Role of keratinocyte–fibroblast cross‐talk in development of hypertrophic scar
Bibliographic record
Abstract
The ability to generate or repair injured tissue is essential to the continuity of human life. As in all other organs, wound healing in the skin is a dynamic process involving tissue response to different types of insults. This process involves a continuous sequence of signals and responses in which platelets, fibroblasts, epithelial, endothelial, and immune cells come together outside their usual domains to orchestrate a very complex event that results in tissue repair. These signals, which are mainly growth factors and cytokines, orchestrate the initiation, continuation, and termination of wound healing. An imbalance in the synthesis and release of these cytokines and growth factors at the wound site, therefore, may result in either retarded wound healing, as is seen in diabetic patients and the elderly population, or overhealing wounds such as fibroproliferative disorders frequently seen following surgical incision, traumatic wounds, and severe electrical and thermal injury. In general, regardless of the site of injury, in any phase of the dynamic healing process, a fine balance between synthesis of extracellular matrix and degradation by a large family of enzymes, known as matrix metalloproteinases, is required for maintaining the structural integrity of healing tissue. The availability of new models such as organotypic co-culture systems have allowed us to gain new insight into the cell-cell interactions at both cellular and molecular levels. Recent evidence indicates that mesenchymal-epithelial interactions play a critical role in regulation of skin homeostasis and this cross-talk is mediated by soluble factors acting as autocrine/paracrine regulators of fibroblast and keratinocyte growth, function, and differentiation. In this review we address the question of how keratinocyte-fibroblast interaction plays a role in controlling the expression of key extracellular matrix molecules such as matrix metalloproteinases, which are critical in the healing process following any types of insults to the skin.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".