Vape flavorants dull sensory perception and cause hyperactivity in developing zebrafish embryos
Bibliographic record
Abstract
E-cigarette use (vaping) during pregnancy has been increasing, and the potential exists for the developing brain in utero to be exposed to chemical constituents in the vape. Vapes come in over 7,000 unique flavors with and without nicotine, and while nicotine is a known neurotoxicant, the effects of vape flavoring alone, in the absence of nicotine, on brain function are not well understood. Here we performed a screen of vape aerosol extracts (VAEs) to determine the potential for prenatal neurotoxicity using the zebrafish embryo photomotor response (PMR) – a translational biosensor of neurobehavioral effects. We screened three commonly used aerosolized vape liquids (flavored and flavorless) either with or without nicotine. No neurobehavioral effects were detected in flavorless, nicotine-free VAEs, while the addition of nicotine to this VAE dulled sensory perception. Flavored nicotine-free VAEs also dulled sensory perception and caused hyperactivity in zebrafish embryos. The combination of flavor and nicotine produced largely additive effects. Flavored VAEs without nicotine had similar neuroactive potency as nicotine. Similar effects were also seen in embryos exposed to the pure flavoring compound, cinnamaldehyde. Together, using zebrafish PMR as a high throughput translational behavioral model for prenatal exposure, our results demonstrate that e-cigarette flavorants that we screened elicit neurobehavioral effects worthy of further investigation for long-term neurotoxic potential, and also have the potential to modulate nicotine impact on the developing 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".