventricular tachycardia (SVT) episode (duration and maximum heart rate) and coronary computed tomography angiography (CCTA) findings in patients with SVT who presented to the emergency room with palpitation.

Methods: This retrospective, single-center, noninvasive study included all patients aged between 18 years and 65 years who presented to the emergency department due to narrow-complex SVT and underwent CCTA to rule out coronary artery disease (CAD) due to elevation of hs-cTnT and reverted back to sinus rhythm after intravenous adenosine. The first, second, and the maximum hs-cTnT levels were obtained from the database.

Surgical management of spontaneous thoracic and lumbar spondylodiscitis by fixation and debridement.

Background: Spondylodiscitis could be considered one of the most disturbing challenges that face neurosurgeons due to variety of management strategies. The lumbar region was highly affected then dorsal region with higher percentage for lesion in L4/5 (25%) followed by T11/12 and L5/S1 (15%). In our study, we discuss the efficacy of debridement and fixation in cases of spontaneous thoracic and lumbar spondylodiscitis.

Intelligent Fault Diagnosis Framework for Modular Multilevel Converters in HVDC Transmission.

Open circuit failure mode in insulated-gate bipolar transistors (IGBT) is one of the most common faults in modular multilevel converters (MMCs). Several techniques for MMC fault diagnosis based on threshold parameters have been proposed, but very few studies have considered artificial intelligence (AI) techniques. Using thresholds has the difficulty of selecting suitable threshold values for different operating conditions.

Three dimensional custom-made PEEK cranioplasty.

Background: An optimal reconstruction of calvarial skull defects is a challenge for neurosurgeons, and the strategy used to achieve the best result remains debatable. Therefore, we conducted this study to compare the esthetic and functional outcome of custom-made three-dimensional (3D) cranioprostheses to handmade bone cement in reconstructing calvarial skull defects.

Methods: We included 66 patients above 10 years of age with calvarial skull defects and undergoing reconstruction: 33 were enrolled in the custom-made 3D implants group and 33 in the handmade implants group in the period from August 2017 to December 2020 in the neurosurgery department of Fayoum University Hospital.

Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method.

Fault detection and classification are two of the challenging tasks in Modular Multilevel Converters in High Voltage Direct Current (MMC-HVDC) systems. To directly classify the raw sensor data without certain feature extraction and classifier design, a long short-term memory (LSTM) neural network is proposed and used for seven states of the MMC-HVDC transmission power system simulated by Power Systems Computer Aided Design/Electromagnetic Transients including DC (PSCAD/EMTDC). It is observed that the LSTM method can detect faults with 100% accuracy and classify different faults as well as provide promising fault classification performance.

The importance of bedside echocardiographic assessment in emergency admissions with probable acute coronary syndromes: A case of an unruptured giant ascending aorta aneurysm.

Symptomatic aortic aneurysms can manifest in different clinical settings, such as acute coronary syndrome (ACS), acute heart failure, a shock that is mostly due to the complications related to dissection or rupture of the aneurysm. In these clinical settings, the diagnosis can be established with the help of medical history, physical examination, and promptly performed imaging tests. However, the diagnosis of an asymptomatic aortic aneurysm is usually incidental.

Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods.

In this paper, we explore learning methods to improve the performance of the open-circuit fault diagnosis of modular multilevel converters (MMCs). Two deep learning methods, namely, convolutional neural networks (CNN) and auto encoder based deep neural networks (AE-based DNN), as well as stand-alone SoftMax classifier are explored for the detection and classification of faults of MMC-based high voltage direct current converter (MMC-HVDC). Only AC-side three-phase current and the upper and lower bridges' currents of the MMCs are used directly in our proposed approaches without any explicit feature extraction or feature subset selection.

Internet addiction disorder detection of Chinese college students using several personality questionnaire data and support vector machine.

With the unprecedented development of the Internet, it also brings the challenge of Internet Addiction (IA), which is hard to diagnose and cure according to the state-of-art research. In this study, we explored the feasibility of machine learning methods to detect IA. We acquired a dataset consisting of 2397 Chinese college students from the University (Age: 19.