Integrating Melt Electrowriting and Fused Deposition Modeling to Fabricate Hybrid Scaffolds Supportive of Accelerated Bone Regeneration
Bibliographic record
Abstract
Emerging additive manufacturing (AM) strategies can enable the engineering of hierarchal scaffold structures for guiding tissue regeneration. Here, the advantages of two AM approaches, melt electrowriting (MEW) and fused deposition modelling (FDM), are leveraged and integrated to fabricate hybrid scaffolds for large bone defect healing. MEW is used to fabricate a microfibrous core to guide bone healing, while FDM is used to fabricate a stiff outer shell for mechanical support, with constructs being coated with pro-osteogenic calcium phosphate (CaP) nano-needles. Compared to MEW scaffolds alone, hybrid scaffolds prevent soft tissue collapse into the defect region and support increased vascularization and higher levels of new bone formation 12 weeks post-implantation. In an additional group, hybrid scaffolds are also functionalized with BMP2 via binding to the CaP coating, which further accelerates healing and facilitates the complete bridging of defects after 12 weeks. Histological analyses demonstrate that such scaffolds support the formation of well-defined annular bone, with an open medullary cavity, smooth periosteal surface, and no evidence of abnormal ectopic bone formation. These results demonstrate the potential of integrating different AM approaches for the development of regenerative biomaterials, and in particular, demonstrate the enhanced bone healing outcomes possible with hybrid MEW-FDM constructs.
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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".