Adsorption of bio-organic eco-corona molecules reduces the toxic response to metallic nanoparticles in Daphnia magna
Bibliographic record
Abstract
As the use of engineered nanomaterials increases, so does the risk of them spreading to natural ecosystems. Hitherto, knowledge regarding the toxic properties of nanoparticles (NP's) and their potential interactions with natural bio-organic molecules adsorbed to them, and thereby forming surface coronas, is limited. However, we show here that the toxic effect of NPs of tungsten carbide cobalt (WC-Co) and cobalt (Co) on the crustacean Daphnia magna is postponed in the presence of natural biological degradation products (eco-corona biomolecules). For Daphnia exposed to WC-Co NPs the survival time increased with 20-25% and for Co NPs with 30-47% after mixing the particles with a solution of eco-corona biomolecules before exposure. This suggests that an eco-corona, composed of biomolecules always present in natural ecosystems, reduces the toxic potency of both studied NPs. Further, the eco-coronas did not affect the particle uptake, suggesting that the reduction in toxicity was related to the particle-organism interaction after eco-corona formation. In a broader context, this implies that although the increasing use and production of NPs may constitute a novel, global environmental threat, the acute toxicity and long-term effects of some NPs will, at least under certain conditions, be reduced as they enter natural ecosystems.
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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) | 0.000 | 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".