Heparin-mediated delivery of bone morphogenetic protein-2 improves spatial localization of bone regeneration
Bibliographic record
Abstract
Supraphysiologic doses of bone morphogenetic protein-2 (BMP-2) are used clinically to promote bone formation in fracture nonunions, large bone defects, and spinal fusion. However, abnormal bone formation (i.e., heterotopic ossification) caused by rapid BMP-2 release from conventional collagen sponge scaffolds is a serious complication. We leveraged the strong affinity interactions between heparin microparticles (HMPs) and BMP-2 to improve protein delivery to bone defects. We first developed a computational model to investigate BMP-2-HMP interactions and demonstrated improved in vivo BMP-2 retention using HMPs. We then evaluated BMP-2-loaded HMPs as a treatment strategy for healing critically sized femoral defects in a rat model that displays heterotopic ossification with clinical BMP-2 doses (0.12 mg/kg body weight). HMPs increased BMP-2 retention in vivo, improving spatial localization of bone formation in large bone defects and reducing heterotopic ossification. Thus, HMPs provide a promising opportunity to improve the safety profile of scaffold-based BMP-2 delivery.
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.
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) | 0.001 | 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".