Proteoglycan 4 ( <scp>PRG4</scp> ) treatment improves skin wound healing in a porcine model
Bibliographic record
Abstract
Proteoglycan 4 (PRG4) is a boundary lubricant originally identified in articular cartilage and has been since shown to have immunomodulation and antifibrotic properties. Previously, we have demonstrated that recombinant human (rh)PRG4 treatment accelerates auricular cartilage injury closure through an inhibition of the fibrotic response, and promotion of tissue regeneration in mice. The purpose of the current study was to examine the effects of rhPRG4 treatment (vs. a DMSO carried control) on full-thickness skin wound healing in a preclinical porcine model. Our findings suggest that while rhPRG4 did not significantly accelerate nor impede full-thickness skin wound closure, it did improve repair quality by decreasing molecular markers of fibrosis and increasing re-vascularization. We also demonstrated that rhPRG4 treatment increased dermal adipose tissue during the healing process specifically by retaining adipocytes in the wound area but did not inhibit lipolysis. Overall, the results of the current study have demonstrated that rhPRG4 acts as antifibrotic agent and regulates dermal adipose tissue during the healing processes resulting in a tissue with a trajectory that more resembles the native skin vs. a fibrotic patch. This study provides strong rationale to examine if rhPRG4 can improve regeneration in human 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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".