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Record W4283031433 · doi:10.3389/fendo.2022.895064

Antidepressants as Endocrine Disrupting Compounds in Fish

2022· review· en· W4283031433 on OpenAlexafffund
William A. Thompson, Mathilakath M. Vijayan

Bibliographic record

VenueFrontiers in Endocrinology · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReproductive biology and impacts on aquatic species
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonoaminergicEndocrine systemAntidepressantBiologyPopulationFish <Actinopterygii>ZebrafishHormoneMedicineNeuroscienceEndocrinologyEnvironmental healthFisherySerotoninReceptor

Abstract

fetched live from OpenAlex

As antidepressant usage by the global population continues to increase, their persistent detection in aquatic habitats from municipal wastewater effluent release has led to concerns of possible impacts on non-target organisms, including fish. These pharmaceuticals have been marketed as mood-altering drugs, specifically targeting the monoaminergic signaling in the brain of humans. However, the monoaminergic systems are highly conserved and involved in the modulation of a multitude of endocrine functions in vertebrates. While most studies exploring possible impact of antidepressants on fish have focused on behavioural perturbations, a smaller spotlight has been placed on the endocrine functions, especially related to reproduction, growth, and the stress response. The purpose of this review is to highlight the possible role of antidepressants as endocrine disruptors in fish. While studies linking the effects of environmentally relevant levels of antidepressant on the endocrine system in fish are sparse, the emerging evidence suggests that early-life exposure to these compounds have the potential to alter the developmental programming of the endocrine system, which could persist as long-term and multigenerational effects in teleosts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.334
Teacher spread0.297 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations32
Published2022
Admission routes2
Has abstractyes

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