The effects of eccentric phase tempo in squats on hypertrophy, strength, and contractile properties of the quadriceps femoris muscle.

Introduction: The aim of the present study was to investigate the effects of eccentric phase tempo in squats on hypertrophy, strength, and contractile properties of the quadriceps femoris (QF) muscle.

Methods: Eighteen participants (10 males and 8 females, age 24.0 ± 1.

On hyper-parameter selection for guaranteed convergence of RMSProp.

RMSProp is one of the most popular stochastic optimization algorithms in deep learning applications. However, recent work has pointed out that this method may not converge to the optimal solution even in simple convex settings. To this end, we propose a time-varying version of RMSProp to fix the non-convergence issues.

Tensor ring rank determination using odd-dimensional unfolding.

While tensor ring (TR) decomposition methods have been extensively studied, the determination of TR-ranks remains a challenging problem, with existing methods being typically sensitive to the determination of the starting rank (i.e., the first rank to be optimized).

A Change in Nosocomial Infections among Surgical ICU Patients in the COVID-19 Era and MALDI-TOF Mass Spectrometry-A Cross-Sectional Study.

During the COVID-19 pandemic, changes occurred within the surgical patient population. An increase in the frequency of resistant Gram-negative bacteria has since been recorded worldwide. After the start of the COVID-19 pandemic, microbiological diagnostics in our institution was performed using MALDI-TOF mass spectrometry.

Finding core labels for maximizing generalization of graph neural networks.

Graph neural networks (GNNs) have become a popular approach for semi-supervised graph representation learning. GNNs research has generally focused on improving methodological details, whereas less attention has been paid to exploring the importance of labeling the data. However, for semi-supervised learning, the quality of training data is vital.

UAdam: Unified Adam-Type Algorithmic Framework for Nonconvex Optimization.

Adam-type algorithms have become a preferred choice for optimization in the deep learning setting; however, despite their success, their convergence is still not well understood. To this end, we introduce a unified framework for Adam-type algorithms, termed UAdam. It is equipped with a general form of the second-order moment, which makes it possible to include Adam and its existing and future variants as special cases, such as NAdam, AMSGrad, AdaBound, AdaFom, and Adan.

Differences and Relationship between Body Composition and Motor Coordination in Children Aged 6-7 Years.

Background: The primary goal of this study was to investigate the relationship between body composition and motor coordination performance, and the secondary goal was to determine sex differences in body composition and motor coordination of preschool children.

Methods: Forty-eight children (23 boys and 25 girls) underwent assessments for body composition and motor coordination using the Köperkoordinationstest für Kinder (KTK).

Results: Linear regression analysis revealed significant associations between body composition and motor coordination in boys ( < 0.

Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease.

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone.

From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People.

Objective: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG.

Methods And Procedures: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work.

Novel PLGA-based nanoformulation decreases doxorubicin-induced cardiotoxicity.

Nanotechnology has the potential to provide formulations of antitumor agents with increased selectivity towards cancer tissue thereby decreasing systemic toxicity. This study evaluated the potential of novel nanoformulation based on poly(lactic--glycolic acid) (PLGA) to reduce the cardiotoxic potential of doxorubicin (DOX). toxicity of PLGADOX was compared with clinically approved non-PEGylated, liposomal nanoformulation of DOX (LipoDOX) and conventional DOX form (ConvDOX).

Case report: Rapid-onset parkinsonism after a hornet sting.

Neurological manifestations with basal ganglia involvement following stings are rare and clinically ill-defined conditions. We present a patient with acute parkinsonism non-responsive to levodopa, who developed striatal lesions after a hornet sting. We report his response to immunomodulatory treatment and subsequent clinical and brain magnetic resonance imaging (MRI) follow-up.

Physically Meaningful Surrogate Data for COPD.

The rapidly increasing prevalence of debilitating breathing disorders, such as chronic obstructive pulmonary disease (COPD), calls for a meaningful integration of artificial intelligence (AI) into respiratory healthcare. Deep learning techniques are "data hungry" whilst patient-based data is invariably expensive and time consuming to record. To this end, we introduce a novel COPD-simulator, a physical apparatus with an easy to replicate design which enables rapid and effective generation of a wide range of COPD-like data from healthy subjects, for enhanced training of deep learning frameworks.

Impact of COVID-19 on cardiac surgery outcomes.

Aim: This study aimed to assess the impact of COVID-19 infection on cardiac surgery outcomes in patients who contracted COVID-19 peri-operatively or had recently recovered from COVID-19.

Methods: The study prospectively enrolled 95 patients scheduled for cardiac surgery who had recently recovered from COVID-19. This formed the post-COVID-19 group.

Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.

