Evidence map›Paper›PMID 42521813›Full record

ReviewMikrochimica acta2026

Metal-organic frameworks for breath-based sensors: a review.

Iliya Ghayour, Hamide Ehtesabi

Abstract readReview
PubMed Publisher
In one paragraph

Review in Mikrochimica acta, 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

2 authors.

Iliya GhayourFaculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran.
Hamide EhtesabiFaculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran. h_ehtesabi@sbu.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid detection and handling of diseases continue to be a priority, improving patient care and individualized treatment. Expensive equipment, the need for expert operators, and extended analytical processes often limit traditional diagnostic methods. Sensor-based approaches for efficient exhaled breath analysis offer a favorable non-invasive alternative. Patients can easily provide exhaled breath, which serves as a nearly inexhaustible diagnostic sample with minimal discomfort. "Breathomics" is an emerging research realm that deeply investigates the metabolomic constitution of exhaled breath, offering information about several (patho)physiological processes. Breathomics reveals unusual products of metabolism by analyzing volatile organic compounds (VOCs) in exhaled breath. Incorporating artificial intelligence (AI), machine learning (ML), and the Internet of Things into breathomics can improve the identification of patterns in breath biomarkers, data analysis, and real-time disease diagnosis. Metal-organic frameworks (MOFs) are promising materials for the production of high-performance gas sensors. They possess porous architectures, substantial surface areas, unique compositions, and functional linkers. The implementation of MOFs as gas-sensitive elements may alter their physical and/or chemical properties. This effect is noticeable during the adsorption and desorption of gases. This review discusses emerging advancements in precise breath-based disease diagnosis using MOFs in various technologies, including electrochemical, chemiresistive, quartz crystal microbalance, and surface-enhanced Raman spectroscopy systems.

Indexed as

Biosensing TechniquesMetal-Organic FrameworksVolatile Organic CompoundsBreath TestsElectrochemical TechniquesHumansMetal-Organic FrameworksVolatile Organic CompoundsBreath analysisDiabetesInfectionLung cancerMOFsVOCs

Identifiers

What OpenQuestion holds

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Registered trials

None linked

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.