Biological interactions of polystyrene nanoplastics: Their cytotoxic and immunotoxic effects on the hepatic and enteric systems
Bibliographic record
Abstract
As emerging pollutants in the environment, nanoplastics (NPs) can cross biological barriers and be enriched in organisms, posing a greatest threat to the health of livestock and humans. However, the size-dependent toxic effects of NPs in higher mammals remain largely unknown. To determine the size-dependent potential toxicities of NPs, we exposed mouse (AML-12) and human (L02) liver cell lines in vitro, and 6-week-old C57BL/6 mice (well-known preclinical model) in vivo to five different sizes of polystyrene NPs (PS-NPs) (20, 50, 100, 200 and 500 nm). We found that ultra-small NPs (20 nm) induced the highest cytotoxicity in mouse and human liver cell lines, causing oxidative stress and mitochondrial membrane potential loss on AML-12 cells. Unexpectedly in vivo, after long-term oral exposure to PS-NPs (75 mg/kg), medium NPs (200 nm) and large NPs (500 nm) induced significant hepatotoxicity, evidenced by increased oxidative stress, liver dysfunction, and lipid metabolism disorders. Most importantly, medium or large NPs generated local immunotoxic effects via recruiting and activating more numbers of neutrophils and monocytes in the liver or intestine, which potentially resulted in increased proinflammatory cytokine secretion and the tissue damage. The discrepancy in in vitro-in vivo toxic results might be attributed to the different properties of biodistribution and tissue accumulation of different sized NPs in vivo. Our study provides new insights regarding the hepatotoxicity and immunotoxicity of NPs on human and livestock health, warranting us to take immense measures to prevent these NPs-associated health damage.
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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".