Photo-Crosslinkable Double-Network Hyaluronic Acid Based Hydrogel Dressing
Bibliographic record
Abstract
Hyaluronic acid (HA)-based hydrogels are widely used in biomedical applications due to their excellent biocompatibility and enzymatic degradability. In this paper a photo-crosslinking double-network hyaluronic acid-based hydrogel dressing was proposed. Hyaluronic acid can be UV-crosslinked by modification with methacrylic anhydride (HA-MA) and disulfide-crosslinked by modification with 3,3'-dithiobis (propionylhydrazide) (DTP) (HA-SH). The mixings of these two materials at different ratios were produced. All the samples can be quickly gelled at 365 nm for 10 s. The rheological tests show that the storage modulus (G') of the double network (HA-SH/HA-MA) hydrogel is increased with the increase of HA-SH content. The HA-SH/HA-MA hydrogel has porous structure, high swelling ratio and Controlled degradation rate. In vitro degradation tests show that the ratio of HA-SH/HA-MA ratio was 9:1 (S9M1) in 100 U/ml hyaluronidase (Hase) degraded by 89.91±2.26% at 11d. The cytocompatibility of HA-SH/HA-MA hydrogels was proved by Live/Dead stainings and CCK-8 assays in the human dermis fibroblasts (HDF) cells test. All these results highlight the biological potential of the HA-SH/HA-MA hydrogels for DFU intervention.
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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".