Multi-objective optimization of an origami inspired super-expandable scaffold for distraction osteogenesis
Bibliographic record
Abstract
The extended bone consolidation period in distraction osteogenesis (DO) is a known clinical challenge. Tissue-engineered scaffolds (TIS) have shown promise in enhancing bone regeneration, but conventional designs are not suitable for the large deformations required in DO. This study presents an origami-inspired super-expandable scaffold (OISES) tailored for DO. A Bayesian machine learning platform was used to predict key structural and mechanical properties influencing bone regeneration, followed by multi-objective optimization. Selected designs were fabricated and tested both virtually and experimentally. The optimized OISES configurations maintain porosity near 80 % and high surface area-to-volume ratios—parameters associated with osteogenesis—while exhibiting large recoverable deformations. Simulations revealed consistent mechanical behavior and strain localization, along with a deformation-dependent increase in permeability and decrease in wall shear stress during scaffold expansion. This transition supports a shift from early mechanical stimulation to enhanced fluid and nutrient transport, mirroring the evolving biological needs during early-stage bone healing. These findings suggest that OISES scaffolds can dynamically adapt to the mechanical and biological demands of DO, offering a new class of tunable scaffolds optimized for patient-specific treatments.
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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".