An in silico study reveals how architectural and mechanical cues jointly regulate angiogenesis and bone regeneration in 3D printed scaffolds
Bibliographic record
Abstract
The treatment of large bone defects is an unmet clinical need. 3D printed scaffolds offer a promising solution, however they are still not widely employed in clinical practice due to inconsistent healing outcomes and limited understanding of the underlying regeneration mechanisms. To address this, we developed a computer model for 3D printed scaffold-guided bone regeneration and angiogenesis. Our novel computer model successfully recapitulated the bone regeneration process within two 3D printed scaffold architectures: one comprised of microfibres of 20 μm diameter fabricated by melt electrowriting and another comprised of larger diameter fibres of 200 μm fabricated by fused deposition modelling. Thereafter, the model was employed to further assess the specific contribution of structural and mechanical cues on vascularisation and bone formation. We found that scaffolds fabricated by melt electrowriting enhanced bone formation because of the advantageous architectural features such as high surface-area-to-volume ratio, despite the lower mechanical stiffness. Additionally, their high open porosity facilitated vessel infiltration and induced mechanical strains accelerating vessel growth as compared to fused deposition modelling scaffolds. However, the small pore size on the outer surface might limit the invasion of larger vessels, which is expected to occur at the later stages of healing. Understanding how scaffold architecture and mechanical properties jointly orchestrate angiogenesis and bone formation is essential for optimising scaffold design and enhancing the regeneration of large bone defects. In silico models like the one presented in this study hold great promise for advancing scaffold design and enhancing clinical outcomes.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".