Triphasic 3D In Vitro Model of Bone-Tendon-Muscle Interfaces to Study Their Regeneration
Bibliographic record
Abstract
The transition areas between different tissues, known as tissue interfaces, have limited ability to regenerate after damage, which can lead to incomplete healing. Previous studies focussed on single interfaces, most commonly bone-tendon and bone-cartilage interfaces. Herein, we develop a 3D in vitro model to study the regeneration of the bone-tendon-muscle interface. The 3D model was prepared from collagen and agarose, with different concentrations of hydroxyapatite to graduate the tissues from bones to muscles, resulting in a stiffness gradient. This graduated structure was fabricated using indirect 3D printing to provide biologically relevant surface topographies. MG-63, human dermal fibroblasts, and Sket.4U cells were found suitable cell models for bones, tendons, and muscles, respectively. The biphasic and triphasic hydrogels composing the 3D model were shown to be suitable for cell growth. Cells were co-cultured on the 3D model for over 21 days before assessing cell proliferation, metabolic activity, viability, cytotoxicity, tissue-specific markers, and matrix deposition to determine interface formations. The studies were conducted in a newly developed growth chamber that allowed cell communication while the cell culture media was compartmentalised. The 3D model promoted cell viability, tissue-specific marker expression, and new matrix deposition over 21 days, thereby showing promise for the development of new interfaces.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".