Expression of fibroblast growth factors and their receptors during full-thickness skin wound healing in young and aged mice
Bibliographic record
Abstract
The highly ordered process of wound healing involves the coordinated regulation of cell proliferation and migration and tissue remodeling, predominantly by polypeptide growth factors. Consequently, the slowing of wound healing that occurs in the aged may be related to changes in the activity of these various regulatory factors. To gain additional insight into these issues, we quantified the absolute copy numbers of mRNAs encoding all the fibroblast growth factors (FGFs), their receptors (FGFRs) and two other growth factors in the dorsal skin of young and aged mice during the healing of full-thickness skin excisional wounds. In young adult mice (8 weeks old), FGF7, FGF10 and FGF22 mRNAs were all strongly expressed in healthy skin, and levels of FGF7 and 10 but not 22 increased 2- to 3.5-fold over differing time courses after wounding. The levels of FGF9, 16, 18 and especially 23 mRNAs were moderate or low in healthy skin but increased 2- to 33-fold after wounding. Among the four FGFRs, expression of only FGFR1 mRNA was augmented during wound healing. Expression of transforming growth factor-beta and hepatocyte growth factor was also high in healthy skin and was upregulated during healing. Notably, in aged mice (35 weeks old), where healing proceeded more slowly than in the young, both the basal and wound-induced mRNA expression of most of these genes was reduced. While these results confirm the established notion that FGFR2 IIIB ligands (FGF7 and FGF10) are important for wound healing, they also suggest that decreased expression of multiple FGF ligands contributes to the slowing of wound healing in aged mice and indicate the potential importance of further study of the involvement of FGF9, 16, 18 and 23 in the wound healing process.
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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 | 0.001 | 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".