Browse Articles

Oral HPV detection and genotyping by next-generation sequencing in a healthy Palestinian cohort: pilot study

A split biotin ligase approach reveals proteins associated with oligomeric alpha-synuclein during aggregation

Thermal and filler concentration modulation of charge transport mechanism and dielectric properties in high-entropy oxide (CoCrFeNiMn)3O4-acrylic polymer composite

Abstract This study investigates the dielectric properties of polymer composites comprising an acrylic matrix and high-entropy oxide (HEO) (CoCrFeNiMn) 3 O 4 filler. Single-phase HEO was synthesized via solid-state reaction followed by calcination at 850 °C (HEO-850). The research systematically examines the temperature- and concentration-dependent evolution of dielectric properties and charge transport mechanisms within these composite systems. Our findings reveal distinct correlations between filler concentration and dielectric performance enhancement. The unique properties of HEOs, stemming from their compositional complexity and structural characteristics, position these composite materials as promising candidates for advanced dielectric applications. The study elucidates fundamental parameters (temperature and filler concentration of HEO-850) governing the electrical behavior of HEO-polymer composites, providing insights into tailoring their properties for different technological applications.

The effect of evening vs. morning medication on morning blood pressure surge

Comparative analysis of ovine and human aortic valve tissue for bioprosthetic valve development using relaxation tests and numerical simulation

High school students in armed conflict-affected North Wollo, Ethiopia, struggle with lived experiences of depression and academic challenges

A weighted gene co-expression network analysis characterises the common defence responses of Eucalyptus to diverse biotic challenges

Spatiotemporal evolution and configurational pathways of synergistic green development in the Yangtze river economic belt

Zn-based metal organic frameworks encapsuated cauliflower leaves-derived biochar composite for photocatalytic removal of victoria blue and crystal violet

Relationship between adjustability of grasping force and upper limb/hand function in individuals with cerebrovascular disorders

Abstract Activities of daily living, including grasping, holding, and pinching, are essential for independence but often become difficult for individuals with cerebrovascular disease owing to upper limb paralysis. We investigated the relationship between adjustability of grasping force (AGF), defined as the ability to adjust grasping force, and upper limb/hand functional performance. Twelve individuals with mild cerebrovascular disorders participated in this preliminary cross-sectional study. Assessments included the AGF task using iWakka, the Fugl-Meyer Assessment, Simple Test for Evaluating Hand Function, Action Research Arm Test (ARAT), and Motor Activity Log (MAL). AGF was quantified using the AGF score, calculated as the absolute error between target and actual grasping force; lower AGF scores indicate better AGF. Partial Spearman’s rank correlations, adjusted for age, revealed that AGF score (isometric section) on the less-affected side was negatively and positively correlated with ARAT and MAL (Quality of Movement) on the less-affected and more-affected sides, respectively. These findings suggest that better AGF on the less-affected side may enhance motor performance on that side but could be associated with lower perceived movement quality on the more-affected side. This highlights the complex role of the less-affected side in post-stroke function and suggests that rehabilitation strategies should consider AGF on both sides of the body.

Validation of SocialBit as a smartwatch algorithm for social interaction detection in a clinical population

Abstract Social interaction supports brain health and recovery after neurological injury. Yet no validated tool exists for real-time measurement in individuals with and without neurological deficits. We developed SocialBit, a lightweight, privacy-preserving machine learning algorithm that detects social interactions using ambient audio features on a commercial smartwatch. In a prospective validation study, we evaluated SocialBit against livestream minute-by-minute human-coded ground truth in 153 hospitalized stroke patients who wore the device for up to 8 days, generating 88,918 min of observation. In these patients, the stroke severity and cognition spanned broad clinical ranges (NIH Stroke Scale 0–25; Montreal Cognitive Assessment 8–30), and 24 patients had aphasia across diverse subtypes, including severe presentations. SocialBit achieved high overall performance (sensitivity 0.87, specificity 0.88, area under the curve 0.94) and maintained accuracy in patients with language deficits (AUC 0.93). Despite lower temporal sampling, SocialBit produced interaction frequency distributions closely matching minute-by-minute human coding. Performance was robust across environments and interaction types. Of clinical relevance, SocialBit showed that patients with more severe strokes engaged in less social interaction, paralleling human-coded results. SocialBit is an accurate digital biomarker of social interaction with potential applications in remote monitoring and clinical trials.

Automated diagnosis of plus form and early stages of ROP using deep learning models

Abstract Retinopathy of Prematurity (ROP) represents a critical ophthalmological pathology affecting premature infants, with established associations to low birth weight (BW) and early gestational age (GA). Elevated risk of severe ROP, which can result in irreversible vision loss, is observed in infants exhibiting lower BW and GA. This research investigates the development of an automated diagnostic system designed to classify Plus disease, a marker of abnormal retinal vascularity, and ROP staging, a determinant of disease progression. Specifically, the model facilitates binary classification of Plus disease (Plus/Normal) and multi-class classification of ROP stage (Stage 0, 1, 2, 3) using a meticulously curated dataset of retinal fundus images. The proposed model demonstrates high diagnostic accuracy, achieving 0.996 for Plus disease detection and 0.98 for ROP stage classification. These results suggest potential clinical utility for automated ROP screening methodologies in supporting timely diagnosis and intervention in similar settings, pending multi-center validation, which could help reduce the incidence of vision impairment in preterm populations.

Coumarin-1,2,4-Triazole hybrids as potential agents against Brassicogethes aeneus (Fabricius 1775)

Exploring the toxic mechanism of cypermethrin-induced neurodegeneration diseases via network toxicology and molecular docking

Multi relational dual attention graph transformer for fine grained sentiment analysis

Correction: A multi-perspective interpretative phenomenological analysis of patient and bed partner experiences with CPAP therapy adaptation in obstructive sleep apnoea

Interpretable machine-learning risk prediction of unplanned extubation among cancer patients with peripherally inserted central catheters

Tailoring combined impact loading using gradient foam composite projectiles with variable fragment shapes

Esculin improves wound healing in diabetic rats by modulating extracellular matrix remodeling and molecular pathways

Sexual and reproductive health consequences of COVID-19 pandemic in Nigeria: an infodemiological survey