Simple detection of polystyrene nanoparticles and effects in freshwater mussels: method development and <i>in situ</i> application to urban pollution
Bibliographic record
Abstract
The ubiquity of plastics in environments worldwide is raising concerns about their toxicity to organisms. The purpose of this study was to investigate simple means to determine the exposure and effects of nanoplastics (NPs) in the freshwater mussels Elliptio complanata (E. complanata ). NP tissue levels were determined using a plasmonic nanogold sensor probe and effects were determined using the refractive index (RI) and thiol-reaction rates (TRR) in protein-dense tissue extracts. This method was adapted to quantitatively measure the concentration of NPs in tissues using a salting-out extraction in the presence of acetonitrile (ACN). Concentrated solutions of albumin were first spiked with NPs to evaluate changes in RI and TRR to determine crowding effects. The data revealed that NPs readily decreased the RI and TRR in albumin in vitro . These three simple assays were then applied on freshwater mussels caged for 3 months at various sites in a largely populated area. Mussels downstream of the city center and found at the street runoff discharge sites were highly contaminated by NPs and the RI and TRR were also reduced. In conclusion, simple and readily accessible assays to assess the NP contamination based on a visual nanogold sensor technology, and the effects of plastics are proposed for freshwater mussels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.000 | 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".