How microplastics affect chiral illicit drug methamphetamine in aquatic food chain? From green alga (Chlorella pyrenoidosa) to freshwater snail (Cipangopaludian cathayensis)
Bibliographic record
Abstract
The biological impacts of microplastics on many organisms have been well documented. However, the combined effects of microplastics and chiral chemicals on the aquatic food chain are less clear. In the present study, the enantioselective environmental behaviors of methamphetamine co-exposed with microplastics through an aquatic food chain (from Chlorella pyrenoidosa to Cipangopaludian cathayensis) have been investigated in a laboratory environment. It was found that the acute toxicity of methamphetamine against these two species was significantly increased in the presence of microplastics: Chlorella pyrenoidosa showed an EC50 shift from 0.77 to 0.32 mg L−1, while cipangopaludian cathayensis showed an LC50 shift from 4.15 to 1.48 mg L−1, upon the addition of microplastics as a co-contaminant with methamphetamine. Upon exposure to methamphetamine and microplastics, the oxidative damage of algae (19.9 to 36.8 nmol mgprot−1), apoptosis (increase about 2.17 times) and filtration rate (41.2 to 65.4 mL h−1) of snails were observably higher when compared to exposure to methamphetamine alone. After ingestion and accumulation of microplastics, the enantioselectivity, BCFs, BMFs, and distribution of methamphetamine were significantly altered. These results provide evidence that the co-occurrence of microplastics and the chiral drug methamphetamine may increase the burden on aquatic species, with potential further impacts throughout aquatic food chain.
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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".