Adaptive growth factor delivery from a polyelectrolyte coating promotes synergistic bone tissue repair and reconstruction
Bibliographic record
Abstract
Traumatic wounds and congenital defects that require large-scale bone tissue repair have few successful clinical therapies, particularly for craniomaxillofacial defects. Although bioactive materials have demonstrated alternative approaches to tissue repair, an optimized materials system for reproducible, safe, and targeted repair remains elusive. We hypothesized that controlled, rapid bone formation in large, critical-size defects could be induced by simultaneously delivering multiple biological growth factors to the site of the wound. Here, we report an approach for bone repair using a polyelectrolye multilayer coating carrying as little as 200 ng of bone morphogenetic protein-2 and platelet-derived growth factor-BB that were eluted over readily adapted time scales to induce rapid bone repair. Based on electrostatic interactions between the polymer multilayers and growth factors alone, we sustained mitogenic and osteogenic signals with these growth factors in an easily tunable and controlled manner to direct endogenous cell function. To prove the role of this adaptive release system, we applied the polyelectrolyte coating on a well-studied biodegradable poly(lactic-co-glycolic acid) support membrane. The released growth factors directed cellular processes to induce bone repair in a critical-size rat calvaria model. The released growth factors promoted local bone formation that bridged a critical-size defect in the calvaria as early as 2 wk after implantation. Mature, mechanically competent bone regenerated the native calvaria form. Such an approach could be clinically useful and has significant benefits as a synthetic, off-the-shelf, cell-free option for bone tissue repair and restoration.
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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 | 0.000 | 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 | 0.000 |
| 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 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".