Evidence map›Paper›PMID 40911666›Full record

ArticleScience advances2025

Machine learning- and multilayer molecular network-assisted screening hunts fentanyl compounds.

Changzhi Shi, Wanli Li, Yang Wang, Xi Chen, Meixiang Yu, Hai Zhang, Zecang You, Maoyong Song, Xiaojun Deng, Mingliang Fang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

10 authors.

Changzhi ShiShanghai Institute for Doping Analyses, Shanghai University of Sport, Shanghai 200438, China.ORCID 0000-0002-0591-2442
Wanli LiShanghai Institute for Doping Analyses, Shanghai University of Sport, Shanghai 200438, China.ORCID 0009-0000-6070-5262
Yang WangShanghai Institute for Doping Analyses, Shanghai University of Sport, Shanghai 200438, China.ORCID 0000-0002-8668-0646
Xi ChenShanghai Institute for Doping Analyses, Shanghai University of Sport, Shanghai 200438, China.ORCID 0009-0008-4343-2686
Meixiang YuShanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China.
Hai ZhangShanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China.ORCID 0000-0002-9177-3855
Zecang YouShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science & Engineering, Fudan University, Shanghai 200443, China.
Maoyong SongKey Laboratory of Environmental Nanotechnology and Health Effects, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Xiaojun DengShanghai Institute for Doping Analyses, Shanghai University of Sport, Shanghai 200438, China.ORCID 0000-0002-1051-2507
Mingliang FangShanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science & Engineering, Fudan University, Shanghai 200443, China.ORCID 0000-0002-2204-9783

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fentanyl and its analogs are a global concern, making their accurate identification essential for public health. Here, we introduce Fentanyl-Hunter, a screening platform that uses a machine learning classifier and multilayer molecular network to select and annotate fentanyl compounds using mass spectrometry (MS). Our classification model, based on 772 fentanyl spectra and spectral binning feature engineering, achieved an

Indexed as

FentanylMachine LearningHumansMass SpectrometryFentanyl

Identifiers

PMID40911666
PMCPMC12412648

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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 Socratic graph.