Respiratory Function and Information Processing Speed in Coal Power Plant Workers: Moderating Effects of Physical Activity and Sedentary Behavior.

Background: In occupational settings like coal power plants, impaired respiratory function and cognitive performance can pose significant risks to worker safety and productivity. This study aimed to investigate the associations between respiratory muscle strength and information processing speed in coal power plant workers, and to explore whether these associations are moderated by physical activity and sedentary behavior.

Methods: A cross-sectional study was conducted among 171 coal power plant workers.

Prevalence and determinants of anaemia in South Asian diaspora women residing in Hong Kong: An exploratory cross-sectional study.

Background: Anaemia has been a significant public health challenge for the South Asian community, in particular women and migrant populations. Despite abundant research in developed Western settings, evidence in urbanized Asian settings is extremely scarce for comparisons. This study aims to assess the prevalence and social determinants of anaemia in South Asian women residing in Hong Kong, an advanced economy in Asia.

Blockade of ZMIZ1-GATA4 Axis Regulation Restores Youthfulness to Aged Cartilage.

Susceptibility to cartilage degeneration increases in an age-dependent manner and older cartilage exhibits increased catabolic factor expression leading to osteoarthritis (OA). While inhibition of cellular senescence can prevent age-related diseases, the understanding of the regulators governing cartilage senescence and the potential for senolytic intervention remains limited. Here, in vitro and in vivo results are reported, demonstrating for the first time that the transcriptional regulator, ZMIZ1, is upregulated in aged and OA cartilage, and that it acts through GATA4 to accelerate chondrocyte senescence and trigger cartilage deterioration.

Cross-Cultural Validation of the Binge Eating Disorder Screener-7 (BEDS-7) Across 42 Countries.

Objective: This study aimed to evaluate the reliability and validity of the Binge Eating Disorder Screener-7 (BEDS-7) across 42 countries and 26 languages, assessing its reliability and validity as a screening tool for binge-eating disorder (BED) in diverse cultural contexts. Specifically, it sought to enhance early recognition of BED symptoms in primary care settings globally, contributing to a standardized framework for assessing BED.

Method: The International Sex Survey, a cross-sectional online study, was conducted in 42 countries and 26 languages.

An Improved, High-Yield Method for Isolating Nuclei from Individual Zebrafish Embryos for Single-Nucleus RNA Sequencing.

Zebrafish is an ideal system to study the effect(s) of chemical, genetic, and environmental perturbations on development due to their high fecundity and fast growth. Recently, single-cell sequencing has emerged as a powerful tool to measure the effect of these perturbations at a whole-embryo scale. These types of experiments rely on the ability to isolate nuclei from a large number of individually barcoded zebrafish embryos in parallel.

Intrinsic and Extrinsic Unity for Chiral-Spin Alignment and Charge-to-Spin Conversion Induced by the Rashba-Edelstein Effect.

Reducing the dimensionality in layered materials typically yields properties distinct from bulk properties. In systems with broken inversion symmetry, strong spin-orbit coupling induces relativistic electron interactions such as the Rashba-Edelstein effect (REE). Initially proposed in two-dimensional magnets, applying the REE theory to real three-dimensional systems poses challenges, necessitating experimental validation.

Bitter Taste Receptor Agonists Induce Apoptosis in Papillary Thyroid Cancer.

Background: Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with a 20% recurrence rate. Bitter taste receptors (T2Rs) and their genes (TAS2Rs) may regulate survival in solid tumors. This study examined T2R expression and function in PTC cells.

Association between the Triglyceride-Glucose Index and Cardiovascular Risk and Mortality across Different Diabetes Durations: A Nationwide Cohort Study.

Background: We aimed to assess the association between triglyceride-glucose (TyG) index and cardiovascular disease (CVD) risk and mortality in a large cohort of diabetes patients.

Methods: A retrospective cohort study of 1,090,485 participants from the Korean National Health Insurance Service database was conducted. Participants were stratified into TyG quartiles.

High-Throughput Empirical and Virtual Screening To Discover Novel Inhibitors of Polyploid Giant Cancer Cells in Breast Cancer.

Therapy resistance in breast cancer is increasingly attributed to polyploid giant cancer cells (PGCCs), which arise through whole genome doubling and exhibit heightened resilience to standard treatments. Characterized by enlarged nuclei and increased DNA content, these cells tend to be dormant under therapeutic stress, driving disease relapse. Despite their critical role in resistance, strategies to effectively target PGCCs are limited, largely due to the lack of high-throughput methods for assessing their viability.

Co-Doped Hybrid Magneto-Mechano-Electric Generator for Powering Watt-Level IoT Systems.

Magneto-mechano-electric (MME) generators, which convert ambient magnetic energy into electricity, show promise as power sources for wireless Internet of Things (IoT) sensors. However, their output power remains insufficient for powering watt-level IoT applications. This study addresses this limitation by co-doping Pb(MgNb)O-Pb(Zr,Ti)O (PMN-PZT) piezoelectric single crystals with manganese (Mn) and lanthanum (La).

Oxygen Atom Transfer Reactions of Colloidal Metal Oxide Nanoparticles.

Redox transformations at metal oxide (MO)/solution interfaces are broadly important, and oxygen atom transfer (OAT) is one of the simplest and most fundamental examples of such reactivity. OAT is a two-electron transfer process, well-known in gas/solid reactions and catalysis. However, OAT is rarely directly observed at oxide/water interfaces, whose redox reactions are typically proposed to occur in one-electron steps.

