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Record W4416765832 · doi:10.1016/j.etap.2025.104876

Dose-dependent toxicokinetics of permethrin in rats: A comparative analysis of four exposure levels

2025· article· en· W4416765832 on OpenAlexafffund
Zeineb Lakehal, Jonathan Côté, Sami Haddad, Michèle Bouchard

Bibliographic record

VenueEnvironmental Toxicology and Pharmacology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsUniversité de Montréal
FundersUniversité de Montréal
KeywordsToxicokineticsMetabolitePermethrinPyrethroidUrineExcretionBiomonitoring

Abstract

fetched live from OpenAlex

Pyrethroid metabolites are used as biomarkers of human exposure but the influence of exposure levels on their toxicokinetics remains unclear. We examined the effect of administered dose on the toxicokinetics of permethrin metabolites. Sprague-Dawley rats were administered single gavage doses of permethrin (trans/cis 60:40) at 0.004, 0.04, 0.4 and 4 mg/kg bw. Serial blood, urine and fecal samples were collected. Trans- and cis-3-(2,2-dichlorovinyl)-2,2-dimethyl-cyclopropane-1-carboxylic acids (trans/cis-DCCA), 3-phenoxybenzoic acid (3-PBA), and 4-hydroxy-3-phenoxybenzoic acid (4-OH3PBA) were quantified. A clear effect of dose on metabolite profiles in blood was observed: appearance rate increased with doses, while terminal elimination half-life and mean residence time decreased. In urine, the predominant elimination route, fraction of metabolites recovered declined significantly at 0.4 and 4 mg/kg bw, whereas minor fecal excretion pathway was unaffected by dose. These findings show that permethrin dose governs both rates and extent of metabolite disposition, with key implications for exposure reconstruction from biomonitoring data.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.295
Teacher spread0.259 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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