Altered locomotion and anxiety after exposure to SiO2 nanoparticles in larval zebrafish
Bibliographic record
Abstract
Abstract Nanoparticles (NP) have been driving the rapid advancement of nanomedicines in recent decades. However, their wide application also raises safety concerns, particularly their neurotoxicity due to their ability to cross the blood-brain barrier and accumulate in the brain, which remains largely underexplored. Here, we used silica nanoparticles (SiO 2 NP) as a model to study the neurotoxicity of nanomedicine, based on their general features and functionalities. Using the light/dark preference behavioral assays of larval zebrafish, we focused on the neurotoxic consequences of exposure to an array of low concentrations of SiO 2 NP, which reflected real-world conditions compared to previous studies, and examined the effect of different exposure durations. We observed dose-dependent and temporally sensitive changes in locomotor activities and elevated anxiety-related behaviors after exposure. Strikingly, exposed animals exhibited biphasic alteration: hypo-locomotion after 24-hour exposure and hyper-locomotion after 48-hour exposure. Our work provided real-world relevant behavioral insights, and highlighted the biphasic response and the temporal sensitivity of the SiO 2 NP neurotoxicity. These findings underscore the potential neurotoxic risks of nanomedicine applications and emphasize the urgent need for further research into NP-associated neurotoxicity and public awareness.
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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) | 0.001 | 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".