Scutellaria multicaulis Bio-Leave Synthesized Silver Nanoparticles: A Potential Anti-proliferative, Antioxidant and Apoptosis Inducer Compound
Bibliographic record
Abstract
Scutellaria multicaulis, a member of the Lamiaceae, 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 with baicalein, wogonin, and chrysin flavonoids as a significant group of chemical ingredients, which can cure different diseases such as breast cancer. S. multicaulis leave 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 leave extract and 2 mM of aqueous AgNO3 solution and confirmed by characterization studies including UV–visible spectrophotometry, FE-SEM, EDX, DLS, zeta potential, TGA, SERS, XRD and FTIR Spectroscopy. SmL-Ag-NPs exhibited higher content of TPC (Total Phenolic Content) and TFC (Total Flavonoid Content) and potential antioxidant activity. SmL-Ag-NPs also demonstrated dose-dependent cytotoxicity against MDA-MB231 cells multiplication with an IC50 value of 37.62 μg/mL at 48h through inducing cell apoptosis. This is the first report on the biosynthesis of silver nanoparticles using S. multicaulis leave extract, which can provide treatment for cancer diseases and reduce some negative effects of chemotherapy.
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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) | 0.000 | 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 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".