Target-seeking antifibrotic compound enhances wound healing and suppresses scar formation in mice
Bibliographic record
Abstract
Permanent scars form upon healing of tissue injuries such as those caused by ischemia (myocardial infarction, stroke), trauma, surgery, and inflammation. Current options in reducing scar formation are limited to local intervention. We have designed a systemically administered, target-seeking biotherapeutic for scar prevention. It consists of a vascular targeting peptide that specifically recognizes angiogenic blood vessels and extravasates into sites of injury, fused with a therapeutic molecule, decorin. Decorin prevents tissue fibrosis and promotes tissue regeneration by inhibiting TGF-β activity and by other regulatory activities. The decorin-targeting peptide fusion protein had substantially increased neutralizing activity against TGF-β1 in vitro compared with untargeted decorin. In vivo, the fusion protein selectively accumulated in wounds, and promoted wound healing and suppressed scar formation at doses where nontargeted decorin was inactive. These results show that selective targeting yields a tissue-healing and scar-reducing compound with enhanced specificity and potency. This approach may help make reducing scar formation by systemic drug delivery a feasible option for surgery and for the treatment of pathological processes in which scar formation is a problem.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".