Defining the Chemical Additives Driving <i>In Vitro</i> Toxicities of Plastics
Bibliographic record
Abstract
Increases in the global use of plastics have caused concerns regarding potential adverse effects on human health. Plastic products contain hundreds of potentially toxic chemical additives, yet the exact chemicals which drive toxicity currently remain unknown. In this study, we employed nontargeted analysis and in vitro bioassays to identify the toxicity drivers in plastics. A total of 56 chemical additives were tentatively identified in five commonly used plastic polymer pellets (i.e., PP, LDPE, HDPE, PET, and PVC) by employing suspect screening and nontargeted analysis. Phthalates and organophosphates were found to be dominant in PVC pellets. Triphenyl phosphate and 2-ethylhexyl diphenyl phosphate accounted for a high amount (53.6%) of the inhibition effect of PVC pellet extract on human carboxylesterase 1 (hCES1) activity. Inspired by the high abundances of chemical additives in PVC pellets, six different end-user PVC-based products including three widely used PVC water pipes were further examined. Among them, extracts of PVC pipe exerted the strongest PPARγ activity and cell viability suppression. Organotins were identified as the primary drivers to these in vitro toxicities induced by the PVC pipe extracts. This study clearly delineates specific chemical additives responsible for hCES1 inhibition, PPARγ activity, and cell viability suppression associated with plastic.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".