Biomaterial‐Induction of a Transplantable Angiosome
Bibliographic record
Abstract
Abstract Creating transplantable vascular networks (angiosomes) that are fed and drained by vessels large enough to be surgically reconnected is key to harnessing the potential of regenerative medicine and advancing reconstructive surgical techniques. Currently, the only way to create a new angiosome is nontrivial and involves pressurizing a vein graft by its surgical attachment to an artery forming an arteriovenous loop (AVL). Material induction of a venous angiosome is reported, by placement of a 3D printed microporous monetite scaffold around a vein and its transplantability is further demonstrated. When the transplanted venosome is cut, it bleeds, illustrating potential reconstructive functionality. The volume of blood vessels generated by biomaterial‐induction is as great as by AVL. Direct contact of the material with the vein does not appear to be critical to luminal sprouting, and wrapping the implant in a silicone membrane significantly reduces sprouting. Pilot studies with microporous polymeric scaffolds induce far less vascular invasion. After 4 weeks, monetite scaffolds are extensively vascularized and can be transplanted to an arterial vessel. This report is significant since a lack of tools to control vascular generation is an impediment to the treatment of several conditions that give rise to tissue ischemia and tissue reconstruction.
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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".