Competitive Protein Binding Influences Heparin-Based Modulation of Spatial Growth Factor Delivery for Bone Regeneration
Bibliographic record
Abstract
Tissue engineering strategies involving the in vivo delivery of recombinant growth factors are often limited by the inability of biomaterials to spatially control diffusion of the delivered protein within the site of interest. The poor spatiotemporal control provided by porous collagen sponges, which are used for the clinical delivery of bone morphogenetic protein-2 (BMP-2) for bone regeneration, has necessitated the use of supraphysiological protein doses, leading to inflammation and heterotopic ossification. This study describes a novel tissue engineering strategy to spatially control rapid BMP-2 diffusion from collagen sponges in vivo by creating a high-affinity BMP-2 sink around the collagen sponge. We designed an electrospun poly-ɛ-caprolactone nanofiber mesh containing physically entrapped heparin microparticles, which have been previously demonstrated to bind and retain large amounts of BMP-2. Nanofiber meshes containing 0.05 and 0.10 mg of microparticles/cm2 demonstrated increased BMP-2 binding and decreased BMP-2 release in vitro compared with meshes without microparticles. However, when microparticle-containing meshes were used in vivo to limit the diffusion of BMP-2 delivered by using collagen sponges in a rat femoral defect, no differences in heterotopic ossification or biomechanical properties were observed. Further investigation revealed that, although BMP-2 binding to heparin microparticles was rapid, the presence of serum components attenuated microparticle-BMP-2 binding and increased BMP-2 release in vitro. These observations provide a plausible explanation for the results observed in vivo and suggest that competitive protein binding in vivo may hinder the ability of affinity-based biomaterials to modulate growth factor delivery.
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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) | 0.001 | 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".