A Novel Behavioral Fish Model of Nociception for Testing Analgesics
Bibliographic record
Abstract
Pain is a major symptom in many medical conditions, and often interferes significantly with a person’s quality of life. Although a priority topic in medical research for many years, there are still few analgesic drugs approved for clinical use. One reason is the lack of appropriate animal models that faithfully represent relevant hallmarks associated with human pain. Here we propose zebrafish (Danio rerio) as a novel short-term behavioral model of nociception, and analyse its sensitivity and robustness. Firstly, we injected two different doses of acetic acid as the noxious stimulus. We studied individual locomotor responses of fish to a threshold level of nociception using two recording systems: a video tracking system and an electric biosensor (the MOBS system). We showed that an injection dose of 10% acetic acid resulted in a change in behavior that could be used to study nociception. Secondly, we validated our behavioral model by investigating the effect of the analgesic morphine. In time-course studies, first we looked at the dose-response relationship of morphine and then tested whether the effect of morphine could be modulated by naloxone, an opioid antagonist. Our results suggest that a change in behavioral responses of zebrafish to acetic acid is a reasonable model to test analgesics. The response scales with stimulus intensity, is attenuated by morphine, and the analgesic effect of morphine is blocked with naloxone. The change in behavior of zebrafish associated with the noxious stimulus can be monitored with an electric biosensor that measures changes in water impedance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".