Photodynamic Therapy and TiO2-Decorated Ag Nanoparticles: Implications for Skin Cancer Treatment
Bibliographic record
Abstract
This in vitro study was conducted to evaluate the effects of blue light (λ = 420-480nm), titanium dioxide-decorated silver (TiO2/Ag) nanoparticles, and their combined application on the viability of A431 skin cancer cells.Cells cultured in a medium enriched with 10% fetal bovine serum were subjected to three distinct treatments: exposure to blue light, application of TiO2/Ag nanoparticles, and a combination of both.The MTT assay was utilized to determine cell viability post 24-hour incubation, with the resultant color intensity measured via a plate reader.Findings indicated a significant reduction in cell viability: blue light exposure led to an approximately 82% decrease at the 45-minute mark; nanoparticle treatment resulted in a notable viability reduction across all concentrations (p≤0.001); and the combined treatment displayed an enhanced effect, reducing cell viability by 42.79% relative to the control group after 45 minutes.Notably, the highest nanoparticle concentration (400 µg/ml) demonstrated superior anticancer efficacy.The synergistic impact of the combined treatment was evident, surpassing the individual effects of either blue light or TiO2/Ag nanoparticles in diminishing A431 cell numbers.This study underscores the potential of integrating photodynamic therapy (PDT) with nanoparticle technology in targeting skin cancer cells, highlighting a promising avenue for therapeutic intervention.
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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".