Green Synthesis of a Novel Silver Nanoparticle Conjugated with Thelypteris glandulosolanosa (Raqui-Raqui): Preliminary Characterization and Anticancer Activity
Bibliographic record
Abstract
In the last decade, the green synthesis of nanoparticles has had a prominent role in scientific research for industrial and biomedical applications. In this current study, silver nitrate (AgNO3) was reduced and stabilized with an aqueous extract of Thelypteris glandulosolanosa (Raqui-raqui), forming silver nanoparticles (AgNPs-RR). UV-vis spectrophotometry, dynamic light scattering (DLS), and scanning transmission electron microscopy (STEM) were utilized to analyze the structures of AgNPs-RR. The results from this analysis showed a characteristic peak at 420 nm and a mean hydrodynamic size equal to 39.16 nm, while the STEM revealed a size distribution of 6.64–51.00 nm with an average diameter of 31.45 nm. Cellular cytotoxicity assays using MCF-7 (ATCC® HTB-22™, mammary gland breast), A549 (ATCC® CCL-185, lung epithelial carcinoma), and L929 (ATCC® CCL-1, subcutaneous connective tissue of Mus musculus) demonstrated over 42.70% of MCF-7, 59.24% of A549, and 8.80% of L929 cells had cell death after 48 h showing that this nanoparticle is more selective to disrupt neoplastic than non-cancerous cells and may be further developed into an effective strategy for breast and lung cancer treatment. These results demonstrate that the nanoparticle surfaces developed are complex, have lower contact angles, and have excellent scratch and wear resistance.
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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".