Periostin and CCN2 Scaffolds Promote the Wound Healing Response in the Skin of Diabetic Mice
Bibliographic record
Abstract
Impaired skin wounds represent a significant and growing complication associated with type II diabetes and effective clinical strategies to reproducibly induce wound resolution do not exist. As a class of secreted nonstructural matrix proteins that modulate many cellular aspects of skin healing, matricellular proteins could represent a novel mechanism to promote skin healing. Based on their role in the regulation of the proliferative phase of healing, we hypothesized that local delivery of periostin and CCN2 could suppress inflammation and progress wounds into the proliferative and remodeling phases of repair. Using electrospinning, scaffolds composed of collagen type I and periostin or CCN2 were fabricated. Delivery of periostin/collagen and CCN2/collagen electrospun scaffolds increased excisional wound closure rates in a murine db/db diabetic model compared with collagen alone or untreated wounds. Reduced neutrophil infiltration was evident in the presence of periostin/collagen and CCN2/collagen scaffolds, concomitant with an increase in mesenchymal cell infiltration versus empty and collagen scaffolds alone at day 7. Microarray analysis demonstrated scaffold-dependent upregulation of gene clusters associated with wound contraction, cell differentiation, and suppression of PPARγ signaling at day 7; however, no changes in mRNA levels of extracellular matrix genes were observed compared with controls. At day 11, significantly increased vascularization of the wound bed was evident. Local delivery of periostin- and CCN2-based biomaterials may represent a novel strategy to induce the proliferative phase of repair and correction of the healing process in impaired skin wounds. Impact Statement Nonhealing skin wounds remain a significant burden on health care systems, with diabetic patients 20 times as likely to undergo a lower extremity amputation due to impaired healing. Novel treatments that suppress the proinflammatory signature and induce the proliferative and remodeling phases are needed clinically. We demonstrate that the addition of periostin and CCN2 in a scaffold form increases closure rates of full-thickness skin wounds in diabetic mice, concomitant with enhanced angiogenesis. Our results demonstrate the efficacy of periostin- and CCN2-containing biomaterials to stimulate wound closure, which could represent a novel method for the treatment of diabetic skin wounds.
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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 | 0.000 |
| 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 | 0.001 | 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".