Bone Regeneration <i>via</i> a Mineral Substrate and Induced Angiogenesis
Bibliographic record
Abstract
Angiogenesis and biomineral substrates play major roles in bone development and regeneration. We hypothesized that macroporous scaffolds of biomineralized 85:15 poly(lactide-co-glycolide), which locally release vascular endothelial growth factor-165 (VEGF), would direct simultaneous regeneration of bone and vascular tissue. The presence of a bone-like biomineral substrate significantly increased regeneration of osteoid matrix (32 +/- 7% of total tissue area; mean +/- SD; p < 0.05) and mineralized tissue (14 +/- 2%; P < 0.05) within a rat cranium critical defect compared with a non-mineralized polymer scaffold (19 +/- 8% osteoid and 10 +/- 2% mineralized tissue). Further, the addition of VEGF to a mineralized substrate significantly increased the generation of mineralized tissue (19 +/- 4%; P < 0.05) compared with mineralized substrate alone. This appeared to be due to a significant increase in vascularization throughout VEGF-releasing scaffolds (52 +/- 9 vessels/mm(2); P < 0.05) compared with mineralized scaffolds without VEGF (34 +/- 4 vessels/mm(2)). Surprisingly, there was no significant difference in total osteoid between the two samples, suggesting that increased vascularization enhances mineralized tissue generation, but not necessarily osteoid formation. These results indicate that induced angiogenesis can enhance tissue regeneration, supporting the concept of therapeutic angiogenesis in tissue-engineering strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".