Target-seeking antifibrotic compound enhances wound healing and suppresses scar formation in mice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it