Browse Articles

Aerobic exercise-induced irisin secretion is associated with improved endothelial function and reduced atherosclerosis in ApoE-deficient mice

Sex and BMI as predictors of pill residue in dysphagia: a multivariate analysis

Abstract We aimed to explore the specific challenges encountered by individuals with dysphagia when taking oral medications, focusing on the types of dysphagia that impede pill swallowing and the relationship between body mass index (BMI) and pill residue. We retrospectively reviewed 70 patients who underwent videofluoroscopic swallowing studies at the Department of Dysphagia Rehabilitation, Tokyo Medical and Dental University Dental Hospital between May 2013 and March 2020. Patients were assessed for pill residue in various anatomical locations, including the mouth, epiglottic vallecula, and piriform sinus. Patient demographics, BMI, functional oral intake scale scores, and clinical histories were collected. Pill residue was most commonly observed in the epiglottic vallecula (17% of patients), followed by the piriform sinus (10%) and mouth (9%). Males were more likely to have tablet residue in the epiglottic vallecula ( p  = 0.047), and a lower BMI was associated with increased pill residue in the piriform sinus ( p  = 0.025). Multivariate analysis identified sex as a significant predictor of epiglottic pill residue ( p  = 0.031), whereas a lower BMI was associated with pill residue in the piriform sinus ( p  = 0.016). Sex and BMI significantly influenced pill residue in patients with dysphagia; males and individuals with a lower BMI (< 18.5 kg/m 2 ) were at higher risk.

Innovative Aboodh-based gractional analytical methods for nonlinear Burgers’ partial differential equations

Machine learning framework for multidimensional assessment of urban quality of life

Thermo-mechanical modelling and experimental production of aluminium circular form detail using three-level hierarchical WAAM model

Force sensorless interaction wrench estimation for neural-learning impedance control of a flying parallel robot with actuator saturation

Statin use and the risk of liver-related events in older adults with steatotic liver disease

Effects of a Pringle maneuver on jejunal mucosal oxygenation and blood flow in a porcine model

Dielectric function of layered GaSe0.8Te0.2 and emergent all van der Waals optical elements

Clinical relevance of circulating blood microaggregates and reactivation of Epstein Barr Virus in long-term Post-CoVID syndrome patients

Immunotolerant Oligomer scaffolds promote regenerative remodeling and improved muscle structure and function after volumetric muscle loss

Ramadan fasting and adverse outcomes in cirrhosis: primary risk estimates with associated independent predictors

GV1001 reduces pathological 4R tau and functional deficits in models relevant to progressive supranuclear palsy

Abstract GV1001, a peptide drug derived from human telomerase reverse transcriptase, has showed the therapeutic effect in the animal model of Alzheimer’s disease (AD), a representative chronic neurodegenerative disease having impaired learning and memory. In our previous studies, GV1001 has showed the multi-functions including anti-apoptosis, anti-oxidative stress, and anti-neuroinflammation in AD-related in vitro and in vivo systems. Here, in addition to these previously reported functions, GV1001 was discovered to reduce the protein level of 4R tau isoform in the pathological condition. There is no studies providing the potential of GV1001 as a therapeutic drug for neurodegenerative movement disorders. Progressive supranuclear palsy (PSP) is a rare atypical Parkinsonism in the midbrain region, leading to more severe motor symptoms and very rapid pathological progression. Increased 4R tau isoform in an affected brain region of the primary 4R tauopathy is a distinct pathological character in PSP patients. In this study, GV1001 down-regulated the protein level of 4R tau specifically in an annonacin-induced PSP in vitro neuronal model as well as in vivo study using 4R TauP301L-BiFC mouse model. These findings suggest a novel role of GV1001 in 4R tauopathy and support its disease-modifying potential within the context of 4R tau–driven neurodegenerative models, including PSP.

PGE2 regulates ferroptosis and osteogenesis of MC3T3-E1 cells via NOS2

Dynamic background motion object semantic segmentation algorithm based on generative adversarial network and transformer collaboration

Optimizing injector nozzle configuration for high efficiency and low emissions in diesel engines fueled with biodiesel and n-butanol blends

An enhanced connected banking system optimizer with multiple strategies for numerical optimization problems

The COVID-19 pandemic could worsen the psychological well-being of people with disabilities in Cambodia

The evolution of fatigue in remote tower controllers: evidence from eye-tracking analysis

An automated decision making framework for modern vehicles CO2 emissions using multi modal engine telemetry and feature interpretability

Abstract Accurate prediction of vehicle CO₂ emissions is challenging due to heterogeneous engine characteristics, nonlinear interactions among fuel, mechanical, and operational parameters, and variable driving conditions. This study proposes a high-performance machine learning framework that combines multi-layer perceptron (MLP) architectures with nature-inspired metaheuristic optimization to model vehicle-induced CO₂ emissions with improved precision and convergence stability. The framework leverages multi-modal engine telemetry—including fuel type, transmission, engine displacement, consumption metrics, and cylinder profiles—alongside advanced feature selection and interpretability techniques such as Recursive Feature Elimination (RFE), SHAP analysis, and Class Activation Mapping (CAM) to identify dominant emission drivers. Two metaheuristic optimizers, Horned Lizard Optimization Algorithm (HLOA) and Giant Armadillo Optimization (GAO), are applied for hyperparameter tuning, with the GAO-enhanced MLP achieving superior predictive performance (R² = 0.9881; RMSE = 6.478). The study highlights the integration of interpretable AI models into vehicle emission prediction, demonstrating their potential to inform low-carbon vehicle design, data-driven urban mobility planning, and environmentally conscious policy-making.