Fibrosis in burns: an overview of mechanisms and therapies
Bibliographic record
Abstract
Scar development remains a common occurrence and a major healthcare challenge affecting the lives of millions of patients annually. Severe injuries to the skin, such as burns can lead to pathological wound healing patterns, often characterized by dermal fibrosis or excessive scarring, and chronic inflammation. The two most common forms of fibrotic diseases following burn trauma are hypertrophic scars (HSCs) and keloids, which severely impact the patient's quality of life. Although the cellular and molecular mechanisms are similar, HSC and keloids have several distinct differences. In this review, we discuss the different forms of fibrosis that occur postburn injury, emphasizing how the extent of burn influences scar development. Moreover, we highlight how a systemic response induced by a burn injury drives wound fibrosis, including both the role of the inflammatory response, as well as the fate of fibroblast during skin healing. Finally, we list potential therapeutics aimed at alleviating pathological scar formation. An understanding of the mechanisms of postburn fibrosis will allow us to effectively move studies from bench to bedside.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".