Evidence map›Paper›PMID 42284469›Full record

ArticleEnvironmental science & technology2026

Probabilistic Concentration-Response Modeling and Risk Prioritization of Defined Mixtures of PFAS Using Human

Lucie C Ford, Hsing-Chieh Lin, Weihsueh A Chiu, Ivan Rusyn

Abstract read
In one paragraph

Article in Environmental science & technology, 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

4 authors.

Lucie C FordDepartment of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, 77843 Texas, United States.
Hsing-Chieh LinDepartment of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, 77843 Texas, United States.
Weihsueh A ChiuDepartment of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, 77843 Texas, United States.ORCID 0000-0002-7575-2368
Ivan RusynDepartment of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, 77843 Texas, United States.ORCID 0000-0001-9340-7384

Funding

Single cell, multi-parametric high throughput platform to classify endocrine disruptor potential of mixturesP42ES027704 · NIEHS · TEXAS A&M UNIVERSITY · PI Thomas Joseph McDonald · 2017 to 2026
$21.2M
Regulatory Science in Environmental Health and ToxicologyT32ES026568 · NIEHS · TEXAS A&M UNIVERSITY · PI Weihsueh A Chiu, Natalie M Johnson · 2016 to 2026
$3.8M
NIEHS NIH HHS P42 ES027704NIEHS NIH HHS T32 ES026568
6 · The paper itself

Abstract

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants. Some individual PFAS are linked to adverse health effects, but little data is available to evaluate PFAS mixtures. We tested the biological effects of 20 defined PFAS mixtures (containing 4-56 PFAS) across eight human cell types, including iPSC-derived cardiomyocytes, neurons, and hepatocytes; primary hepatocytes; and HepG2s, endothelial, and renal proximal tubule epithelial cell lines. Mixtures were designed to reflect realistic exposures based on drinking/surface water data, regulatory limits, biomonitoring results, and prior

Indexed as

FluorocarbonsHepatocytesHep G2 CellsHumansRisk AssessmentFluorocarbonsconcentration additionconcentration−response modelinghuman in vitro assaysmixture risk assessmentPFAS mixtures

Identifiers

PMID42284469
PMCPMC13325866

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.