Optimization of Polymeric Microneedle Electrode for High-Quality EMG recording.

This paper proposes the optimization of polyimide microneedle electrode using a 3D print double molding fabrication. Various shapes (circle, triangle, square, star, and hexagon) of microneedles are investigated by changing printing angles and double molding process. Aspect ratio, insertion force, and break point of the fabricated microneedles are investigated to maximize penetration capacity.

Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.

Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenarios requiring sustained knowledge transfer, the performance of TL-based models gradually declines as the number of transfers increases-a phenomenon known as catastrophic forgetting. To address this issue, we introduce a novel domain-incremental learning framework for the continual motor imagery (MI) EEG classification.

EEG Channel Localization and Selection via Training with Noise Injection for BCI Applications.

Electroencephalography (EEG) is crucial for monitoring brain activity in neuroscience and clinical applications. However, the multitude of channels recorded by scalp electrodes poses challenges, including impractical usage and high model complexity. This paper addresses the challenges of high dimensionality in EEG data and introduces an innovative EEG channel selection algorithm, LSvT-NI, based on model training and noise injection, achieving substantial reductions in channels, model size, and complexity while maintaining high classification accuracy.

Walking Speed and Uncertainty Estimation Using Mixture Density Networks for Dynamic Ambulatory Environments.

Walking speed, often considered a representative indicator of activity levels, becomes notably reduced as muscle strength and cardiovascular function decline with aging. Wearable walking rehabilitation devices aim to alleviate the effort during walking or enhance the necessary muscles. Measuring the wearer's walking speed provides an objective assessment of rehabilitation progress.

Ex vivo studies of efficacy of DeepFocus: a technique for minimally-invasive deep-brain stimulation.

Invasive deep-brain stimulation is increasingly being investigated as a treatment for neural disorders. A non-invasive alternative for deep-brain neuromodulation would likely broaden the range of application. However, existing techniques, such as transcranial electrical or magnetic stimulation (TES, TMS), are limited in their depth of stimulation.

Feasibility Study for Controlled Radiation Capsule for High Source Activity for Low Dose Radiation Brachytherapy.

In this paper, we propose a new radiation capsule design to enable a high dose rate for a low energy radiation source Iodine-125, conventionally used for low dose radiation (LDR) brachytherapy to lower the radiation risk and reduce the treatment duration for cancer treatment. Conventional LDR has a dose rate of less than 2 Gy/day. The proposed capsule enables a high dose rate of 10 Gy/min or higher due to the ability to control the stoppage of the radiation.

EMGCipher: Decoding Electromyography for Upper-limb Gesture Classification with Explainable AI for Resource Optimization.

Assistive limb devices often employ surface electromyography (sEMG) and deep learning (DL) models for gesture classification. While DL models effectively classify diverse upper-limb gestures, their decision-making mechanisms often lack transparency. To address this, we introduce EMGCipher, an interpretable DL framework for upper-limb gesture classification using sEMG.

Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.

Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remove artifacts such as eye movement or use techniques such as linear detrending, which inadvertently discard crucial task-relevant information. To address this issue, we present BGNet, a novel deep learning framework that leverages underutilized baseline EEG signals for dynamic noise mitigation and robust feature extraction to improve motor imagery (MI) EEG classification.

Reproduction of central-brachial-radial arterial blood pressure wave propagation using a cardiovascular hardware simulator.

This study reproduced changes according to the central-brachial-radial blood pressure wave propagation using a cardiovascular hardware simulator. Blood pressure is a key indicator of cardiovascular health, and its importance has recently emerged, and research into the correlation between the two is in progress. This requires a large amount of clinical data, but the amount and distribution are limited.

Classification of Abnormal Gaits with Machine Learning Algorithms using Sensor-Inherited Insoles.

The digital health industry's interest in gait analysis has driven research into sensor-enabled insoles for practical, everyday gait monitoring. Traditional methods, such as 3D motion capture systems, are costly and time-consuming. To address this, we propose an efficient method to evaluate gait performance.

Enhancing Few-Shot Chest X-ray Classification through Generative Class Augmentation.

Meta-learning-based models trained on multiple classification tasks based on multiple classes can adapt to new classification tasks with limited training samples, thereby achieving few-shot learning. However, when the number of classes in the classification task chest X-ray image analysis is also limited, meta-learning can result in overfitting. This study sought to overcome this with a class augmentation method using a generative adversarial network to generate pseudo-classes, thereby increasing the number of classes.

Voltage Independent Depth Control and Acute Lesion Formation Findings in Epicardial Pulsed Field Ablation System for Surgical Ablations.

Pulsed Field Ablation (PFA) is a novel ablation technology that utilizes electric field strength to ablate tissue. PFA is unique in its cell selectivity, which is found in the biophysics of the bilipid membrane's sensitivity to electric fields. PFA permits an unseen level of flexibility in its delivery due to its non-abrasive ablation methods and ability to selectively ablate regardless of direct contact.

Proxy-based Masking Module for Revealing Relevance of Characteristics in Motor Imagery.

Brain-computer interface (BCI) has been developed for communication between users and external devices by reflecting users' status and intentions. Motor imagery (MI) is one of the BCI paradigms for controlling external devices by imagining muscle movements. MI-based EEG signals generally tend to contain signals with sparse MI characteristics (sparse MI signals).