Toxic effects on enzymatic activity, gene expression and histopathological biomarkers in organisms exposed to microplastics and nanoplastics: a review
Bibliographic record
Abstract
Abstract Microplastics (MPs) and nanoplastics (NPs) have become an important global environmental issue due to their widespread contamination in the environment. This review summarizes existing literature on the effects of MPs/NPs on three important biomarkers including enzymatic activity, gene expression, and histopathology in various organisms from 2016 to 2021 and suggests a path forward for future research. Application of enzymatic activity, gene expression, and histopathology biomarkers are increasingly used in experimental toxicology studies of MPs/NPs because of their early signs of environmental stress to organisms. Between 2016 to 2021, 70% of published studies focused on aquatic organisms, compared to terrestrial organisms. Zebrafish were widely used as a model organism to study adverse impacts of MPs/NPs. Polystyrene (PS) were the most important polymer used in experimental toxicology studies of MPs/NPs. Fewer studies focused on the histopathological alterations compared to studies on enzymatic activity and gene expression of different organisms exposed to MPs/NPs. There is a growing need to better understand toxic effects of environmentally relevant concentrations of MPs/NPs on enzymatic activity, gene expression, and histopathology biomarkers of both aquatic and terrestrial organisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".