A Cell‐Based Self‐Assembly Approach for the Production of Human Osseous Tissues from Adipose‐Derived Stromal/Stem Cells
Bibliographic record
Abstract
Achieving optimal bone defect repair is a clinical challenge driving intensive research in the field of bone tissue engineering. Many strategies focus on seeding graft materials with progenitor cells prior to in vivo implantation. Given the benefits of closely mimicking tissue structure and function with natural materials, the authors hypothesize that under specific culture conditions, human adipose-derived stem/stromal cells (hASCs) can solely be used to engineer human reconstructed osseous tissues (hROTs) by undergoing osteoblastic differentiation with concomitant extracellular matrix production and mineralization. Therefore, the authors are developing a self-assembly methodology allowing the production of such osseous tissues. Three-dimensional (3D) tissues reconstructed from osteogenically-induced cell sheets contain abundant collagen type I and are 2.7-fold less contractile compared to non-osteogenically induced tissues. In particular, hROT differentiation and mineralization is reflected by a greater amount of homogenously distributed alkaline phosphatase, as well as higher calcium-containing hydroxyapatite (P < 0.0001) and osteocalcin (P < 0.0001) levels compared to non-induced tissues. Taken together, these findings show that hASC-driven tissue engineering leads to hROTs that demonstrate structural and functional characteristics similar to native osseous tissue. These highly biomimetic human osseous tissues will advantageously serve as a platform for molecular studies as well as for future therapeutic in vivo translation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".