Development of Neovasculature in Axially Vascularized Calcium Phosphate Cement Scaffolds
Bibliographic record
Abstract
Augmenting the vascular supply to generate new tissues, a crucial aspect in regenerative medicine, has been challenging. Recently, our group showed that calcium phosphate can induce the formation of a functional neo-angiosome without the need for microsurgical arterial anastomosis. This was a preclinical proof of concept for biomaterial-induced luminal sprouting of large-diameter vessels. In this study, we investigated if sprouting was a general response to surgical injury or placement of an inorganic construct around the vessel. Cylindrical biocement scaffolds of differing chemistries were placed around the femoral vein. A contrast agent was used to visualize vessel ingrowth into the scaffolds. Cell populations in the scaffold were mapped using immunohistochemistry. Calcium phosphate scaffolds induced 2.7-3 times greater volume of blood vessels than calcium sulphate or magnesium phosphate scaffolds. Macrophage and vSMC populations were identified that changed spatially and temporally within the scaffold during implantation. NLRP3 inflammasome activation peaked at weeks 2 and 4 and then declined; however, IL-1β expression was sustained over the course of the experiment. IL-8, a promoter of angiogenesis, was also detected, and together, these responses suggest a role of sterile inflammation. Unexpectedly, the effect was distinct from an injury response as a result of surgical placement and also was not simply a foreign body reaction as a result of placing a rigid bioceramic next to a vein, since, while the materials tested had similar microstructures, only the calcium phosphates tested elicited an angiogenic response. This finding then reveals a potential path towards a new strategy for creating better pro-regenerative biomaterials.
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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".