Combined cytotoxicity of polystyrene nanoplastics and phthalate esters on human lung epithelial A549 cells and its mechanism
Bibliographic record
Abstract
Awareness of risks posed by widespread presence of nanoplastics (NPs) and bioavailability and potential to interact with organic pollutants has been increasing. Inhalation is one of the more important pathways of exposure of humans to NPs. In this study, combined toxicity of concentrations of polystyrene NPs and various phthalate esters (PAEs), some of the most common plasticizers, including dibutyl phthalate (DBP) and di-(2-ethyl hexyl) phthalate (DEHP) on human lung epithelial A549 cells were investigated. When co-exposed, 20 μg NPs/mL increased viabilities of cells exposed to either DBP or DEHP and the modulation of toxic potency of DEHP was greater than that of DBP, while the 200 μg NPs/mL resulted in lesser viability of cells. PAEs sorbed to NPs decreased free phase concentrations (Cfree) of PAEs, which resulted in a corresponding lesser bioavailability and joint toxicity at the lesser concentration of NPs. The opposite effect was observed at the greater concentration of NPs, which may result from the dominated role of NPs in the combined toxicity. Furthermore, our data showed that oxidative stress and inflammatory reactions were mechanisms for combined cytotoxicities of PAEs and NPs on A549 cells. Results of this study emphasized the combined toxic effects and mechanisms on human lung cells, which are helpful for assessing the risk of the co-exposure of NPs and organic contaminants in humans.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".