A biocompatible Ag NP/SA.GL hydrogel for enhanced delivery and sustained release of doxorubicin in cancer treatment
Bibliographic record
Abstract
This research focused on the development of a hydrogel of silver nanoparticles (Ag NPs), sodium alginate (SA) and gelatin (GL) for the targeted delivery of anticancer drugs. Doxorubicin (DOX), an anticancer drug, was selected as a model drug and successfully loaded into the hydrogel. By incorporating Ag NPs and DOX, the hydrogel enables tumor-specific targeting of the drug and controlled release. The characterization of synthesized silver nano-particles (AgNPs) by laser ablation method was performed using UV-visible spectroscopy and transmission electron microscopy (TEM). UV-vis spectroscopy confirmed nanoparticle formation by detecting a distinct surface plasmon resonance (SPR) peak at approximately 420 nm, which is characteristic of AgNPs. TEM imaging provided detailed morphological analysis, revealing spherical nanoparticles with an average diameter of 20 nm. The structural and chemical properties of DOX-loaded Ag NPs/SA.GL hydrogel was analyzed by UV spectroscopy, Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FESEM) with energy dispersive X-ray spectroscopy (EDX). The hydrogel did not show an initial explosive release of DOX, with only about 5% of the drug being released within the first 24 min. The drug release was rapid in the initial phase before slowing down over time, with the cumulative release pattern following this trend. At a pH of 7.4, approximately 60% of DOX was released from the Ag-NPs/SA.GL hydrogel. In addition, the encapsulation efficiency of DOX within the hydrogel was approximately 15%, highlighting its strong ability to retain the drug. These results suggest that Ag NPs/SA.GL hydrogels loaded with DOX are promising for targeted drug delivery and cancer treatment applications.
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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".