Ascorbyl palmitate nanofiber-reinforced hydrogels for drug delivery in soft tissues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nanofiber-based hydrogel delivery systems have recently shown great potential in biomedical applications, specifically due to their high surface-to-volume ratio of ultra-fine nanofibers and their ability to carry low solubility drugs. Herein, we introduce a visible light-triggered in situ-gelling drug vehicle (GAP Gel) composed of ascorbyl palmitate (AP) nanofibers and gelatin methacryloyl polymer. AP nanofibers form self-assembled structures through intermolecular interactions with a hydrophobic drug-loading core. We demonstrate that the hydrophilic periphery of AP nanofibers allows them to interact with other hydrophilic molecules via hydrogen bonds. The presence of AP nanofibers significantly enhances the viscoelasticity of GAP Gel in a concentration-dependent manner. Further, GAP Gel shows in vitro biocompatibility and sustained drug delivery efficacy when loaded with a hydrophobic antibiotic. Likewise, GAP Gel shows excellent in vivo biocompatibility when implanted in immunocompetent mice in various forms. Lastly, GAP Gels maintain cell viability when cultured in a 3D-environment over 7 days, establishing it as a promising and versatile hydrogel platform for the delivery of biotherapeutics. Nanofiber-based hydrogels are useful delivery systems in biomedical applications due to their drug loading capability and controlled release. Here, a biocompatible visible light-triggered in situ-gelling drug delivery system is demonstrated consisting of ascorbyl palmitate nanofibers and gelatin methacryloyl polymer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it