Release of insulin from PLGA–alginate dressing stimulates regenerative healing of burn wounds in rats
Bibliographic record
Abstract
Burn wound healing involves a complex set of overlapping processes in an environment conducive to ischaemia, inflammation and infection costing $7.5 billion/year in the U.S.A. alone, in addition to the morbidity and mortality that occur when the burns are extensive. We previously showed that insulin, when topically applied to skin excision wounds, accelerates re-epithelialization and stimulates angiogenesis. More recently, we developed an alginate sponge dressing (ASD) containing insulin encapsulated in PLGA [poly(D,L-lactic-co-glycolic acid)] microparticles that provides a sustained release of bioactive insulin for >20 days in a moist and protective environment. We hypothesized that insulin-containing ASD accelerates burn healing and stimulates a more regenerative, less scarring healing. Using heat-induced burn injury in rats, we show that burns treated with dressings containing 0.04 mg insulin/cm(2) every 3 days for 9 days have faster closure, a higher rate of disintegration of dead tissue and decreased oxidative stress. In addition, in insulin-treated wounds, the pattern of neutrophil inflammatory response suggests faster clearing of the burned dead tissue. We also observe faster resolution of the pro-inflammatory macrophages. We also found that insulin stimulates collagen deposition and maturation with the fibres organized more like a basket weave (normal skin) than aligned and cross-linked (scar tissue). In summary, application of ASD-containing insulin-loaded PLGA particles on burns every 3 days stimulates faster and more regenerative healing. These results suggest insulin as a potential therapeutic agent in burn healing and, because of its long history of safe use in humans, insulin could become one of the treatments of choice when repair and regeneration are critical for proper tissue function.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".