Cytotoxicity of Ag, Au and Ag-Au bimetallic nanoparticles prepared using golden rod (Solidago canadensis) plant extract
Bibliographic record
Abstract
Production and use of metallic nanoparticles have increased dramatically over the past few years and design of nanomaterials has been developed to minimize their toxic potencies. Traditional chemical methods of production are potentially harmful to the environment and greener methods for synthesis are being developed in order to address this. Thus far phytosynthesis have been found to yield nanomaterials of lesser toxicities, compared to materials synthesized by use of chemical methods. In this study nanoparticles were synthesized from an extract of leaves of golden rod (Solidago canadensis). Silver (Ag), gold (Au) and Ag-Au bimetallic nanoparticles (BNPs), synthesized by use of this "green" method, were evaluated for cytotoxic potency. Cytotoxicity of nanomaterials to H4IIE-luc (rat hepatoma) cells and HuTu-80 (human intestinal) cells were determined by use of the xCELLigence real time cell analyzer. Greatest concentrations (50 µg/mL) of Ag and Ag-Au bimetallic were toxic to both H4IIE-luc and HuTu-80 cells but Au nanoparticles were not toxic. BNPs exhibited the greatest toxic potency to these two types of cells and since AuNPs caused no toxicity; the Au functional portion of the bimetallic material could be assisting in uptake of particles across the cell membrane thereby increasing the toxicity.
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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".