Harnessing Advances in Bone Tissue Engineering for Design of Bone‐on‐Chip Systems
Bibliographic record
Abstract
Organ-on-Chip systems demonstrate significant potential as next-generation models to study human (patho)physiology and to assess new therapies. Whereas on-chip models of soft tissues and organs have progressed substantially over the past decade, the development of bone-on-chip (BoC) systems remains comparatively slower. This slower progress stems from the structural and functional complexity of bone tissue, which hampers efforts to recapitulate bone (patho)physiology on-chip. Advances in bone tissue engineering (BTE) now provide opportunities regarding i) innovative biomaterials design strategies and ii) advanced bioengineering tools, enabling a closer replication of the architectural and functional complexity of native bone. However, these technological advances have primarily resulted in improved regenerative therapies, while also offering opportunities for innovative BoC designs. This perspective article identifies key requirements for BoCs, explores existing models and their respective advantages and limitations. Subsequently, opportunities derived from BTE are highlighted to accelerate the development of BoCs which more accurately capture crucial features of in vivo bone (patho)physiology. Finally, key considerations for BoC design are discussed, and an outlook for further developments in this emerging field is provided.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".