Loss of Epithelial Hypoxia-Inducible Factor Prolyl Hydroxylase 2 Accelerates Skin Wound Healing in Mice
Bibliographic record
Abstract
Skin wound healing in mammals is a complex, multicellular process that depends on the precise supply of oxygen. Hypoxia-inducible factor (HIF) prolyl hydroxylase 2 (PHD2) serves as a crucial oxygen sensor and may therefore play an important role during reepithelialization. Hence, this study was aimed at understanding the role of PHD2 in cutaneous wound healing using different lines of conditionally deficient mice specifically lacking PHD2 in inflammatory, vascular, or epidermal cells. Interestingly, PHD2 deficiency only in keratinocytes and not in myeloid or endothelial cells was found to lead to faster wound closure, which involved enhanced migration of the hyperproliferating epithelium. We demonstrate that this effect relies on the unique expression of β3-integrin in the keratinocytes around the tip of the migrating tongue in an HIF1α-dependent manner. Furthermore, we show enhanced proliferation of these cells in the stratum basale, which is directly related to their attenuated transforming growth factor β signaling. Thus, loss of the central oxygen sensor PHD2 in keratinocytes stimulates wound closure by prompting skin epithelial cells to migrate and proliferate. Inhibition of PHD2 could therefore offer novel therapeutic opportunities for the local treatment of cutaneous wounds.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".