Facilitating tissue infiltration and angiogenesis in a tubular collagen scaffold
Bibliographic record
Abstract
Among different strategies to provide blood supply to tissue-engineered devices and implants, the use of arteriovenous loops and bundles has been proposed. The aim of this study was to compare the vascularization and healing processes that took place in a one-end closed tubular collagen-based scaffold at different implantation sites in mice. These conditions were in the presence or absence of heparin and/or bone marrow cells. By 30 days, very few cell infiltrations were observed in the dorsal subcutaneous and peritoneal implants at any conditions; however, the presence of heparin and bone marrow cells improved cell infiltration toward an inflammatory reaction. The insertion of an arteriovenous bundle into the central cavity of the scaffold resulted in partial wound tissue infiltration in the control scaffolds implanted subcutaneously in the hind limb. In similar conditions, the presence of bone marrow cells and heparin resulted in dense wound tissue with numerous capillaries and a significant amount of newly deposited collagen fibers. The design of a central cavity in a porous scaffold with one closed end may facilitate invasion from the central part of the implant toward the implant wall. In addition, the presence of both a vascular component and stem/progenitor cells may lead to a vascularized implant while limiting the inflammatory reaction.
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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".