M-CSF-stimulated myeloid cells can convert into epithelial cells to participate in re-epithelialization and hair follicle regeneration during dermal wound healing
Bibliographic record
Abstract
Dermal wound healing is a complex process which requires the interaction of many cell types and mediators in a highly sophisticated temporal sequence. Myeloid cells which compose of a significant proportion of the inflammatory cells infiltrate to the to a wound site where they play important roles in clearance of damaged tissue and microorganisms. Myeloid cells have the capacity to be converted into fibroblast-like cells and endothelial cells during wound healing process. However, whether myeloid cells in wounds can convert into epithelial cells where they contribute to healing process is not clear. 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 showed that during the healing process, some of the CD45-positive hematopoietic cells also expressed keratin 14, a marker for keratinocytes. Further, double immunofluorescent staining in dermal wounds, using CD11b and K14 antibodies indicated that CD11b-positive myeloid cells were the origin of newly generated epithelial cells. Through tracing injected labeled splenocyte-derived myeloid cells in skin, we confirmed that myeloid cells were able to convert into keratinocytes in repaired skin. Furthermore, our results from in vivo experiments provided 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 through conversion of M-CSF-stimulated CD11b-positive myeloid cells into epithelial cells in a murine model.
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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) | 0.001 | 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".