Atopy

How to quickly determine whether patients with chronic cough need corticosteroid treatment--construction of a predictive model for corticosteroid-responsive cough in chronic cough.

Patients with chronic cough need to undergo a wide range of tests and rely on empirical medication to determine the underlying cause. Corticosteroid-responsive cough (CRC) accounts for the majority of the causes of chronic cough, and the diagnostic process is relatively complicated. To develop a predictive model for diagnosis of CRC, which is based on readily available clinical information. A retr...

AI-based prediction of aspirin-exacerbated respiratory disease using nasal epithelial mRNA expression profiles.

BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) is a distinct asthma endotype marked by asthma, nasal polyposis, and respiratory reactions to COX-1 inhibitors. Early and accurate identification of AERD remains clinically challenging. OBJECTIVE: We sought to develop and externally validate an artificial intelligence (AI)-based diagnostic model that uses nasal epithelial mRNA expression p...

On-device cough detection and respiratory disease classification enhanced by generative data augmentation.

BACKGROUND: Cough sounds are accessible, non-invasive biomarkers for respiratory disease assessment and can be captured using consumer-grade smartphon...

Targeting Type 2 and Non-type 2 Asthma: Emerging Biologics and Personalized Strategies.

PURPOSE OF REVIEW: Asthma continues to pose a serious global health issue affecting billions of people and causing significant morbidity. It is immuno...

AI-Based Quantitative Nasal Cytology for Predicting Eosinophilic Chronic Rhinosinusitis With Nasal Polyps: A Pilot Study.

AI for predicting exacerbations in KIDs with asthma (AIRE-KIDS).

Recurrent acute care visits are a common yet preventable outcome for many children with asthma. Machine learning (ML) applied to electronic medical re...

Combining bioinformatics and machine learning to analyze and validate sepsis-related cell senescence genes and potential drugs.

Sepsis is a life-threatening organ malfunction induced by the host's abnormal reaction to infection. Sepsis can induce cellular senescence, thereby ex...

Artificial intelligence-based models in predicting acute exacerbations and diagnosing pediatric asthma: a systematic review and meta-analysis.

PURPOSE: This systematic review and meta-analysis evaluated the performance of artificial intelligence (AI)-based models in diagnosing pediatric asthm...

Machine learning-driven drug repurposing for GPR17: activity prediction via graph neural networks and multistage computational validation.

To identify novel GPR17-targeting ligands with potential relevance to multiple sclerosis (MS) therapy, we developed an integrated computational workfl...

The evolution of scientific knowledge in childhood asthma over time.

Paediatric asthma management has undergone a significant transformation from rudimentary assessments in the early 20th century to sophisticated diagno...

Current understanding and future directions in severe asthma through artificial intelligence-integrated multi-omic approaches.

Severe asthma remains a challenging, heterogeneous condition, despite significant advances in therapeutic strategies. A subset of patients continues t...

Tobacco rattle virus triggers local defense and systemically coordinates taxoid biosynthesis in Taxus baccata revealed by integrated metabolic analysis.

The clinical demand for paclitaxel necessitates novel strategies to enhance its production. While recent breakthroughs have elucidated the complete bi...

Machine learning prediction of surgical approach and clinical heterogeneity in cervical spondylotic myelopathy.

BACKGROUND: Preoperative planning of the surgical approach for cervical spondylotic myelopathy (CSM) is central to precision medicine. This study used...

Air pollution-related health impacts from domestic waste burning and associated interventions: the merits of a traditional versus machine learning scoping review methodology.

Regularly updating repositories of air pollution impacts on health is essential for evidence-based policies, interventions, and progress monitoring. H...

Few-shot learning-driven discovery of Lutein suppresses Th1-mediated inflammation via glucose metabolism.

The discovery of selective immunosuppressants for T cell-mediated diseases like Ulcerative Colitis (UC) is a significant challenge. While traditional ...

Predictors of the Efficacy of Focused Ultrasound for Moderate-to-Severe Persistent Allergic Rhinitis: A Retrospective Cohort Study.

BACKGROUND: Focused ultrasound (FUS) has achieved favorable results in the treatment of allergic rhinitis (AR). However, some patients still have poor...

Identification of diagnostic senescence-associated marker genes in asthma based on machine learning and experimental validation.

BACKGROUND AND AIMS: Asthma imposes a significant global health burden; however, the mechanistic role of cellular senescence in its pathogenesis remai...

Developing and validating an electronic health record-embedded AI model for managing multimorbid hospitalisation risk in patients with chronic RESpiratory disease (AiRES): a study protocol.

BACKGROUND: Chronic respiratory diseases (CRDs), such as asthma and chronic obstructive pulmonary disease (COPD), are heterogeneous conditions with a ...

Canadian radiology: 2026 update.

Canadian radiology continues to produce scholarship that is technically sophisticated, clinically relevant, and increasingly attentive to the wider sy...

A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).

The integration of artificial intelligence (AI) into clinical decision support (CDS) holds promise for proactive, personalized, and precision care. Ho...