Extracellular matrix-inspired biomaterials for wound healing
Bibliographic record
Abstract
Diabetic foot ulcers (DFU) are a debilitating and life-threatening complication of Diabetes Mellitus. Ulceration develops from a combination of associated diabetic complications, including neuropathy, circulatory dysfunction, and repetitive trauma, and they affect approximately 19-34% of patients as a result. The severity and chronic nature of diabetic foot ulcers stems from the disruption to normal wound healing, as a result of the molecular mechanisms which underly diabetic pathophysiology. The current standard-of-care is clinically insufficient to promote healing for many DFU patients, resulting in a high frequency of recurrence and limb amputations. Biomaterial dressings, and in particular those derived from the extracellular matrix (ECM), have emerged as a promising approach for the treatment of DFU. By providing a template for cell infiltration and skin regeneration, ECM-derived biomaterials offer great hope as a treatment for DFU. A range of approaches exist for the development of ECM-derived biomaterials, including the use of purified ECM components, decellularisation and processing of donor/ animal tissues, or the use of in vitro-deposited ECM. This review discusses the development and assessment of ECM-derived biomaterials for the treatment of chronic wounds, as well as the mechanisms of action through which ECM-derived biomaterials stimulate 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".