Accelerating skin wound healing by M-CSF through generating SSEA-1 and -3 stem cells in the injured sites
Bibliographic record
Abstract
Wound healing is a complicated process requiring the collaborative efforts of different cell lineages. Our recent studies have found that one subset of hematopoietic cells can be induced to dedifferentiate into multipotent stem cells by means of a proliferating fibroblast releasable factor, M-CSF. Understanding the importance of stem cells on skin wound healing, here we evaluate the biological significance of M-CSF on skin wound healing. In an in vivo mouse skin excisional wound model, we found that SSEA-positive stem cells were present in wounded but not normal skin. After isolating skin cells from either normal or wounded skin by collagenase digestion, and analyzing the SSEA-1 positive cells by flow cytometry, we found a significant increase in the number of SSEA-1 positive cells in wounded skin. Topical application of M-CSF in skin wounds accelerated healing remarkably, while application of M-CSF-neutralizing antibody slowed wound healing. Furthermore, injection of EGFP-labeled hematopoietic cell-derived stem cells generated from M-CSF treated splenocytes resulted in EGFP-labeled cells being enriched in the skin wound site and further differentiated into functional organ-specific cells. Together, these data demonstrated that M-CSF makes a significant contribution to the healing process by inducing hematopoietic cell dedifferentiation into stem cells.
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 | 0.000 | 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".