Synthesis, Characterization, and Cytotoxicity Evaluation of Gallic Acid Nanoparticles Towards Breast T47D Cancer Cells
Bibliographic record
Abstract
Introduction: Gallic acid is a naturally polyphenolic acid which shows cytotoxicity against several cancer cells, as well as it displays chemo-preventive activity which is attributed to its strong apoptosis-inducing and antioxidant effects. Thus, gallic acid has become an attractive substance to be further developed due to its strong cytotoxic activity. This study aimed to synthesize gallic acid nanoparticle coating with alginate-chitosan, and evaluate its cytotoxicity against breast T47D cancer cells. Methods: Gallic acid nanoparticle was synthesized using ionic gelation method. The yield, size and morphology of the nanoparticles were determined by UV-Vis Spectroscopy, Transmission electron microscopy (TEM) and Fourier Transform Infrared (FTIR) spectroscopy. Cytotoxicity evaluation of gallic acid nanoparticle towards breast T47D cancer cell is carried out by MTT(3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazoliumbromide) assay. Results: Spherical nanoparticles of gallic acid with the size of 100-200 nm has been successfully synthesized in 96% of yield. Compared to gallic acid (IC 50 : 20.86 g/mL) and alginate-chitosan nanoparticle (IC 50 : 38.46 g/mL), gallic acid coating with alginate-chitosan nanoparticles demonstrated higher cytotoxicity towards breast T47D cancer cells with IC 50 value of 9.03g/mL. Conclusion: Our results clearly confirmed that gallic acid nanoparticles coating with alginate-chitosan showed a strong cytotoxicity towards breast T47D cancer cells, which is potential to be developed as a candidate for new anti-breast cancer agent.
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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".