Prenatal Polysubstance Exposure Alters Behaviour in Zebrafish Larvae
Bibliographic record
Abstract
Abstract Substance use during pregnancy has been linked to various adverse outcomes in infants, including congenital disabilities, neurodevelopmental delays, and long-term effects such as learning difficulties. An additional concern is that newborns are often exposed to multiple substances in utero. The biological consequences of such exposure remain largely unknown. Zebrafish offer an exciting alternative to fill this gap and deepen our understanding of the biological impact of prenatal multidrug exposure. We utilized zebrafish’s scalability to expose embryos to some of the most commonly used substances: nicotine, alcohol, opioids, and all their possible combinations. After embryonic drug exposure, we conducted a detailed behavioural analysis across three developmental stages. Our results revealed drug-specific outcomes, including both synergistic and antagonistic effects. Furthermore, we identified distinctive effects across development, highlighting potential developmental shifts and individual differences in resilience. Overall, these findings demonstrate that prenatal polydrug exposure results in complex, stage-dependent effects, sometimes antagonistic, which cannot be predicted from single-drug outcomes. Our study emphasizes the value of zebrafish as a model for investigating polydrug interactions and provides a framework for exploring biomarkers of vulnerability and resilience in offspring.
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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) | 0.003 | 0.001 |
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".