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Record W4286005444 · doi:10.3390/ijms23147985

Assessing the Morphological and Behavioral Toxicity of Catechol Using Larval Zebrafish

2022· article· en· W4286005444 on OpenAlexaff
Michael G. Morash, Kelly H. Soanes, John C. Achenbach, Lee Ellis

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2022
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCatecholHypoactivityZebrafishToxicityGenotoxicityToxicologyBiologyChemistryPharmacologyBiochemistryNeuroscienceGene

Abstract

fetched live from OpenAlex

Catechol is a ubiquitous chemical used in the manufacturing of fragrances, pharmaceuticals and flavorants. Environmental exposure occurs in a variety of ways through industrial processes, during pyrolysis and in effluent, yet despite its prevalence, there is limited information regarding its toxicity. While the genotoxicity and gastric carcinogenicity of catechol have been described in depth, toxicological studies have potentially overlooked a number of other effects relevant to humans. Here, we have made use of a general and behavioral larval zebrafish toxicity assay to describe previously unknown catechol-based toxicological phenomena. Behavioral testing revealed catechol-induced hypoactivity at concentrations an order of magnitude lower than observable endpoints. Catechol exposure also resulted in punctate melanocytes with concomitant decreases in the expression of pigment production and regulation markers mitfa, mc1r and tyr. Because catechol is converted into a number of toxic metabolites by tyrosinase, an enzyme found almost exclusively in melanocytes, an evaluation of the effects of catechol on these cells is critical to evaluating the safety of this chemical. This work provides insights into the toxic nature of catechol and highlights the benefits of the zebrafish larval testing platform in being able to dissect multiple aspects of toxicity with one model.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.149

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.383
Teacher spread0.323 · 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.

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

Citations10
Published2022
Admission routes1
Has abstractyes

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