Facile bio-inspired fabrication of AgNPs from Salvia elegans leaf extract and determination of their cytotoxic DNA photocleavage potential
Bibliographic record
Abstract
This study introduces a sustainable method for producing silver nanoparticles (AgNPs), focusing on sustainability and environmental protection.The technique includes the use of Salvia elegans aqueous leaf extract as a reducing agent.In addition, the research investigates the dose-dependent degradation of pUC19 DNA by silver nanoparticles, which is facilitated by the generation of singlet oxygen.The presence of S. elegans extract results in significant color changes, going from a colorless state to a dark brown hue, serving as an indication of the synthesis of AgNPs.A range of experimental methods were employed to analyze the biogenic AgNPs, such as UV-visible absorption spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM) and scanning electron microscopy (SEM).The meticulously produced AgNPs demonstrate a high level of uniformity, featuring a spherical shape and a particle size of 60 nm.DNA photo-cleavage studies show that singlet oxygen plays a crucial role in triggering DNA damage caused by AgNPs, indicating their potential as powerful cytotoxic agents specifically aimed at cancer cells.Additional studies are required to clarify the effectiveness and specificity of AgNPs against various cancer cell types to assess their therapeutic potential.The cytotoxic effects of AgNPs, particularly in relation to DNA photocleavage, are of considerable interest in cancer research.This research holds promise for developing novel and sustainable cancer therapies based on the unique properties of biogenic silver nanoparticles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".