358 Can Immune Cells Become Skin Cells in Large Burn Injury?
Bibliographic record
Abstract
Upon any kind of dermal injury, keratinocytes and fibroblasts migrate from the edge of injury site to the wound site where they proliferate and promote wound healing. However, it is unlikely that these cells from the edges of large burn injury be able to migrate to a very long distance to cover the injury site. Here, we hypothesize that skin injury initiates a signal through which a subset of circulating immune cells become de-differentiated into stem like cells and these cells then become the major source of skin cells during the healing process. The potential role of releasable factors from the proliferating fibroblasts on trans-differentiation of immune cells to multi-potent stem like cells was evaluated by culturing immune cells in fibroblast conditioned medium for 6 days. Cells were then examined for their morphology and the expression of a set of stem cell markers and their capacity to further differentiation into other cell types. The finding showed that culturing a subset of blood derived immune cells have the capacity to be de-differentiated into fibroblast like cells when co-cultured with proliferating fibroblasts. These cells were then identified to be fibroblast like cells with capacity to express a panel of stem cell markers such as alkaline phosphatase, formation of embryonic bodies, and expression of other pluripotent stem cells markers. Further, these cells showed a capacity to further differentiate into fibroblasts, osteocytes, adipocytes, smooth muscle cells, endothelial cells, neural cells. This finding was further confirmed in a mouse model by showing an easy detection of SSEA-1, a main marker for PSCs in wounded but not in normal tissues. These data confirm that a subset of circulating immune cells have the capacity to become de-differentiated into PSCs within the wound environment and that these cells become the main source of skin cells in large wounds including burn. Identifying the factors responsible for conversion of immune cells to skin cells would make it possible to topically apply these factors to promote the healing and reduce inflammation in large burn injury.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".