Toxicological Evaluation of Silver Nanoparticles Synthesized with Peel Extract of Stenocereus queretaroensis
Bibliographic record
Abstract
Silver nanoparticles (AgNPs) synthesized with plants are widely used in different industries, such as the medical, industrial, and food industries; however, their hazards and risks remain unclear. Here, we aimed to evaluate the toxicological effects of AgNPs in both in vitro and in vivo models. Previously, we developed and characterized green synthesized AgNPs based on Stenocereus queretaroensis (S. queretaroensis). The present study evaluates the toxicity of these AgNPs through cytotoxicity and mutagenicity tests in vitro, as well as genotoxicity tests, including the evaluation of acute oral, dermal, and inhalation toxicity, along with dermal and ocular irritation, in vivo, according to guidelines of The Organization for Economic Co-operation and Development (OECD). We evaluated cell cytotoxicity in L929 cells, and the half-maximal inhibitory concentration was 134.76 µg/mL. AgNPs did not cause genotoxic or mutagenic effects. Furthermore, in vivo oral, dermal, and acute inhalation toxicity results did not show any adverse effects or mortality in the test animals, and after the dermal and ocular irritation assessments, the in vivo models did not exhibit irritation or corrosion. Therefore, the results show that these previously synthesized S. queretaroensis AgNPs do not represent a risk at the tested concentrations; however, little is known about the effects that AgNPs induce on physiological systems or the possible risk following long-term exposure.
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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".