Neurotoxic effects of citronellol induced by the conversion of kynurenine to 3-hydroxykynurenine
Bibliographic record
Abstract
Citronellol is widely utilized in consumer products, including cosmetics, fragrances, and household items. However, despite being considered a relatively safe chemical, the health effects and toxicity mechanisms associated with exposure to high concentrations of citronellol, based on product content, remain inadequately understood. Here, we aimed to analyze the neurological effects of citronellol in zebrafish larvae using behavioral and histological analyses and elucidate the mechanisms underlying its neurotoxicity in vivo. Exposure to citronellol (2, 4 and 8 mg/L) in zebrafish larvae induced a range of neurotoxic effects, including locomotor impairments, anxiety-like behaviors, oxidative stress, an inflammatory response, and apoptosis in the brain. Additionally, citronellol exposure compromised the blood-brain barrier (BBB) integrity, permitting the infiltration of inflammatory cell into the brain. Neurotoxic effects were further sustained by increased kynurenine (KYN) metabolism to the neurotoxic metabolite 3-hydroxykynurenine (3-HK), accompanied by altered neurosteroid levels, including reduced progesterone and allopregnanolone, and elevated cortisol. Similar metabolic dysregulation was observed in mouse models following oral administration (345, 690 and 3450 mg/kg) and in human brain organoids exposed to citronellol (1, 10 and 100 μM), suggesting conserved mechanisms across species. Notably, experiments using zebrafish, mice and brain-chip systems confirmed that citronellol crosses the BBB and accumulates in the brain. Overall, we identified a novel neurotoxic pathway involving the KYN to 3-HK metabolic pathway, oxidative stress, and neuroinflammation, underscoring the potential risks of prolonged citronellol exposure.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".