Polyethylene Terephthalate Microplastics Generated from Disposable Water Bottles Induce Interferon Signaling Pathways in Mouse Lung Epithelial Cells
Bibliographic record
Abstract
Microplastics (MPs) are present in ambient air in a respirable size fraction; however, their potential impact on human health via inhalation routes is not well documented. In the present study, methods for a lab-scale generation of MPs from regularly used and littered plastic articles were optimized. The toxicity of 11 different types of MPs, both commercially purchased and in-lab prepared MPs, was investigated in lung epithelial cells using cell viability, immune and inflammatory response, and genotoxicity endpoints. The underlying mechanisms were identified by microarray analysis. Although laborious, the laboratory-scale methods generated a sufficient quantity of well characterized MPs for toxicity testing. Of the 11 MPs tested, the small sized polyethylene terephthalate (PETE) MPs prepared from disposable water bottles induced the maximum toxicity. Specifically, the smaller size PETE MPs induced a robust activation of the interferon signaling pathway, implying that PETE MPs are perceived by cells by similar mechanisms as those employed to recognize pathogens. The PETE MPs of heterogenous size and shapes induced cell injury, triggering cell death, inflammatory cascade, and DNA damage, hallmark in vitro events indicative of potential in vivo tissue injury. The study establishes toxicity of specific types of plastic materials in micron and nano size.
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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".