Controlled Nitric Oxide Delivery Platform Based on<i>S</i>-Nitrosothiol Conjugated Interpolymer Complexes for Diabetic Wound Healing
Bibliographic record
Abstract
Nitric oxide (NO) is known to play a critical role in enhancing wound healing as topical NO administration has demonstrated enhanced wound healing in diabetic animal models. However, this approach has been limited by the short duration of NO release, short half-life of NO, and instability of available NO donors. To overcome these deficiencies, we have developed a new NO delivery platform based on grafting S-nitrosothiols (RSNOs), derived from endogenous glutathione (GSH) or its oligomeric derivatives, phytochelatins (PCs), onto poly(vinyl methyl ether-co-maleic anhydride) (PVMMA), and their subsequent formation of interpolymer complexes with poly(vinyl pyrrolidone) (PVP). Such interpolymer complexes provide controlled release of NO for an extended duration (>10 days) and exhibit enhanced stability in the solid state over that of free RSNOs. The existence of intermolecular hydrogen bonding in such complexes and the formation of disulfide bonds following the NO release have been confirmed by FTIR and Raman. Preliminary wound healing study in a diabetic rat model demonstrates that, with a single topical application, the present controlled release NO delivery system can effectively accelerate wound closure as compared with the control (p < 0.05). The results suggest that the present NO releasing interpolymer complexes could be potentially useful for diabetic wound healing.
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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".