Green Synthesis of Silver Nanoparticles Using Zingiber officinale and Ocimum gratissimum Formulation for Its Anti-inflammatory and Antidiabetic Activity: An In Vitro Study
Bibliographic record
Abstract
Aim This study aims to synthesize silver nanoparticles (AgNPs) using herbal formulations derived from Zingiber officinale (ginger) and Ocimum gratissimum and to evaluate their anti-inflammatory and antidiabetic activities in vitro. Methods The synthesis of AgNPs was performed using Z. officinale and O. gratissimum, and the AgNPs were confirmed by analyzing their ultraviolet-vis spectra. The anti-inflammatory activity was assessed using two assays, specifically the bovine serum albumin (BSA) denaturation assay and the egg albumin (EA) denaturation assay. The antidiabetic activity was assessed using the α-amylase inhibitory assay and the β-glucosidase inhibitory assay. Results This study evaluated the anti-inflammatory and antidiabetic activities of green-synthesized AgNPs using Z. officinale and O. gratissimum. The maximum absorption peak was observed for AgNPs at ~433 nm (wavelength). In the BSA denaturation assay, 78% inhibition was observed at a concentration of 50 μl. Similarly, in the EA denaturation assay, an inhibition of 74% was observed at the same concentration compared to the standard. In terms of antidiabetic activity, when compared to the standard at a concentration of 50 μl, the α-amylase inhibitory assay and the β-glucosidase inhibitory assay showed approximately 78% and 80% inhibition, respectively. Conclusion The use of Z. officinale and O. gratissimum extracts for the synthesis of AgNPs using a green synthesis method presents a sustainable and environmentally friendly approach. The synthesized AgNPs demonstrated significant anti-inflammatory and antidiabetic efficacy, suggesting their potential application in pharmaceuticals for treating diabetes and inflammation. Further research is necessary to investigate the effectiveness and safety of these substances in humans and to understand their underlying mechanisms of action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".