Biomaterials for immunomodulation in wound healing
Bibliographic record
Abstract
The substantial economic impact of non-healing wounds, scarring, and burns stemming from skin injuries is evident, resulting in a financial burden on both patients and the healthcare system. This review paper provides an overview of the skin's vital role in guarding against various environmental challenges as the body's largest protective organ and associated developments in biomaterials for wound healing. We first introduce the composition of skin tissue and the intricate processes of wound healing, with special attention to the crucial role of immunomodulation in both acute and chronic wounds. This highlights how the imbalance in the immune response, particularly in chronic wounds associated with underlying health conditions such as diabetes and immunosuppression, hinders normal healing stages. Then, this review distinguishes between traditional wound-healing strategies that create an optimal microenvironment and recent peptide-based biomaterials that modulate cellular processes and immune responses to facilitate wound closure. Additionally, we highlight the importance of considering the stages of wounds in the healing process. By integrating advanced materials engineering with an in-depth understanding of wound biology, this approach holds promise for reshaping the field of wound management and ultimately offering improved outcomes for patients with acute and chronic 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) | 0.000 | 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) | 0.002 | 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".