Melt Electrowritten Scaffold-Reinforced Affibody-Conjugated Hydrogels for Controlled Bone Morphogenetic Protein-2 Delivery
Bibliographic record
Abstract
Abstract Bone morphogenetic protein-2 (BMP-2) is clinically used to promote bone regeneration but suffers from uncontrolled release when delivered from collagen sponges, necessitating high doses that can cause adverse effects. Hydrogels offer tunable protein release but are limited by weak mechanics and poor stability during storage and handling. Here, we introduce a two-part protein delivery platform that integrates mechanical reinforcement with affinity-controlled protein release. We developed a melt electrowritten (MEW) scaffold–reinforced, affibody-conjugated polyethylene glycol maleimide (PEG-mal) hydrogel for affinity-controlled BMP-2 delivery. MEW scaffolds improved hydrogel handling, compressive resistance, and stability during lyophilization and rehydration, without altering bulk stiffness. Engineered BMP-2-specific affibodies provided affinity-based control over BMP-2 release. This ability to control BMP-2 release was preserved after lyophilization and rehydration of the hydrogels. In vivo , affibody conjugation of high-affinity affibodies to the hydrogels significantly enhanced BMP-2 retention in subcutaneous implants, while MEW reinforcement significantly increased bone volume and defect bridging in rat femoral bone defects. This affibody-conjugated, MEW scaffold-reinforced hydrogel system effectively integrates mechanical reinforcement with tunable protein-material affinity interactions, advancing hydrogel-based delivery strategies for BMP-2 and other protein therapeutics in musculoskeletal repair.
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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".