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Record W4400952966 · doi:10.1289/ehp13667

Investigation of Peroxisome Proliferator-Activated Receptor Genes as Requirements for Visual Startle Response Hyperactivity in Larval Zebrafish Exposed to Structurally Similar Per- and Polyfluoroalkyl Substances (PFAS)

2024· article· en· W4400952966 on OpenAlexaff
Sebastian Gutsfeld, Leah C. Wehmas, Ifeoluwa Omoyeni, Nicole Schweiger, David Leuthold, Paul Michaelis, Xia Meng Howey, Shaza Gaballah, N. Herold, Carolina Vogs, Carmen R. Wood, Luísa Becker Bertotto, Gi‐Mick Wu, Nils Klüver, Wibke Busch, Stefan Scholz, Jana Schor, Tamara Tal

Bibliographic record

VenueEnvironmental Health Perspectives · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsInstitut de Recherche et de Développement en Agroenvironnement
FundersOffice of Research and DevelopmentU.S. Environmental Protection Agency
KeywordsZebrafishGenePeroxisome proliferatorReceptorLarvaPeroxisome proliferator-activated receptorBiologyEndocrinologyInternal medicineChemistryMedicineGeneticsEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Per- and polyfluoroalkyl Substances (PFAS) are synthetic chemicals widely detected in humans and the environment. Exposure to perfluorooctanesulfonic acid (PFOS) or perfluorohexanesulfonic acid (PFHxS) was previously shown to cause dark-phase hyperactivity in larval zebrafish. OBJECTIVES: The objective of this study was to elucidate the mechanism by which PFOS or PFHxS exposure caused hyperactivity in larval zebrafish. METHODS: PFOS or 0.4% DMSO from 1-4 dpf and behavior was assessed at 5 dpf. Coexposure with the ppard antagonist GSK3787 and PFOS was also performed. RESULTS: in PFOS-exposed larvae. DISCUSSION: function. https://doi.org/10.1289/EHP13667.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.328
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Health PerspectivesSame topicPer- and polyfluoroalkyl substances researchFrench-language works237,207