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Record W7117157474 · doi:10.1016/j.neuro.2025.103371

Power calculations for larval zebrafish in light-dark transition test for developmental neurotoxicity

2025· article· en· W7117157474 on OpenAlexaff
Kathryn S. Konrad, Katherine Allen-Moyer, Lee Ellis, Ellen V.S. Hessel, Oihane Jaka, Arantza Muriana, Beatriz Molina Martinez, Ana del Pozo, Valentina Schiavone, Vincenzo Di Donato, C. Miguel Sanz, Lisa Truong, Keith R. Shockley, Kristen Ryan, Jui‐Hua Hsieh

Bibliographic record

VenueNeuroToxicology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsNational Research Council Canada
FundersNational Institute of Environmental Health SciencesNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsZebrafishSample size determinationAnalysis of varianceStatistical powerSample (material)AutismStatistical hypothesis testingRepeated measures designNeurotoxicity

Abstract

fetched live from OpenAlex

The link between environmental chemical exposures and neurodevelopmental disorders such as autism and attention-deficit/hyperactivity disorder underscores the need to develop efficient developmental neurotoxicity (DNT) assays for chemical evaluation. The zebrafish Light-Dark Transition Test (LDTT) assesses changes in zebrafish larval behavioral responses to chemical exposure by recording their distance moved under alternating light and dark conditions. To gain confidence in classifying a chemical as having a DNT effect for the LDTT assay, it is important to determine the minimum sample size to obtain a robust behavioral response. We calculated statistical power under common models based on LDTT data collected from four laboratories using standard protocol parameters, where each 96-well plate contained 5–7 test concentrations and 12–16 vehicle control wells (1 larva/well). Power calculations were conducted to identify concentration effects using t-tests, analysis of variance (ANOVA), and repeated measures ANOVA (RMANOVA), with data from four endpoints: Total Distance, Movement Similarity, Distance Change, and Distance Shift. The tests showed the highest power for the Movement Similarity and Distance Change endpoints, which had the lowest intra- and inter-laboratory variability, resulting in a smaller necessary sample size to estimate dose effects. The use of these endpoints more than doubled the power of the statistical tests for the Total Distance endpoints using the same sample size and typically required between 8-32 samples to achieve 80% power at a 20% effect size. This work demonstrates that the LDTT can be improved for detecting DNT effects by careful consideration of endpoint selection, data transformation, and type of statistical test. • New light-dark transition endpoints streamline zebrafish neurodevelopment research • Statistical power differs across zebrafish endpoints and light-dark phases • Data normalization by vehicle control per plate minimizes inter-lab variations • Normalized data doubled statistical power in the study protocol • 8-32 samples needed per dose group for 80% power at 20% effect size

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)0.002
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 score0.454
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.000
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 head0.013
GPT teacher head0.308
Teacher spread0.295 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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