A Bioactive Coating Enhances Bone Allografts in Rat Models of Bone Formation and Critical Defect Repair
Bibliographic record
Abstract
Bone allografts are inferior to autografts for the repair of critical-sized defects. Prior studies have suggested that bone morphogenetic protein-2 (BMP-2) can be combined with allografts to produce superior healing. We created a bioactive coating on bone allografts using polycondensed deoxyribose isobutyrate ester (PDIB) polymer to deliver BMP-2 ± the bisphosphonate zoledronic acid (ZA) and tested its ability to enhance the functional utility of allografts in preclinical Wistar rat models. One ex vivo and two in vivo proof-of-concept studies were performed. First, PDIB was shown to be able to coat bone grafts (BGs). Second, PDIB was used to coat structural allogenic corticocancellous BG with BMP-2 ± ZA ± hydroxyapatite (HA) microparticles and compared with PDIB-coated grafts in a rat muscle pouch model. Next, a rat critical defect model was performed with treatment groups including (i) empty defect, (ii) BG, (iii) collagen sponge + BMP-2, (iv) BG + PDIB/BMP-2, and (v) BG + PDIB/BMP-2/ZA. Key outcome measures included detection of fluorescent bone labels, microcomputed tomography (CT) quantification of bone, and radiographic healing. In the muscle pouch study, BMP-2 did not increase net bone volume measured by microCT, however, fluorescent labeling showed large amounts of new bone. Addition of ZA increased BV by sevenfold (p < 0.01). In the critical defect model, allografts were insufficient to promote reliable union, however, union was achieved in collagen/BMP-2 and all BG/BMP-2 groups. Statement of clinical significance: These data support the concept that PDIB is a viable delivery method for BMP-2 and ZA delivery to enhance the bone forming potential of allografts. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2278-2286, 2019.
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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 | 0.001 |
| 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) | 0.001 | 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".