Evidence map›Paper›PMID 41425322›Full record

ArticleACS measurement science au2025

Non-Targeted Analysis Workflow of Endocrine-Disrupting Chemicals in Ovarian Follicular Fluid: Identification of Parabens by Diagnostic Fragmentation Evidence and Additional Contaminants via Mass Spectral Library Matching.

Zilin Zhou, Michael S Neal, Warren G Foster, Yong-Lai Feng

Abstract read
In one paragraph

Article in ACS measurement science au, 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

4 authors.

Zilin ZhouExposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON K1A 0K9, Canada.ORCID https://orcid.org/0000-0002-8310-9401
Michael S NealONE Fertility, Burlington, ON L7N 3T1, Canada.
Warren G FosterDepartment of Obstetrics & Gynecology, McMaster University, Hamilton, ON L8S 4L8, Canada.
Yong-Lai FengExposure and Biomonitoring Division, Environmental Health Science and Research Bureau, Environmental and Radiation Health Sciences Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON K1A 0K9, Canada.ORCID https://orcid.org/0000-0002-0353-7239

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ubiquitously distributed in the environment, food supply, and consumer products, endocrine-disrupting chemicals (EDCs) are exogenous substances that disrupt hormonal activities in the endocrine system. Increasing evidence suggests that women with reproductive disorders tend to accumulate higher levels of EDCs, such as phthalates and parabens, in ovarian follicular fluid. However, most existing studies focus on the measurements of a limited number of prevalent EDCs, overlooking chemicals and metabolites that are not known or prioritized. To address the knowledge gap, we developed a non-targeted analysis (NTA) workflow for broader EDC detection in follicular fluid samples using liquid chromatography-high-resolution mass spectrometry (LC-HRMS). By taking advantage of the higher-energy collisional dissociation (HCD) in the Orbitrap mass spectrometer, we first identified up to 17 characteristic product ions for parabens and their metabolites. Compared to conventional mass spectral matching via online databases and

Indexed as

endocrine-disrupting chemicals (EDCs)follicular fluidhigh-resolution mass spectrometry (HRMS)non-targeted analysis (NTA)parabensphenols

Identifiers

PMID41425322
PMCPMC12715743

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.