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Record W4416775568 · doi:10.3389/fnbeh.2025.1717998

Exploring the impact of acute solvent exposure on larval zebrafish behaviour

2025· article· en· W4416775568 on OpenAlexafffund
Ethan V. Hagen, Yanbo Zhang, Trevor J. Hamilton

Bibliographic record

VenueFrontiers in Behavioral Neuroscience · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsMacEwan UniversityWomen and Children’s Health Research InstituteUniversity of Alberta
FundersInstitute of Circulatory and Respiratory HealthCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaFondation Brain Canada
KeywordsThigmotaxisZebrafishEthanolDimethyl sulfoxideLarvaPrepulse inhibitionMethanol

Abstract

fetched live from OpenAlex

) are commonly used to test the impact of pharmacological and toxicological compounds. Larval zebrafish are extensively used because of high throughput procedures allowing simultaneous behavioural measurement in 24-, 48-, or 96-well plates. Often solvents are used as a vehicle for poorly soluble or insoluble compounds, however, the impact of dimethyl sulfoxide (DMSO), methanol, and ethanol after acute administration is not well characterized. Here we investigated the impact of 30-min exposures of DMSO, methanol, and ethanol (0.01%, 0.1%, and 1.0% vol/vol) on 5-day old larval zebrafish locomotion and startle responses. We found no effect of DMSO on distance moved and thigmotaxis in a spontaneous swimming test, and no effect on dark-, light-, or tap-startle responses compared to controls. Methanol and ethanol, both at 1.0% increased the distance moved, and ethanol decreased the dark startle response at 1.0%. Neither ethanol nor methanol had any impact on time in thigmotaxis zone, light- or tap-startle responses. Results from this study suggest that with acute exposure to experimental compounds requiring a solvent, the least impact on behaviour would occur with DMSO, followed by methanol, then ethanol.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.351
Teacher spread0.304 · 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 designObservational
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

Citations2
Published2025
Admission routes2
Has abstractyes

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