Detection of shockable ventricular cardiac arrhythmias from ECG signals using FFREWT filter-bank and deep convolutional neural network.

Among various life-threatening cardiac disorders, ventricular tachycardia (VT) and ventricular fibrillation (VF) are shockable ventricular cardiac arrhythmias (SVCA) which require immediate defibrillation therapy for the survival of patients. Timely and accurate detection of rapid VT or VF episodes using ECG signals is extremely important before initiating external defibrillator (AED) and implantable cardioverter-defibrillator (ICD) therapies. In this paper, a novel approach for the detection of SVCA using ECG signals is proposed.

COVIDiag: a clinical CAD system to diagnose COVID-19 pneumonia based on CT findings.

Objectives: CT findings of COVID-19 look similar to other atypical and viral (non-COVID-19) pneumonia diseases. This study proposes a clinical computer-aided diagnosis (CAD) system using CT features to automatically discriminate COVID-19 from non-COVID-19 pneumonia patients.

Methods: Overall, 612 patients (306 COVID-19 and 306 non-COVID-19 pneumonia) were recruited.

Axicabtagene Ciloleucel in the Non-Trial Setting: Outcomes and Correlates of Response, Resistance, and Toxicity.

Purpose: Axicabtagene ciloleucel (axi-cel) was approved by the Food and Drug Administration for relapsed aggressive B-cell non-Hodgkin lymphoma in part on the basis of durable remission rates of approximately 40% in a clinical trial population. Whether this efficacy, and the rates of toxicity, would be consistent in a postcommercial setting, with relaxed eligibility criteria and bridging therapy, is unknown. This study describes the efficacy and safety correlates and outcomes in this setting.

Utilization of Family Planning Methods Among Postpartum Mothers in Kailali District, Nepal.

Background: Postpartum contraceptives use offers a distinctive opportunity to maintain appropriate birth spacing for health benefits to both mother and child. However, the concept of postpartum family planning (PPFP) is poorly understood and contraceptives use during the postpartum period remains low in Nepal. Therefore, this study aimed to assess the factors affecting the utilization of family planning (FP) methods among postpartum mothers in the Kailali district, Nepal.

Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks.

Fast diagnostic methods can control and prevent the spread of pandemic diseases like coronavirus disease 2019 (COVID-19) and assist physicians to better manage patients in high workload conditions. Although a laboratory test is the current routine diagnostic tool, it is time-consuming, imposing a high cost and requiring a well-equipped laboratory for analysis. Computed tomography (CT) has thus far become a fast method to diagnose patients with COVID-19.

The biocompatibility of polyaniline and polypyrrole 2: Doping with organic phosphonates.

Conducting polymers (CP) can be used as pH- and/or electro-responsive components in various bioapplications, for example, in 4D smart scaffolds. The ability of CP to maintain conductivity under physiological conditions is, therefore, their crucial property. Unfortunately, the conductivity of the CP rapidly decreases in physiological environment, as their conducting salts convert to non-conducting bases.

Automated Detection of Presymptomatic Conditions in Spinocerebellar Ataxia Type 2 Using Monte Carlo Dropout and Deep Neural Network Techniques with Electrooculogram Signals.

Application of deep learning (DL) to the field of healthcare is aiding clinicians to make an accurate diagnosis. DL provides reliable results for image processing and sensor interpretation problems most of the time. However, model uncertainty should also be thoroughly quantified.

Automated sleep apnea detection from cardio-pulmonary signal using bivariate fast and adaptive EMD coupled with cross time-frequency analysis.

Sleep apnea is a sleep related pathology in which breathing or respiratory activity of an individual is obstructed, resulting in variations in the cardio-pulmonary (CP) activity. The monitoring of both cardiac (heart rate (HR)) and pulmonary (respiration rate (RR)) activities are important for the automated detection of this ailment. In this paper, we propose a novel automated approach for sleep apnea detection using the bivariate CP signal.

Automated pre-screening of arrhythmia using hybrid combination of Fourier-Bessel expansion and LSTM.

Health care in developing countries demands systems-based screening solutions. In view of this, we present a new rhythm-based methodology for the point-of-care diagnosis of cardiac arrhythmia at a primary level. Such a system will reduce the workload of cardiologists significantly.

Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review.

