Angiogenesis in Calcium Phosphate Scaffolds by Inorganic Copper Ion Release
Bibliographic record
Abstract
Angiogenesis in a tissue-engineered device may be induced by incorporating growth factors (e.g., vascular endothelial growth factor [VEGF]), genetically modified cells, and=or vascular cells. It represents an important process during the formation and repair of tissue and is essential for nourishment and supply of reparative and immunological cells. Inorganic angiogenic factors, such as copper ions, are therefore of interest in the fields of regenerative medicine and tissue engineering due to their low cost, higher stability, and potentially greater safety compared with recombinant proteins or genetic engineering approaches. The purpose of this study was to compare tissue responses to 3D printed macroporous bioceramic scaffolds implanted in mice that had been loaded with either VEGF or copper sulfate. These factors were spatially localized at the end of a single macropore some 7 mm from the surface of the scaffold. Controls without angiogenic factors exhibited only poor tissue growth within the blocks; in contrast, low doses of copper sulfate led to the formation of microvessels oriented along the macropore axis. Further, wound tissue ingrowth was particularly sensitive to the quantity of copper sulfate and was enhanced at specific concentrations or in combination with VEGF. The potential to accelerate and guide angiogenesis and wound healing by copper ion release without the expense of inductive protein(s) is highly attractive in the area of tissue-engineered bone and offers significant future potential in the field of regenerative biomaterials.
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) | 0.000 | 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".