A Green Approach for the Biosynthesis of Gold Nanoparticles Using Cuminum cyminum L. Seed and Its Application for Pain Management in Rats
Bibliographic record
Abstract
This study investigated the antinociceptive effect of cumin and its biosynthesized gold nanoparticles (AuNPs).Cumin extract (E) and cumin-AuNPs (GN) were prepared and administered intraperitoneally at the concentrations of 200, 500, and 1000 mg/ml to 27 male rats. Ultraviolet-visible spectroscopy and atomic force microscopy were applied for AuNPs synthesis confirmation. The nociceptive behavior was assessed, and IL-6 serum levels were measured.Cumin-AuNPs showed a peak absorption of 515 nm, and a size of about 40 nm. Three different concentrations of extract had no significant effect on acute and chronic nociceptive behavior. GN + E200 (46.00 ± 10.59) showed a significant acute anti-nociceptive effect compared to the control (98.66 ± 4.91; p = 0.029) and SS300 (98.33 ± 20.30; p = 0.029) groups. Also, GN + E500 (42.00 ± 11.84) significantly reduced acute nociceptive behavior compared to the control (98.66 ± 4.91; p = 0.019), SS300 (98.33 ± 20.30; p = 0.020), and GN + E1000 (91.00 ± 26.00; p = 0.040) groups. IL-6 serum levels reduced significantly in GN + E500 (24.65 ± 10.38; p = 0.002) and SS300 (33.08 ± 1.68; p = 0.039) compared to the controls (46.24 ± 3.02). Chronic nociceptive behavior was significantly lower in the SS300 (255.33 ± 26.30) compared to E200 (477.00 ± 47.29; p = 0.021), E500 (496.25 ± 46.29; p = 0.013), and GN + E500 (437.00 ± 118.03; p = 0.032) groups.Our findings suggest the potential effects of cumin-AuNPs on formalin-induced nociceptive behavior, which is independent of IL-6serum levels.
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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".