Deep learning models have become a popular mode to classify electrocardiogram (ECG) data. Investigators have used a variety of deep learning techniques for this application. Herein, a detailed examination of deep learning methods for ECG arrhythmia detection is provided.

Ankle Surgery in a Patient with Acute Subdural Hematoma Under Combined Lumbar Plexus and Proximal Sciatic Nerve Block - A Case Report.

Acute subdural hematoma (aSDH) is commonly encountered in the emergency department in patients with traumatic injuries. If the hematoma is small, non-expanding and asymptomatic, it is managed conservatively. However, other injuries sustained during trauma may warrant surgical intervention, during which anesthetic management becomes challenging.

Automated phase classification in cyclic alternating patterns in sleep stages using Wigner-Ville Distribution based features.

Sleep is one of the most important body mechanisms responsible for the proper functioning of human body. Cyclic alternating patterns (CAP) play an indispensable role in the analysis of sleep quality and related disorders like nocturnal front lobe epilepsy, insomnia, narcolepsy etc. The traditional manual segregation methods of CAP phases by the medical experts are prone to human fatigue and errors which may lead to inaccurate diagnosis of sleep stages.

Development of an automated system for the detection of genotype in polypoidal choroidal vasculopathy using retinal image phenotype.

Background And Objectives: Polypoidal choroidal vasculopathy (PCV) is a retinal disorder characterized by the presence of aneurismal polypoidal lesions in the choroidal vasculature. A single nucleotide polymorphism (SNP) is a common genetic variant which may be associated with the disease. This study is to investigate the association of HERPUD1 (rs2217332) gene with PCV in the Indian population and develop an automated system for genotype and phenotype correlation using fundus images and machine learning methods.

Impact of Hydrogen Bonds Limited Dipolar Disorder in High-k Polymer Gate Dielectric on Charge Carrier Transport in OFET.

The paper contributes to the characterization and understanding the mutual interactions of the polar polymer gate dielectric and organic semiconductor in organic field effect transistors (OFETs). It has been shown on the example of cyanoethylated polyvinylalcohol (CEPVA), the high-k dielectric containing strong polar side groups, that the conditions during dielectric layer solidification can significantly affect the charge transport in the semiconductor layer. In contrast to the previous literature we attributed the reduced mobility to the broader distribution of the semiconductor density of states (DOS) due to a significant dipolar disorder in the dielectric layer.

A novel hybrid approach for automated detection of retinal detachment using ultrasound images.

Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes.

Pericapsular Nerve Group Block: An Excellent Option for Analgesia for Positional Pain in Hip Fractures.

Fractures in and around the hip are common presentations in the emergency department. It is commonly seen in the elderly as a result of osteoporotic changes. However, younger age groups are also affected, especially as a result of high velocity trauma.

A computational intelligence tool for the detection of hypertension using empirical mode decomposition.

Hypertension (HPT), also known as high blood pressure, is a precursor to heart, brain or kidney diseases. Some symptoms of HPT include headaches, dizziness and fainting. The potential diagnosis of masked hypertension is of specific interest in this study.

Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals.

Heart valve diseases (HVDs) are a group of cardiovascular abnormalities, and the causes of HVDs are blood clots, congestive heart failure, stroke, and sudden cardiac death, if not treated timely. Hence, the detection of HVDs at the initial stage is very important in cardiovascular engineering to reduce the mortality rate. In this article, we propose a new approach for the detection of HVDs using phonocardiogram (PCG) signals.

Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals.

Autistic individuals often have difficulties expressing or controlling emotions and have poor eye contact, among other symptoms. The prevalence of autism is increasing globally, posing a need to address this concern. Current diagnostic systems have particular limitations; hence, some individuals go undiagnosed or the diagnosis is delayed.

Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network.

Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset.

Sptlc1 is essential for myeloid differentiation and hematopoietic homeostasis.

Serine palmitoyltransferase (SPT) long-chain base subunit 1 (SPTLC1) is 1 of the 2 main catalytic subunits of the SPT complex, which catalyzes the first and rate-limiting step of sphingolipid biosynthesis. Here, we show that Sptlc1 deletion in adult bone marrow (BM) cells results in defective myeloid differentiation. In chimeric mice from noncompetitive BM transplant assays, there was an expansion of the Lin- c-Kit+ Sca-1+ compartment due to increased multipotent progenitor production, but myeloid differentiation was severely compromised.