ArticleInternational journal of molecular sciences2026
Unsupervised Machine-Learning-Based Endotype Discovery Using Iterative Resampling in Dupilumab-Treated Patients.
Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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11 authors.
Funding
Abstract
Asthma is a heterogeneous inflammatory disorder involving multiple immune pathways, frequently presenting alongside comorbidities such as chronic rhinosinusitis with nasal polyps (CRSwNP). Although biologic therapies such as dupilumab have shown clinical efficacy, the molecular mechanisms underlying variable treatment responses remain poorly understood. This study aimed to characterize transcriptomic patterns that distinguish asthmatic patients from healthy controls and to evaluate transcriptomic changes induced by dupilumab. Whole-blood RNA-seq was performed in 66 samples, 18 patients (G0) with severe asthma before and after 6 months of dupilumab treatment compared with 30 non-asthmatic controls. Differentially expressed genes (DEGs) were identified and validated by quantitative PCR (qPCR). Clinical responses were assessed using the FEV1, Exacerbations, Oral corticosteroids, Symptoms (FEOS) score and the Sino-Nasal Outcome Test-22 (SNOT-22). A total of 1124 DEGs were identified, distinguishing asthmatic patients from controls. Notably,
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