Endochondral bone tissue engineering: using cartilage to drive vascularized bone regeneration (87.6)
Bibliographic record
Abstract
There is a significant unmet clinical need to develop improved strategies to promote vascularized bone regeneration. Current bone graft technologies promote repair through direct osteogenesis by use of auto‐ or allografted bone. Critical failures associated with these therapies include osteonecrosis and poor osseointegration. The long‐term goal of this project is to develop a tissue engineering approach to promote endochondral bone formation, in which bone forms through a cartilage intermediate. Endochondral ossification is the normal mechanism by which long bones form and the majority of fractures heal. By leveraging this normal healing response, we hypothesize we can overcome the clinical problems of osteonecrosis and poor integration to produce a vascularized and integrated bone regenerate. To test this hypothesis, critical sized defects were created in mid‐diaphysis of murine tibia. Defects remained empty, or were filled with a cartilage graft, autograft or allograft bone. Our data show cartilage grafts effectively transformed into vascularized bone and integrated with the host bone to perform statistically better than allografts and equivalent to autografts. To translate this therapy we have developed a biomimetic acrylated hyaluronic acid hydrogel that contains peptide sequences for cell attachment (bone sialoprotein‐derived RGD), heparin for modulation and sequestration of growth factors, and enzymatically degradable MMP‐13 crosslinks. Hypertrophic chondrocytes encapsulated in these scaffolds mineralization during in vitro culture in basal medium. In ongoing work, these scaffolds have been transplanted into a subcutaneous model of osteonecrosis to evaluate the capacity to re‐vitalize bone. This novel approach to bone tissue engineering has the potential to improve clinical outcomes in bone regeneration. Grant Funding Source : Supported by NIH 5F32AR062469 (CSB), MTF Junior Investigator Award (CSB), UCSF CTSI Award (CSB)
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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) | 0.001 | 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".