Browse Articles

Caffeine-augmented exercise as a pretreatment for locomotor and balance impairments induced by REM sleep deprivation in rats

Molecular spin sensor for in-situ monitoring of crystallization behavior and phase transition in aromatic materials

Virulence and genome analysis of baculovirus isolates from different Lymantria dispar populations

Histone lactylation regulates DOCK4 to control heat nociception and supports Dynein-mediated Nav1.7 trafficking

Science populism impacts perceptions of credibility across scientific professions

The spatial landscape of glial pathology and T cell response in Parkinson’s disease substantia nigra

A novel passive shimming optimization method of MRI magnet based on a PSA-SQP hybrid algorithm

Temporo-spatial cellular atlas of the regenerating alveolar niche in idiopathic pulmonary fibrosis

Abstract Healthy alveolar repair relies on the ability of alveolar stem cells to differentiate into specialized epithelial cells for gas exchange. In chronic fibrotic lung diseases such as idiopathic pulmonary fibrosis (IPF), this regenerative process is abnormal but the underlying mechanisms remain unclear. Here, using human lung tissue that represents different stages of disease and a 33-plex single-cell imaging mass cytometry (IMC), we present a high-resolution, temporo-spatial cell atlas of the regenerating alveolar niche. With unbiased mathematical methods which quantify statistically enriched interactions, CD206himacrophage subtype and an alveolar basal intermediate epithelial cell emerge as the most statistically robust spatial association in the epithelial and immune cell interactome, found across all stages of disease. Spatially resolved receptor–ligand analysis further offers an in silico mechanism by which these macrophages may influence epithelial regeneration. These findings provide a foundational step toward understanding immune–epithelial dynamics in aberrant alveolar regeneration in IPF.

Firefly algorithm with multiple learning ability based on gender difference

Trifunctional local-range order oxygen structure enhanced strength-ductility and fatigue resistance in large-scale metastable titanium alloy

Comparable efficacy of generic and original alginate for symptom control in PPI-refractory GERD

Abstract Gastroesophageal reflux disease (GERD) is a prevalent global condition, affecting 18.1–27.8% of North Americans and 6.3–18.3% of the Thai population. While proton pump inhibitors (PPIs) are the first-line treatment, only about one-third of patients achieve adequate symptom control. Alginate-based medications in combination with PPIs have shown promise, but the comparative effectiveness of generic versus original alginates remains unexplored. To compare the effectiveness of generic alginate (ONE GERD) versus original alginate (Gaviscon Dual Action Suspension) in combination with PPIs for treating GERD symptoms in patients who failed standard PPI therapy.This multicenter prospective randomized controlled non-inferiority trial included 48 patients who failed standard-dose PPI treatment. Patients were randomized to receive either generic or original alginate four times daily for 28 days. Treatment response was evaluated using the Reflux Disease Questionnaire (RDQ) at days 7 and 28. At day 7, both groups showed identical response rates of 45.83%. By day 28, response rates increased to 54.17% for generic alginate and 70.83% for original alginate (p = 0.23). Total RDQ scores and symptom-free rates showed no significant differences between groups at both time points. Adverse event rates were comparable (16.67% vs. 8.33%, p = 0.66). Analysis of specific symptoms (heartburn, chest pain, and regurgitation) revealed similar improvements in both groups throughout the study period. This study provides evidence supporting the therapeutic equivalence of the generic alginate (ONE GERD) to the original formulation (Gaviscon Dual Action Suspension) in treating symptoms for patients with GERD who have failed PPI therapy. Crucially, the comparable efficacy and safety, coupled with the inherent lower cost of generic medications, suggest significant economic benefits and the potential for wider patient access to effective GERD management. This makes generic alginate a viable and attractive alternative in clinical practice, particularly in resource-limited settings or for patients facing financial constraints, thereby contributing to more equitable healthcare solutions without compromising therapeutic outcomes.

Virus-human chromatin interactions reorganise 3D genome and hijack KDM5B for promoting metastasis in nasopharyngeal carcinoma

Hyperbaric oxygen therapy for long COVID: a prospective registry

A supramolecular approach to improve the performance and operational stability of all-perovskite tandem solar cells

Development of molecular diagnostic methods to distinguish acerola species for quality assurance of food, dietary supplements and natural health products

Embracing nonlinearity and geometry: a dimensional analysis guided design of shock absorbing materials

Slip and tractive efficiency of an electric tractor with a 4WID E-axle system

Molecular manipulation of polyamide nanostructures reconciles the permeance-selectivity threshold for precise ion separation

Investigation of the temperature influence on the catalytic hydrogenation upgrading of bio-oil using industrial nickel based catalyst RZ409

A multimodal dataset for precision oncology in head and neck cancer

Abstract Head and neck cancer is a common disease and is associated with a poor prognosis. A promising approach to improving patient outcomes is personalized treatment, which uses information from a variety of modalities. However, only little progress has been made due to the lack of large public datasets. We present a multimodal dataset, HANCOCK, that comprises monocentric, real-world data of 763 head and neck cancer patients. Our dataset contains demographical, pathological, and blood data as well as surgery reports and histologic images, that can be explored in a low-dimensional representation. We can show that combining these modalities using machine learning is superior to a single modality and the integration of imaging data using foundation models helps in endpoint prediction. We believe that HANCOCK will not only open new insights into head and neck cancer pathology but also serve as a major source for researching multimodal machine-learning methodologies in precision oncology.