A systematic review of the potential neurotoxicity of micro-and nanoplastics: the known and unknown
Bibliographic record
Abstract
BACKGROUND: The escalating accumulation of micro- and nanoplastics (MNPs) in the environment has raised significant concerns regarding their neurotoxic potential in vertebrates. This critical review synthesizes evidence from 234 original research articles across aquatic and terrestrial models, as well as in vitro systems, to evaluate the impacts of MNPs on the brain. MAIN BODY: Emerging data suggest that MNPs may reach the brain via olfactory translocation or by penetrating the blood-brain barrier, potentially facilitated by biomolecular corona formation. However, distribution kinetics, long-term retention, and true internal exposure levels remain unresolved. We highlight that neurotoxic outcomes, such as oxidative stress, cholinergic dysfunction, neurotransmitter imbalances, and neuronal apoptosis, vary widely depending on particle size, shape, polymer type, exposure concentration, and host species. Nevertheless, inconsistencies across models and experimental conditions, such as mismatches between oxidative stress markers and behavioral effects or lack of dose-response relationships, hinder mechanistic clarity and translational relevance to human health. Notably, most current studies employ spherical polystyrene particles at supraphysiological concentrations, limiting ecological and clinical extrapolation. Interactions with microbial biofilms and host microbiota are largely unexplored, despite their probable role in modulating neurotoxicity via the gut-brain axis. Moreover, most studies rely on analytical methods validated only for microplastic detection, while robust, standardized approaches for identifying nanoplastics in environmental and biological matrices remain lacking. These gaps hinder accurate exposure quantification, obscure tissue-specific accumulation patterns, and complicate human health risk estimation. CONCLUSION: To advance the field, we recommend comprehensive physicochemical characterization of MNPs, adoption of environmentally relevant exposure scenarios, inclusion of diverse polymer types and shapes, and mechanistic integration through multi-omics and adverse outcome pathway frameworks. Addressing these challenges through harmonized methodologies and interdisciplinary collaboration is essential for developing predictive models of MNP-induced neurotoxicity and informing human health risk assessments.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".