Acute and chronic alcohol dose: population differences in behavior and neurochemistry of zebrafish
Bibliographic record
Abstract
The zebrafish has been in the forefront of developmental genetics for decades and has also been gaining attention in neurobehavioral genetics. It has been proposed to model alcohol-induced changes in human brain function and behavior. Here, adult zebrafish populations, AB and SF (short-fin wild type), were exposed to chronic treatment (several days in 0.00% or 0.50% alcohol v/v) and a subsequent acute treatment (1 h in 0.00%, 0.25%, 0.50% or 1.00% alcohol). Behavioral responses of zebrafish to computer-animated images, including a zebrafish shoal and a predator, were quantified using videotracking. Neurochemical changes in the dopaminergic and serotoninergic systems in the brain of the fish were measured using high-precision liquid chromatography with electrochemical detection. The results showed genetic differences in numerous aspects of alcohol-induced changes, including, for the first time, the behavioral effects of withdrawal from alcohol and neurochemical responses to alcohol. For example, withdrawal from alcohol abolished shoaling and increased dopamine and 3,4-dihydroxyphenylacetic acid in AB but not in SF fish. The findings show that, first, acute and chronic alcohol induced changes are quantifiable with automated behavioral paradigms; second, robust neurochemical changes are also detectable; and third, genetic factors influence both alcohol-induced behavioral and neurotransmitter level changes. Although the causal relationship underlying the alcohol-induced changes in behavior and neurochemistry is speculative at this point, the results suggest that zebrafish will be a useful tool for the analysis of the biological mechanisms of alcohol-induced functional changes in the adult brain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".