On-Demand Dissolvable Self-Healing Hydrogel Based on Carboxymethyl Chitosan and Cellulose Nanocrystal for Deep Partial Thickness Burn Wound Healing
Bibliographic record
Abstract
Deep partial thickness burn wounds present big challenges due to the long healing time, large size and irregular shape, pain and reinjury at wound dressing changes, as well as scarring. The clinically effective therapy to alleviate pain at wound dressing changes, and the scar left on the skin after the healing of wound is still unavailable. To combat this, we develop a nanocomposite self-healing hydrogel that can be injected into irregular and deep burn wound beds and subsequently rapidly self-heal to reform into an integrated piece of hydrogel that thoroughly fills the wound area and protects the wound site from external environment, finally being painlessly removed by on-demand dissolving using amino acid solution at wound dressing changes, which accelerates deep partial thickness burn wound healing and prevents scarring. The hydrogel is made out of naturally occurring polymers, namely, water-soluble carboxymethyl chitosan (CMC) and rigid rod-like dialdehyde-modified cellulose nanocrystal (DACNC). They are cross-linked by dynamic Schiff-base linkages between amines from CMC and aldehydes from DACNC. The large aspect ratio and specific surface area of DACNC raise massive active junctions within the hydrogel, which can be readily broken and reformed, allowing hydrogel to rapidly self-heal. Moreover, DACNC serves as nanoreinforcing fillers to improve the hydrogel strength, which also restricts the "soft" CMC chains' motion when soaked in aqueous system, endowing high fluid uptake capacity (350%) to hydrogel while maintaining integrity. Cytotoxicity assay and three-dimensional cell culture demonstrate excellent biocompatibility of the hydrogel and capacity as extracellular matrix to support cell growth. This work opens a novel pathway to fabricate on-demand dissolvable self-healing hydrogels to speed deep partial thickness burn wound healing and eliminate pain at wound dressing changes and prevent scar formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".