The Known and Unknown: Investigating the Carcinogenic Potential of Plastic Additives
Bibliographic record
Abstract
Microplastics are routinely ingested and inhaled by humans and other organisms. Despite the frequency of plastic exposure, little is known about its health consequences. Of particular concern are plastic additives─chemical compounds that are intentionally or unintentionally added to plastics to improve functionality or as residual components of plastic production. Additives are often loosely bound to the plastic polymer and may be released during plastic exposures. To better understand the health effects of plastic additives, we performed a comprehensive literature search to compile a list of 2,712 known plastic additives. Then, we performed an integrated toxicogenomic analysis of these additives, utilizing cancer classifications and carcinogenic expression pathways as a primary focus. Screening these substances across two chemical databases revealed two key observations: (1) over 150 plastic additives have known carcinogenicity and (2) the majority (∼90%) of plastic additives lack data on carcinogenic end points. Analyses of additive usage patterns pinpointed specific polymers, functions, and products in which carcinogenic additives reside. Based on published chemical-gene interactions, both carcinogenic additives and additives with unknown carcinogenicity impacted similar biological pathways. The predominant pathways involved DNA damage, apoptosis, the immune response, viral diseases, and cancer. This study underscores the urgent need for a systematic and comprehensive carcinogenicity assessment of plastic additives and regulatory responses to mitigate the potential health risks of plastic exposure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".