The main purpose of this paper is to provide information on how to create a convolutional neural network (CNN) for extracting features from EEG signals. Our task was to understand the primary aspects of creating and fine-tuning CNNs for various application scenarios. We considered the characteristics of EEG signals, coupled with an exploration of various signal processing and data preparation techniques.

The Deep-Match Framework: R-Peak Detection in Ear-ECG.

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes which are embedded within earphones. However, the significant increase in wearability and comfort enabled by Ear-ECG is often accompanied by a degradation in signal quality - an obstacle that is shared by the majority of wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios.

Hearables: feasibility of recording cardiac rhythms from single in-ear locations.

The ear is well positioned to accommodate both brain and vital signs monitoring, via so-called hearable devices. Consequently, ear-based electroencephalography has recently garnered great interest. However, despite the considerable potential of hearable based cardiac monitoring, the biophysics and characteristic cardiac rhythm of ear-based electrocardiography (ECG) are not yet well understood.

Reducing racial bias in SpO estimation: The effects of skin pigmentation.

Accurate pulse-oximeter readings are critical for clinical decisions, especially when arterial blood-gas tests - the gold standard for determining oxygen saturation levels - are not available, such as when determining COVID-19 severity. Several studies demonstrate that pulse oxygen saturation estimated from photoplethysmography (PPG) introduces a racial bias due to the more profound scattering of light in subjects with darker skin due to the increased presence of melanin. This leads to an overestimation of blood oxygen saturation in those with darker skin that is increased for low blood oxygen levels and can result in a patient not receiving potentially life-saving supplemental oxygen.

Feasibility of Transfer Learning from Finger PPG to In-Ear PPG.

The success of deep learning methods has enabled many modern wearable health applications, but has also highlighted the critical caveat of their extremely data hungry nature. While the widely explored wrist and finger photoplethysmography (PPG) sites are less affected, given the large available databases, this issue is prohibitive to exploring the full potential of novel recording locations such as in-ear wearables. To this end, we assess the feasibility of transfer learning from finger PPG to in-ear PPG in the context of deep learning for respiratory monitoring.

Hearables: Automatic Sleep Scoring from Single-Channel Ear-EEG in Older Adults.

Sleep disorders are a prevalent problem among older adults, yet obtaining an accurate and reliable assessment of sleep quality can be challenging. Traditional polysomnography (PSG) is the gold standard for sleep staging, but is obtrusive, expensive, and requires expert assistance. To this end, we propose a minimally invasive single-channel single ear-EEG automatic sleep staging method for older adults.

Generalization Error of a Regression Model for Non-Invasive Blood Pressure Monitoring using a Single Photoplethysmography (PPG) Signal.

Photoplethysmography (PPG) sensors integrated in wearable devices offer the potential to monitor arterial blood pressure (ABP) in patients. Such cuffless, non-invasive, and continuous solution is suitable for remote and ambulatory monitoring. A machine learning model based on PPG signal can be used to detect hypertension, estimate beat-by-beat ABP values, and even reconstruct the shape of the ABP.

Hearables: Heart Rate Variability from Ear Electrocardiogram and Ear Photoplethysmogram (Ear-ECG and Ear-PPG).

This work aims to classify physiological states using heart rate variability (HRV) features extracted from electrocardiograms recorded in the ears (ear-ECG). The physiological states considered in this work are: (a) normal breathing, (b) controlled slow breathing, and (c) mental exercises. Since both (b) and (c) cause higher variance in heartbeat intervals, breathing-related features (SpO and mean breathing interval) from the ear Photoplethysmogram (ear-PPG) are used to facilitate classification.

Elderly rats fed with a high-fat high-sucrose diet developed sex-dependent metabolic syndrome regardless of long-term metformin and liraglutide treatment.

Aim/introduction: The study aimed to determine the effectiveness of early antidiabetic therapy in reversing metabolic changes caused by high-fat and high-sucrose diet (HFHSD) in both sexes.

Methods: Elderly Sprague-Dawley rats, 45 weeks old, were randomized into four groups: a control group fed on the standard diet (STD), one group fed the HFHSD, and two groups fed the HFHSD along with long-term treatment of either metformin (HFHSD+M) or liraglutide (HFHSD+L). Antidiabetic treatment started 5 weeks after the introduction of the diet and lasted 13 weeks until the animals were 64 weeks old.

Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller.

At present, a medium-level microcontroller is capable of performing edge computing and can handle the computation of neural network kernel functions. This makes it possible to implement a complete end-to-end solution incorporating signal acquisition, digital signal processing, and machine learning for the classification of cardiac arrhythmias on a small wearable device. In this work, we describe the design and implementation of several classifiers for atrial fibrillation detection on a general-purpose ARM Cortex-M4 microcontroller.