Nanobiofunctionalized chitosan nanocomposite hydrogel as a highly biocompatible cancer drug carrier
Bibliographic record
Abstract
Abstract A bio‐inspired drug carrier was developed by dual functionalization of chitosan using L ‐glutamic acid (GA) and phyto‐synthesized zinc oxide nanoparticles (ZNPs). A highly porous, three‐dimensional network of nanocomposite hydrogel (GA‐CHGZ) was obtained upon cross‐linking chitosan using biomass‐derived dialdehyde cellulose. The hydrogel was optimally loaded with naringenin (NRG) and further characterized using nuclear magnetic resonance (NMR), Fourier‐transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD), scanning electron microscopy (SEM), and swelling studies. An enhanced NRG loading efficiency of 85.23% was obtained using functionalized hydrogel compared to 52.54% using non‐functionalized hydrogel. Delivery studies displayed a maximum release of 69.63% for 1.0 mg/ml of initial NRG concentration at pH 5, which is a highly preferred condition for cancer therapeutics. While ZNPs’ embedment was instrumental in improving the NRG loading and delivery rates, the GA conjugation increased the stability of NRG in the GA‐CHGZ, aiding sustained NRG release, which followed a non‐Fickian diffusion mechanism with polymer swelling. Antimicrobial potential was explored against Staphylococcus aureus and Trichophyton rubrum strains. The biocompatibility assay using L929 normal cells showed enhanced cell proliferation characteristics for the materials, revealing significant cell viability. The anticancer activity of NRG tested against A431 human skin carcinoma cells increased up to nine‐fold with a reduced IC 50 value when a functionalized hydrogel was used instead of pure NRG without the nanocomposite carrier. Thus, the bio‐functionalized drug–carrier system has a promising application for wound healing and topical skin cancer therapies.
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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".