Biochemical and Biophysical Cues in Matrix Design for Chronic and Diabetic Wound Treatment
Bibliographic record
Abstract
Progress in biomaterial science and engineering and increasing knowledge in cell biology have enabled us to develop functional biomaterials providing appropriate biochemical and biophysical cues for tissue regeneration applications. Tissue regeneration is particularly important to treat chronic wounds of people with diabetes. Understanding and controlling the cellular microenvironment of the wound tissue are important to improve the wound healing process. In this study, we review different biochemical (e.g., growth factors, peptides, DNA, and RNA) and biophysical (e.g., topographical guidance, pressure, electrical stimulation, and pulsed electromagnetic field) cues providing a functional and instructive acellular matrix to heal diabetic chronic wounds. The biochemical and biophysical signals generally regulate cell-matrix interactions and cell behavior and function inducing the tissue regeneration for chronic wounds. Some technologies and devices have already been developed and used in the clinic employing biochemical and biophysical cues for wound healing applications. These technologies can be integrated with smart biomaterials to deliver therapeutic agents to the wound tissue in a precise and controllable manner. This review provides useful guidance in understanding molecular mechanisms and signals in the healing of diabetic chronic wounds and in designing instructive biomaterials to treat them.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".