Methylene Blue Is a Widely Used Antifungal Agent That Confounds Behavioral Toxicity Assays in Larval Zebrafish
Bibliographic record
Abstract
Zebrafish are widely used as model organisms in biological research with embryos typically reared in media supplemented with methylene blue (MB) as an antifungal agent. Many animal care guidelines recommend the use of MB during early development stages. However, this practice overlooks MB’s known effects as a monoamine oxidase inhibitor and antidepressant. This study demonstrates that at recommended husbandry concentrations, MB significantly reduces zebrafish locomotion in a 24 h behavior assay, a finding consistent across strains and laboratories. Gene expression profiling and pharmacological experiments using the MAO-inhibitor deprenyl suggest that MB induces hypolocomotion by increasing the serotonergic tone. Importantly, MB use in standard embryo medium masks known hypolocomotor responses to fluoxetine, a common aquatic contaminant and selective serotonin-reuptake inhibitor. These findings have significant implications for the increasing use of larval zebrafish in high-throughput neurotoxicity assessments and highlight the need to reconsider the use of MB in zebrafish research. The study emphasizes the importance of eliminating potential confounds in husbandry practices and improving experimental protocol reporting to enhance reproducibility in zebrafish-based (eco)toxicity testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".