Anti-inflammatory and Antioxidant Activity of Ocimum tenuiflorum- and Stevia rebaudiana-Mediated Silver Nanoparticles: An In Vitro Study
Bibliographic record
Abstract
Background: Nanotechnology, a field bridging material science and biology, explores various applications. Silver nanoparticles, ranging from 1 nm to 100 nm, are commonly labeled as "silver," although some contain a substantial quantity of silver oxide owing to the heightened ratio of silver atoms on the surface compared to the bulk. This characteristic establishes silver as a prominent nanoparticulate material. Stevia, valued for its leaf's sweetness and purported therapeutic qualities, has been utilized for centuries in South America functioning both as a natural sweetener and in traditional health remedies. The objective of this study was to evaluate the anti-inflammatory and antioxidant activity of Ocimum tenuiflorum- and Stevia rebaudiana-mediated silver nanoparticles. Methods: The methods employed involved evaluating the anti-inflammatory effects using the bovine serum albumin (BSA) assay and antioxidant effects using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, with varying concentrations (10 µL, 20 µL, 30 µL, 40 µL, and 50 µL) of the nanoparticles. Results: The results indicated that the anti-inflammatory properties of the nanoparticles surpassed standard values at concentrations of 10 µL, 20 µL, and 30 µL, while the antioxidant properties were also notably surpassing standard values at equivalent concentrations. The maximum inhibition percentage was noted with 10 µL (72.5%). Conclusion: The silver nanoparticles, fortified with extracts from Ocimum tenuiflorum and Stevia rebaudiana, exhibited a promising potential as effective anti-inflammatory and antioxidant agents, suggesting their viability as alternatives to commercially available products.
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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 | 0.000 |
| 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 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".