Environmental exposure to microplastics: a scoping review on potential human health effects and knowledge gaps
Bibliographic record
Abstract
Background: Microplastics are fast becoming a major global environmental contaminant. Little is known about the health effects of microplastic exposure to humans despite being omnipresent in all spheres of life and ecology. This scoping review explores the existing evidence of potential human health effects of microplastics and subsequent knowledge gaps. Methods: An electronic search of published articles in PubMed, Scopus, EMBASE, Cochrane databases, and Google Scholar was conducted, using a combination of subject headings and keywords for microplastics and human health effects. Documents only published in English between 2004 and March 2020 were included. A grey literature search was conducted following a comprehensive checklist in Google Scholar and the environmental organization websites. The initial search resulted in 17,043 published articles and grey literature. After a full review of published articles and their references, 125 publications were identified for further detailed review. Throughout the screening process, every document was reviewed by at least two of the researchers. Results: These articles indicate that human exposure to microplastics might occur through ingestion, inhalation, and dermal contact due to its presence in food, water, air, and consumer products. Microplastics exposure can cause particle toxicity through oxidative stress, inflammatory lesions, and increased uptake or translocation. Several studies have demonstrated the potentiality of metabolic disturbances, neurotoxicity, increased cancer risk, and reproductive toxicity in humans. Moreover, microplastics were found to release their constituent compounds and those adsorbed onto its surface in human tissues. Conclusion: Knowledge of microplastic toxicity on human health is still limited. Further research is needed to quantify the effect of microplastics on human health and pathogenesis.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".