M-CSF-stimulated myeloid cells can convert into epithelial cells to participate in re-epithelialization and hair follicle regeneration during skin wound healing
Bibliographic record
Abstract
Abstract Skin wound healing is a complex process which requires the interaction of many cell types and mediators in a highly sophisticated temporal sequence. Myeloid cells compose a significant proportion of the inflammatory cells recruited to a wound site and play important roles in clearance of damaged tissue and microorganisms. Myeloid cells have also been suggested to convert into fibro-blast-like cells and endothelial cells in participation of wound healing process. However, whether myeloid cells in wound skin can convert into epithelial cells and contribute to re-epithelialization and skin appendage regeneration is still unclear. In this study, we performed double immunofluorescent staining with antibodies for hematopoietic cells and keratinocytes as well as cell tracing technique to investigate hematopoietic cell conversion. The result show that during the healing process, some of the CD45-positive hematopoietic cells are overlapped with keratin 14, the markers of keratinocytes. Further, CD11b-positive myeloid cells seem the origin of converted epithelial cells. To confirm these results, we culture CD11b-posiitve myeloid cells from mouse splenocytes in a medium containing macrophage colony-stimulating factor (M-CSF) and dermal injection of these cells into the healthy skin when punch biopsy is created in mouse skin. Tracing injected labeled splenocyte-derived myeloid cells in skin, we confirm that myeloid cells able to convert into keratinocytes in repaired skin. Furthermore, our results from in vivo experiments provide new information on contribution of myeloid cells in hair follicle regeneration. In conclusion, this work highlights the myeloid cell contributions in wound repair and hair follicle regeneration in mice through conversion of M-CSF-stimulated CD11b+ myeloid cells into epithelial cells.
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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".