Evidence map›Paper›PMID 41963583›Full record

ArticleScientific reports2026

Rapid detection of airborne fungal contamination using a molecularly imprinted polymer approach for ergosterol.

Eun-Sook Choi, Jung-Hee Kim, Yun-Cheol Na, Bong Gu Lee, Min-Kyeong Yeo, Eunjoo Kim

Abstract read
In one paragraph

Article in Scientific reports, 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
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1 · What the graph read from it

What it found

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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.

2 · The registry

The trial behind it

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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

6 authors.

Eun-Sook ChoiDivision of ABB Research, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Techno-jungangdaero 333, Dalsung-gun, Daegu, 42988, Republic of Korea.
Jung-Hee KimDivision of ABB Research, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Techno-jungangdaero 333, Dalsung-gun, Daegu, 42988, Republic of Korea.
Yun-Cheol NaMetropolitan Seoul Center, Korea Basic Science Institute (KBSI), Seoul, 03759, Republic of Korea.
Bong Gu LeeDepartment of Environmental Science and Engineering, Kyung Hee University, Yougin, 44670, Republic of Korea.
Min-Kyeong YeoDepartment of Environmental Science and Engineering, Kyung Hee University, Yougin, 44670, Republic of Korea.
Eunjoo KimDivision of ABB Research, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Techno-jungangdaero 333, Dalsung-gun, Daegu, 42988, Republic of Korea. ejkim@dgist.ac.kr.

Funding

Ministry of Environment, Republic of Korea No. 2021003370004Ministry of Science and ICT, Republic of Korea DGIST Basic Program, Project No. 25-IT-03
6 · The paper itself

Abstract

Fungi are major biological contaminants in indoor air, and their concentration is typically assessed using the culture-based CFU method, which is labor-intensive and time-consuming. Ergosterol, a major fungal cell membrane component, has emerged as a preferred target for alternative analytical approaches. However, ergosterol is highly hydrophobic, and specific affinity probes such as antibodies or aptamers have not yet been successfully developed. In this study, we fabricated ergosterol-specific probes using molecularly imprinted polymers (MIPs) immobilized on carbon nanotubes (CNTs) and integrated them into a screen-printed electrode (SPE) platform. Surface polymerization was initiated through a thiol-ene click reaction using pentaerythritol tetrakis(3-mercaptopropionate) (PETMP) and glyoxal bis(diallyl acetal) (GO), which were selected based on predicted stable conformations for MIP synthesis. The resulting MIP@CNT sensor achieved an imprinting factor (IF) of 19.26 and a limit of detection (LOD) of 0.22 pM for ergosterol. Ergosterol levels in indoor air samples collected on PVC filters were quantified using the MIP@CNT sensor and showed significant correlation with GC/MS measurement (R

Indexed as

Air MicrobiologyAir Pollution, IndoorErgosterolFungiMolecular ImprintingMolecularly Imprinted PolymersLimit of DetectionNanotubes, CarbonPolymersErgosterolMolecularly Imprinted PolymersNanotubes, CarbonPolymersErgosterolFungiIndoor airMolecularly imprinted polymerScreen-printed electrode

Identifiers

PMID41963583
PMCPMC13230549

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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.