Eco-Friendly Green Synthesis and Characterization of Silver Nanoparticles by Scutellaria multicaulis Leaf Extract and Its Biological Activities
Bibliographic record
Abstract
Scutellaria multicaulis is a medicinal plant indigenous to Iran, Afghanistan, and Pakistan. It has been widely used as a prominent herb in traditional medicine for thousands of years. This plant is reported to contain baicalein, wogonin, and chrysin flavonoids, which are a significant group of chemical ingredients which can cure different diseases, such as breast cancer. S. multicaulis leaf extract was used for the bioreduction of silver nanoparticles (SmL-Ag-NPs), and their phytochemical contents and antioxidant, antibacterial, anti-proliferative, and apoptotic activity were evaluated. Optimal physicochemical properties of SmL-Ag-NPs were obtained by mixing 5% of leaf extract and 2 mM of aqueous AgNO3 solution and confirmed by characterization studies including UV–visible spectrophotometry, Field Emission Scanning Electron Microscope (FE-SEM), Energy Dispersive X-ray (EDX), Dynamic Light Scattering (DLS), zeta potential, Thermogravimetric analysis (TGA), Surface-enhanced Raman spectroscopy (SERS), X-ray crystallography (XRD), and Fourier transform infrared (FTIR) Spectroscopy. SmL-Ag-NPs exhibited a higher content of total phenol and total flavonoid and potential antioxidant activity. SmL-Ag-NPs also demonstrated dose-dependent cytotoxicity against MDA-MB231 cell multiplication with an IC50 value of 37.62 μg/mL through inducing cell apoptosis. Results show that SmL-Ag-NPs is effective at inhibiting the proliferation of MDA-MB231 cells compared to tamoxifen. This demonstrates that SmL-Ag-NPs could be a bio-friendly and safe strategy to develop new cancer therapies with a reduction in the adverse effects of chemotherapy in the near future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".