Evidence map›Paper›PMID 41953764›Full record

ArticleERJ open research2026

Multidrug-resistant

Fransiskus Xaverius Ivan, Ali Versi, Pei Yee Tiew, Mahmoud I Abdel-Aziz, Nazanin Zounemat Kermani, Anke H Maitland-Van Der Zee, Peter Howarth, Mariko Siyue Koh, Ian M Adcock, Sanjay H Chotirmall and 1 more

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Article in ERJ open research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Fransiskus Xaverius IvanLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Ali VersiNational Heart and Lung Institute, and Data Science Institute, Imperial College London, London, UK.
Pei Yee TiewLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
Mahmoud I Abdel-AzizDepartment of Pulmonary Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.ORCID https://orcid.org/0000-0001-9263-7137
Nazanin Zounemat KermaniNational Heart and Lung Institute, and Data Science Institute, Imperial College London, London, UK.
Anke H Maitland-Van Der ZeeDepartment of Pulmonary Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.
Peter HowarthRespiratory Department, Faculty of Medicine, Southampton University, Southampton, UK.ORCID https://orcid.org/0000-0003-0619-7927
Mariko Siyue KohDepartment of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore, Singapore.ORCID https://orcid.org/0000-0002-1230-0129
Ian M AdcockNational Heart and Lung Institute, and Data Science Institute, Imperial College London, London, UK.ORCID https://orcid.org/0000-0003-2101-8843
Sanjay H ChotirmallLee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.ORCID https://orcid.org/0000-0003-0417-7607
Kian Fan ChungNational Heart and Lung Institute, and Data Science Institute, Imperial College London, London, UK.ORCID https://orcid.org/0000-0001-7101-1426

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Severe asthma encompasses heterogeneous inflammatory phenotypes and airway bacteriome diversity but the state of its airway resistome remains understudied. We therefore evaluated the link between the airway microbiome and the antibiotic-resistant genes by determining the clusters from a bacteriome-resistome integration from sputum samples of patients with severe asthma. Methods: Induced sputum samples from severe asthma (SA; n=96), mild-moderate asthma (MMA; n=23) and healthy controls (HCs; n=23) in the European U-BIOPRED asthma cohort were metagenomically sequenced. Respiratory bacteriome was evaluated by taxonomical and functional classification. The comprehensive antibiotic resistance database was used to determine airway resistome and Similarity Network Fusion to cluster integratively the bacteriome-resistome. Results: More multidrug-resistance genes were present in SA compared with MMA and HCs with the Conclusion: The demonstration of U-BIOPRED multiresistant

Identifiers

PMID41953764
PMCPMC13054712

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.