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Sensors are transforming the world — work together to maximize their benefits

A hidden route of exposure: adsorption of endocrine disrupting compounds and chemicals of emerging concern on tire rubber

Street-dog policy in India is barking up the wrong tree

A tri-omics and machine learning framework identifies prognostic biomarkers and metabolic signatures in sepsis

Quantum spinning effect observed in a levitating magnet

Using physical model test and numerical simulation for revealing the mechanism of stope collapse: a case study

Educational Strategies to Prepare Trainees for Clinical Uncertainty

Genetic predisposition to elevated total immunoglobulin E levels defines a distinct adult-onset-predominant asthma phenotype

Abstract Asthma heterogeneity remains a major barrier in precision medicine. Although elevated total serum immunoglobulin E (IgE) is a hallmark of asthma, even in nonatopic patients, its causal role in asthma pathogenesis is debated. We hypothesized that genetic predisposition to increased IgE defines a distinct asthma endotype. A genome-wide association study of total serum IgE in 1,287 non-asthmatic Japanese adults was used to construct IgE polygenic risk scores (IgE_PRS). Applying IgE_PRS to 745 patients with asthma, we performed cluster analysis incorporating age at onset, total IgE levels, IgE_PRS, and percent predicted forced expiratory volume in 1 s (pFEV 1 ), identifying four distinct adult asthma phenotypes. Notably, one cluster had the highest IgE_PRS and adult-onset-predominant type 2 inflammation. Conversely, the second cluster displayed the highest IgE levels but average IgE_PRS. The remaining two clusters comprised patients with lower IgE_PRS. One cluster was characterized by eosinophilia and smoking-related airflow limitation, whereas the other exhibited a type 2 low phenotype. In a 10-year retrospective cohort, over 30% of newly diagnosed asthma cases fell into the genetically predisposed high-IgE_PRS cluster. These findings reveal a distinct adult-onset-predominant asthma phenotype driven by genetically determined IgE production, offering new avenues for endotype-driven diagnosis and personalized therapy.

A Naturally Occurring Gain-of-Function Mutation in Factor VIII

As we breach 1.5 °C, we must replace temperature limits with clean-energy targets

Epidemiology and prevention of HPV-driven cervical cancer in urban–rural China: a population-based study of 60,280 women

Popeye Sign in Transthyretin Amyloidosis

Exceeding 1.5 °C requires rethinking accountability in climate policy

Cancer classification with radiomics in controlled preclinical models

Abstract The premise of radiomics involves extracting high-dimensional quantitative features from medical images to aid clinical decision-making. While radiomics has shown promise in predicting disease characteristics, concerns regarding confounders, reproducibility, and interpretability limit its clinical adoption. In this study, we assessed the ability of radiomic features extracted from contoured CT images to classify two distinct tumour models, CT26 colorectal cancer (CRC) and 4T1 breast cancer (BC), in a highly controlled murine setting. We aimed to provide compelling data for the role of radiomics as a reliable cancer biomarker. We benchmarked radiomics-based classification against previously established blood-based biomarkers, including leukocyte populations and plasma proteins. Feature filtering reduced the original 1409 radiomic features to 18 non-redundant, high-importance predictors, primarily texture-based transformations. Unsupervised clustering via UMAP revealed that radiomics-based features did not segregate tumour types as effectively as blood biomarkers, suggesting potential confounding factors. Supervised machine learning using Random Forest showed that radiomic features achieved a classification accuracy of 0.87, lower than the 0.96 and 0.99 accuracies obtained with cell and plasma biomarkers, respectively. Furthermore, integrating radiomics with blood biomarkers did not enhance classification performance, and feature importance analysis using SHAP identified blood-based markers as the dominant predictors. These findings suggest that while radiomics retains some predictive value, it is less effective than blood biomarkers in this classification task and does not significantly contribute to multimodal tumour classification models. Our study underscores the need for further standardization and validation of radiomics before its clinical implementation.

Peripheral Artery Disease in the Legs

Dynamics of brain connectivity across the Alzheimer’s disease spectrum through magnetoencephalography

The Essential Role of States in Protecting Immunization Access

Technology is changing how we write — and how we think about writing

TIPE2 serves as a favorable prognostic biomarker and suppresses cholangiocarcinoma progression by targeting RAC1-mediated integrin αvβ6 trafficking

Toxic Erythema of Chemotherapy