Oxidized Hyaluronic Acid-Based Sponges: A Promising Biomaterial for Oral Mucosa Lesion Application
Bibliographic record
Abstract
Chitosan (CHT) and hyaluronic acid (HA) are biomaterials with diverse properties. While each has been individually employed for the treatment of oral lesions, there is a need for further evidence regarding their combined properties. This study compares the effects on the properties and biocompatibility of chitosan sponges, CHT crosslinked with oxidized hyaluronic acid (OHA) (oxidized at 1:1 and 1:2 ratios, respectively), and CHT crosslinked with oxidized hyaluronic acid and polyethylene glycol diglycidyl ether (PEGDE). Spectroscopy revealed reduced free amino groups and the amide I/II ratio in CHT sponges crosslinked with OHA. SEM confirmed the porous network morphology with an average pore size ranging from 155 to 213 μm. TGA indicated the scaffolds' decomposition temperature (Td) increased from 253° to 308°, with the CHT-OHA 1:2 sponge exhibiting the highest thermal stability. Compression testing highlighted that the chitosan sponges crosslinked with AHO and PEGDE at a 1:2 ratio displayed a higher elastic modulus than the other studied scaffolds. The MTS assay confirmed that the fabricated biomaterials were not cytotoxic. This study demonstrates the enhanced properties and biocompatibility of CHT-OHA and CHT-OHA-PEGDE sponges, highlighting their potential for oral lesion treatment.
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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".