MétaCan
Menu
Back to cohort
Record W4406787201 · doi:10.1038/s42003-025-07471-8

Methylene blue at recommended concentrations alters metabolism in early zebrafish development

2025· article· en· W4406787201 on OpenAlexafffund
Niepukolie Nipu, Lai Wei, Lauren E Hamilton, Hyojin Lee, Jith K. Thomas, Jan A. Mennigen

Bibliographic record

VenueCommunications Biology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsHealth CanadaUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaHealth CanadaUniversity of Ottawa
KeywordsZebrafishMethylene blueMetabolismChemistryCell biologyBiologyBiochemistryGene

Abstract

fetched live from OpenAlex

Methylene blue (MB) is an antifungal agent widely used during critical stages of zebrafish development. Most guidelines recommend 0.00005% or 0.0001% of MB for embryo/larval rearing. The Organisation for Economic Co-operation and Development zebrafish embryo toxicity test guideline omits MB recommendations, leading to inconsistent MB use in zebrafish research. Because MB affects oxidative energy metabolism in vitro and in vivo, we investigate possible metabolic effects of recommended MB concentrations in developing zebrafish (1–5 days post-fertilization (dpf)). MB increases O2 consumption rate at 1 dpf, followed by an overall reduction in oxidative energy metabolism in post-hatch eleutheroembryos (4–5 dpf). Concomitantly, mitochondrial transcripts decrease in 1 and 4 dpf zebrafish. Our findings show that MB, at recommended husbandry concentrations, affects oxidative metabolism and can thus confound experiments. Since the zebrafish embryo/larval model is gaining traction as a high-throughput New Approach Methodology (NAM) for toxicity assessment, researchers should reconsider MB use. Methylene Blue is an antifungal additive widely used in zebrafish embryo medium. At recommended concentrations, it exerts acute metabolic effects. This represents a previously unappreciated confound in embryo/larval zebrafish experiments.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.641

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 science0.0010.001
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 head0.028
GPT teacher head0.342
Teacher spread0.314 · 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

Citations9
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueCommunications BiologySame topicZebrafish Biomedical Research ApplicationsFrench-language works237,207