Biochemical effects of polypropylene microplastics on red tilapia (Oreochromis niloticus) after individual and combined exposure with boron
Bibliographic record
Abstract
Abstract Toxicity of single pollutants or microplastics (MPs) on organisms have been widely reported. However, their combined toxicity with boron has not been investigated. This study examined effects of individual polypropylene microplastics (PP-MPs) or mixed PP-MPs and boron on biochemical biomarkers in red tilapia (Oreochromis niloticus). O. niloticus were exposed for 21 days to pristine PP-MPs concentrations (10 or 100 mg/L), concentrations of boron alone (30 or 70 mg/L), and identical concentrations of boron in the presence of PP-MPs in laboratory aquaria. Results showed that higher concentrations of individual PP-MPs lead to significantly decreased acetylcholinesterase (AChE) in the brain and malondialdehyde (MDA) in fish liver. In contrast, superoxide dismutase (SOD), glutathione peroxidase (GPx), and glutathione (GSH) were significantly increased in fish liver exposed to higher concentrations of individual PP-MPs. Mixed concentrations of boron and PP-MPs significantly decreased AChE, GSH, and MDA activity in fish. In contrast, mixed concentrations of boron and PP-MPs significantly increased CAT, SOD, and GPx activity in fish. Findings highlight that PP-MPs may increase adverse effects of boron in O. niloticus. We present evidence that individual MPs in long-term exposure have a significant impact on biomarker responses in O. niloticus.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".