Gelatin-Oxidized Alginate and Chitosan-Coated Zein Nanoparticle Hydrogel Composite to Enhance Breast Cancer Cytotoxicity in Dual-Drug Delivery
Bibliographic record
Abstract
This study explores the combined delivery of doxorubicin and quercetin using a gelatin-oxidized alginate-based hydrogel as a promising strategy for localized breast cancer therapy. Our approach involves the incorporation of doxorubicin within the hydrogel matrix and loading quercetin into chitosan-coated zein nanoparticles. The hydrogel exhibited self-healing properties attributed to Schiff base cross-linking and demonstrated injectability. Characterization of its microstructural, mechanical, and textural properties revealed a porous and flexible structure, demonstrating its suitability for drug release applications. Both drugs exhibited distinct in vitro release profiles at pH 6.8 (typical of tumor tissue), with doxorubicin at 81.2% and quercetin at 9.7%. After 72 h of release, the cytotoxicity against MCF-7 breast cancer cells was assessed. The hydrogel formulation containing doxorubicin increased the cytotoxic action by 4.66-fold, whereas the hydrogel composite, containing both doxorubicin and quercetin-loaded nanoparticles, enhanced it by 20.7-fold compared with doxorubicin alone. Thus, the findings of our study highlight the enhancing effect of the dual release system, thereby expanding the utility of gelatin-oxidized alginate-based hydrogels as advanced drug delivery systems, as exemplified by the combined delivery of doxorubicin and quercetin.
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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".