Ferritin Nanocage Conjugated Hybrid Hydrogel for Tissue Engineering and Drug Delivery Applications
Bibliographic record
Abstract
Hydrogels have recently been attractive in various drug delivery and tissue engineering applications because of their structural similarities to the natural extracellular matrix. Despite enormous advances in the application of hydrogels, poor mechanical properties and lack of control for the release of drugs and biomolecules act as major barriers for widespread clinical applications. To overcome these challenges, we developed both physically and covalently conjugated nanocage-laden hydrogels between the surface of the nanocage and a gelatin methacryloyl (GelMA) hydrogel matrix. Ferritin and its empty-core equivalent apoferritin were used as nanocages that could be easily incorporated into a GelMA hydrogel via physical bonding. To fabricate covalently conjugated nanocage-laden GelMA hydrogels, ferritin and apoferritin were chemically modified to present the methacryloyl groups, ferritin methacryloyl (FerMA) and apoferritin methacryloyl (ApoMA), respectively. The covalently conjugated FerMA- and ApoMA-GelMA hydrogels offered a better ability to tune mechanical properties compared with those prepared by direct dispersion of ferritin and apoferritin into GelMA hydrogels with physical bonding, without affecting their porosity or cell growth. Furthermore, the ability of the nanocage to release small chemical compounds was confirmed by performing a cumulative release test on fluorescein isothiocyanate (FITC) encapsulated apoferritin and ApoMA incorporated GelMA hydrogels by pH stimulus. Thus, the nanocage incorporated hydrogels have emerged as excellent materials for drug delivery and tissue engineering 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".