3D Bioprinting of the Sustained Drug Release Wound Dressing with Double-Crosslinked Hyaluronic-Acid-Based Hydrogels
Bibliographic record
Abstract
Hyaluronic acid (HA)-based hydrogels are widely used in biomedical applications due to their excellent biocompatibility. HA can be Ultraviolet (UV)-crosslinked by modification with methacrylic anhydride (HA-MA) and crosslinked by modification with 3,3′-dithiobis(propionylhydrazide) (DTP) (HA-SH) via click reaction. In the study presented in this paper, a 3D-bioprinted, double-crosslinked, hyaluronic-acid-based hydrogel for wound dressing was proposed. The hydrogel was produced by mixing HA-MA and HA-SH at different weight ratios. The rheological test showed that the storage modulus (G’) of the HA-SH/HA-MA hydrogel increased with the increase in the HA-MA content. The hydrogel had a high swelling ratio and a high controlled degradation rate. The in vitro degradation test showed that the hydrogel at the HA-SH/HA-MA ratio of 9:1 (S9M1) degraded by 89.91% ± 2.26% at 11 days. The rheological performance, drug release profile and the cytocompatibility of HA-SH/HA-MA hydrogels with loaded Nafcillin, which is an antibacterial drug, were evaluated. The wound dressing function of this hydrogel was evaluated by Live/Dead staining and CCK-8 assays. The foregoing results imply that the proposed HA-SH/HA-MA hydrogel has promise in wound repair 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) | 0.000 | 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".