Eco-Friendly Green Synthesis and Characterization of Silver Nanoparticles by Scutellaria multicaulis Leaf Extract and Its Biological Activities
Why this work is in the frame
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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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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 it