Expression of Endo180 is spatially and temporally regulated during wound healing
Bibliographic record
Abstract
AIMS: Interactions of cells with the extracellular matrix are important for normal wound healing and may play a role in scar formation. Remarkably, wound healing in human gingiva does not result in scar formation and serves as a model for wound regeneration. Endo180 (CD280) is a cell surface receptor that has novel functions to regulate cell migration and bind and internalize collagens that are key processes in wound healing. The aim of this study was to examine the expression of Endo180 during gingival wound regeneration. METHODS AND RESULTS: Biopsies were collected from normal human gingiva and 1-60 days after wounding and expression of Endo180 was analysed by immunostaining. Expression of Endo180 by cultured fibroblasts and keratinocytes was studied by immunoblotting and semiquantitative reverse transcriptase-polymerase chain reaction. In normal gingiva, Endo180 was expressed by basal epithelial cells, fibroblasts, myofibroblasts, pericytes, macrophages and endothelial cells. In wounds, Endo180 expression was spatiotemporally increased in the migrating and differentiating wound epithelium, in subsets of myofibroblasts, pericytes, macrophages and endothelial cells. Growth factors involved in wound healing up-regulated the expression of Endo180 in keratinocytes and fibroblasts. CONCLUSIONS: The findings suggest that Endo180 plays a role in re-epithelialization and connective tissue remodelling during wound regeneration.
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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 | 0.000 |
| 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".