Monitoring angiogenesis in soft‐tissue engineered constructs for calvarium bone regeneration: an <i>in vivo</i> longitudinal DCE‐MRI study
Bibliographic record
Abstract
Tissue engineering is a promising technique for bone repair and can overcome the major drawbacks of conventional autogenous bone grafting. In this in vivo longitudinal study, we proposed a new tissue-engineering paradigm: inserting a biological soft-tissue construct within the bone defect to enhance angiogenesis for improved bone regeneration. The construct acts as a resorbable scaffold to support desired angiogenesis and cellular activity and as a vector of vascular endothelial growth factor, known to promote both vessel and bone growth. Dynamic contrast- enhanced magnetic resonance imaging was performed to investigate and characterize angiogenesis necessary for bone formation following the proposed paradigm of inserting a VEGF-impregnated tissue-engineered construct within the critical-sized calvarial defect in the membranous parietal bone of the rabbit. Results show that a model-free quantitative approach, the normalized initial area under the curve metric, provides sensitive and reproducible measures of vascularity that is consistent with known temporal evolution of angiogenesis during bone regeneration.
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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".