Exploring the potential protective role of anthocyanins in mitigating micro/nanoplastic-induced reproductive toxicity: A steroid receptor perspective
Bibliographic record
Abstract
, inducing reproductive toxicity through mechanisms such as oxidative stress and inflammation. Considering these adverse effects, identifying natural compounds that can mitigate the toxicity of MPs/NPs is increasingly important. Plants offer a wealth of antioxidants and anti-inflammatory compounds that can counteract these harmful effects. Among these, anthocyanins, natural colorants responsible for the vibrant hues of fruits and flowers, exhibit a wide range of biological activities, including antioxidant, anti-inflammatory, and anti-neoplastic properties. Moreover, anthocyanins can modulate sex hormone levels and alleviate reproductive toxicity. Cyanidin-3-glucoside (C3G), one of the most extensively studied anthocyanins, shows promise in reducing reproductive toxicity, particularly in females, and in protecting male reproductive organs, including the testis and epididymis. This protective effect is believed to result from its interaction with steroid receptors, specifically the androgen and estrogen receptors (ERs). These findings highlight the need to explore the mechanisms by which anthocyanins mitigate the reproductive toxicity caused by MPs/NPs. This review provides novel insights into how natural compounds can be leveraged to lessen the impact of environmental contaminants on human health, especially concerning reproductive health.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".