Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 27, 2026

LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium
06:16

LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium

Published on: July 28, 2023

Unsupervised Machine-Learning-Based Endotype Discovery Using Iterative Resampling in Dupilumab-Treated Patients.

Emma Moreno-Jiménez1,2, Natalia Morgado1,2, Asunción García-Sánchez2,3,4

  • 1Departamento de Microbiología y Genetica, Universidad de Salamanca, 37007 Salamanca, Spain.

International Journal of Molecular Sciences
|June 26, 2026
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Decalogue of the Declaration of Salamanca: Advancing Undergraduate Education in Allergy and Clinical Immunology Across Europe.

Allergy·2026
Same author

Real-world retrospective study on allergy screening and its effect on specialist referral patterns.

Frontiers in allergy·2026
Same author

Estimation of the indirect costs associated with chronic rhinosinusitis with nasal polyposis in Spain: an analysis from a societal perspective.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery·2026
Same author

Clinical importance of the 5-Step Precision Medicine (5SPM) method in shared decision-making to choose the antipsychotic treatment for a patient with first-episode psychosis: a case report.

BMC psychiatry·2026
Same author

Growth Differentiation Factor 15 as a Biomarker of Cardiovascular Burden and Mortality in a Population-Based Cohort.

International journal of molecular sciences·2026
Same author

Olfactory Screening Tools Compared-Further Results From the BOT-8.

International forum of allergy & rhinology·2026

This study reveals two distinct patient subgroups in severe asthma, showing different molecular responses to dupilumab treatment. These findings support personalized medicine approaches for asthma patients receiving biologic therapies.

Area of Science:

  • Immunology
  • Genomics
  • Precision Medicine

Background:

  • Asthma is a complex inflammatory condition often co-occurring with chronic rhinosinusitis with nasal polyps (CRSwNP).
  • Biologic therapies like dupilumab are effective, but individual responses vary due to poorly understood molecular mechanisms.

Purpose of the Study:

  • To identify transcriptomic patterns differentiating asthma patients from controls.
  • To analyze molecular changes induced by dupilumab treatment in severe asthma.
  • To explore distinct patient endotypes and predict treatment response.

Main Methods:

  • Whole-blood RNA sequencing (RNA-seq) on 66 samples from severe asthma patients and controls.
  • Differential gene expression analysis and quantitative PCR (qPCR) validation.
Keywords:
CRSwNPdupilumabendotypesprecision medicinesevere asthmasuper-responsetranscriptomics

More Related Videos

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

Related Experiment Videos

Last Updated: Jun 27, 2026

LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium
06:16

LipidUNet-Machine Learning-Based Method of Characterization and Quantification of Lipid Deposits Using iPSC-Derived Retinal Pigment Epithelium

Published on: July 28, 2023

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

  • Unsupervised machine learning to identify patient subgroups and treatment response patterns.

    • Main Results:

    • Identified 1124 differentially expressed genes (DEGs) between asthmatic patients and controls.
    • Discovered two distinct patient subgroups (G1, G2) with divergent transcriptomic responses to dupilumab.
    • Found baseline DIXDC1 expression predicts non-response in CRSwNP patients.

    Conclusions:

    • Unsupervised machine learning on transcriptomic data reveals two distinct asthma endotypes with differential dupilumab response.
    • This highlights the potential for a precision medicine strategy in severe asthma management.
    • Identified key genes and biomarkers for disease characterization and treatment prediction.