Silver nanoparticle toxicity effect on growth and cellular viability of the aquatic plant <i>Lemna gibba</i>
Bibliographic record
Abstract
The toxicity effect of silver nanoparticles (AgNPs) on growth and cellular viability was investigated on the aquatic plant Lemna gibba exposed over 7 d to 0, 0.01, 0.1, 1, and 10 mg/L of AgNPs. Growth inhibition was demonstrated by a significant decrease of frond numbers dependent on AgNP concentration. Under these conditions, reduction in plant cellular viability was detected for 0.1, 1, and 10 mg/L of AgNPs within 7 d of AgNPs treatment. This effect was highly correlated with the production of intracellular reactive oxygen species (ROS). A significant increase of intracellular ROS formation was triggered by 1 and 10 mg/L of AgNP exposure. The induced oxidative stress was related to Ag accumulation within L. gibba plant cells and with the increasing concentration of AgNP exposure in the medium. The authors' results clearly suggested that AgNP suspension represented a potential source of toxicity for L. gibba plant cells. Due to the low release capacity of free soluble Ag from AgNP dissolution in the medium, it is most likely that the intracellular uptake of Ag was directly from AgNPs, triggering cellular oxidative stress that may be due to the release of free Ag inside plant cells. Therefore, the present study demonstrated that AgNP accumulation in an aquatic environment may represent a potential source of toxicity and a risk for the viability of duckweeds.
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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".