Titanium Dioxide Nanoparticles Aggravated the Developmental Neurotoxicity of Ammonia Nitrogen on Zebrafish Embryos
Bibliographic record
Abstract
Total ammonia nitrogen (TAN) is a common and potent neurotoxic pollutant in aquatic environments. Due to their strong adsorption capacity, titanium dioxide nanoparticles (n-TiO2), a widely used engineered material, can induce combined toxicity with multiple pollutants. However, the combined neurotoxicity of n-TiO2 and TAN and its underlying mechanisms remain unclear. In this study, zebrafish embryos were exposed to TAN (0, 0.1, 1, 10 mg/L) and n-TiO2 (100 µg/L) individually or in combination for 120 h. The results indicated that co-exposure to n-TiO2 and TAN significantly increased the bioaccumulation of TAN in zebrafish embryos compared to TAN alone. Consequently, this led to exacerbated neurotoxicity, manifested as developmental impairments and abnormal motor behavior. Mechanistic investigations revealed that the co-exposure aggravated developmental neurotoxicity by triggering neuronal apoptosis and oxidative stress, disrupting the cholinergic and dopaminergic systems, and impairing neural and retinal development. Transcriptomic analysis further indicated that the co-exposure predominantly perturbed neurodevelopment, oxidative stress, and apoptosis. In conclusion, this study confirms that n-TiO2 significantly amplifies TAN-induced neurodevelopmental toxicity by promoting its bioaccumulation and synergistically disrupting multiple neurophysiological processes. These findings provide crucial scientific evidence for assessing the combined ecological risks of nanomaterials and conventional pollutants.
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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".