Identification of fibrocytes in postburn hypertrophic scar
Bibliographic record
Abstract
Fibrocytes are a unique leukocyte subpopulation implicated in wound healing. They are derived from peripheral blood mononuclear cells, display fibroblast-like properties, and synthesize extracellular matrix macromolecules. This study investigated whether fibrocytes are present in healing burn wounds and whether the number of fibrocytes in tissue correlates with the degree of burn injury and the development of hypertrophic scar. Proteins extracted from cultured fibrocytes and nonadherent lymphocytes were found to be similar using two-dimensional gel electrophoresis and quite distinct from those obtained from fibroblasts. However, one protein, identified as leukocyte-specific protein 1 using mass spectrometric peptide mapping, was found in significantly larger amounts in fibrocytes than in lymphocytes but was undetectable in fibroblasts. Double immunostaining with antibodies to leukocyte-specific protein-1 and to the N-terminal propeptide of type I collagen was performed on cryosections of hypertrophic scar, mature scar, and normal skin. Fibrocytes were seen in scar tissue as dual-labeled spindle-shaped cells but were absent from normal skin. Moreover, the number of fibrocytes was higher in hypertrophic than in mature scar tissue. We conclude that fibrocytes, which have been reported to be antigen-presenting cells, are recruited to wounds following extensive burn injury and could potentially upregulate the inflammatory response and synthesize collagen and other matrix macromolecules, thus contributing to the development of hypertrophic scarring.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".