Anti-Cancer and Stress Response Pathway Effects of Nanosilver and Sodium Ascorbate
Bibliographic record
Abstract
Nanosilver (nAg) has superb antimicrobial, antiviral, antifungal, antiparasitic, and anticancer properties; and plays an important role in nanoscience, nanotechnology, and nanomedicine.Smaller nAg particles can enter cells and interact with the cellular components.The exposure dose, particle size, coating, and aggregation state, as well as the cell type or organism, are all large determining factors on the beneficial or toxic effects of nAg.Sodium ascorbate is a vital water-soluble antioxidant that can neutralize free radicals produced during illness.It has pro-oxidant effects in its oxidized form, and in this form is able to destroy cancer cells through the production of hydrogen peroxide.With the growing prevalence of cancer, there is an increasing need to find both new treatments and combinations of treatments to provide greater effectiveness.Nanosilver was found to be more toxic to HCT116 human colon cancer cells (24-hour EC50 of 78.43 ± 0.70 µg/mL) than to HIEC-6 human intestinal epithelial cells (no toxicity was observed for the treatment concentrations tested).Combined treatments of high dose sodium ascorbate with nAg demonstrated significantly increased toxicity to HCT116 cells as opposed to HIEC-6 cells, and increased the cell death from that observed with either of the treatments alone.The novel result was found that it is more toxic to cancer cells to use a combined treatment of high dose sodium ascorbate with low dose nAg; as opposed to low dose sodium ascorbate with high dose nAg, where a protective effect is seen.Nanosilver induced cell cycle arrest in the G2/M phase in HCT116 cells, and combined treatment with sodium ascorbate further increased this effect.This was found to be a nAg-specific effect and did not occur with silver nitrate.Cellular oxidative stress was not induced by nAg, however, mitochondrial oxidative stress was induced in HCT116 cells after 24 hours of treatment.No significant effect on mitochondrial
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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) | 0.001 | 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".