Evidence map›Paper›PMID 42137964›Full record

ArticleMultidisciplinary respiratory medicine2026

Electronic nose technology in bronchial asthma diagnosis and management.

Lushuo Zhang, Mengnan Guo, Hui Wang, Anna Dong, Ning Song, Binghan Zhong, Xiaying Li, Chenqi Liu, Yunxia Zhao

Abstract read
In one paragraph

Article in Multidisciplinary respiratory medicine, 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

9 authors.

Lushuo ZhangDepartment of Respiratory, Hebei Medical University Third Hospital.
Mengnan GuoDepartment of Respiratory, Hebei Medical University Third Hospital.
Hui WangDepartment of Respiratory, Hebei Medical University Third Hospital.
Anna DongDepartment of Respiratory, Hebei Medical University Third Hospital.
Ning SongDepartment of Respiratory, Hebei Medical University Third Hospital.
Binghan ZhongDepartment of Respiratory, Hebei Medical University Third Hospital.
Xiaying LiDepartment of Respiratory, Hebei Medical University Third Hospital.
Chenqi LiuDepartment of Respiratory, Hebei Medical University Third Hospital.
Yunxia ZhaoDepartment of Respiratory, Hebei Medical University Third Hospital.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic nose (e-nose) technology, as an emerging non-invasive diagnostic tool, has demonstrated considerable potential in the diagnosis and management of bronchial asthma. By mimicking the human olfactory system, this technology utilizes gas sensor arrays combined with pattern recognition algorithms to enable rapid analysis and identification of volatile organic compounds (VOCs) in the exhaled breath of patients with asthma. Accumulating evidence suggests that e-nose systems can discriminate between asthma patients and healthy individuals with relatively high accuracy, and they have also shown promising performance in asthma phenotyping. Compared with conventional pulmonary function tests, e-nose technology offers distinct advantages, including cost-effectiveness, rapid response, and non-invasiveness. These features make it particularly suitable for populations requiring repeated dynamic assessment or those unable to perform high-quality pulmonary function testing. However, current evidence is primarily derived from exploratory studies, and heterogeneity exists across studies in terms of population characteristics and methodologies. This highlights the need for further standardized research to improve the consistency and reproducibility of findings.

Indexed as

asthma,electronic-nose,volatile organic compounds

Identifiers

PMID42137964
PMCPMC13313609

What OpenQuestion holds

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Registered trials

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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.