Biomaterials for immunomodulation in wound healing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it