Perfusion of Endothelial Capillary Structure in Tissue Engineered Constructs by <i>In Vitro</i> Inosculation
Bibliographic record
Abstract
In the present study we wanted to prove controlled perfusion of endothelial cells capillary channels in a collagen/chitosan/glycosaminoglycan (CCGAG) biopolymer inosculated on to a microfabricated device. This connection between a microfluidic structure, with one inlet and one outlet, and a tissue engineered construct made from human fibroblasts and umbilical vein endothelial cells should allow the generation of flow inside capillaries spontaneously created by endothelial cells within the biopolymer. We used many soft lithographic techniques to create the polycaprolactone‐poly (DL‐lactide‐co‐glycolide) acid microfluidic structure. The biopolymer is made by freeze‐drying the CCGAG solution, and then cells are seeded and cultured for a total of 31 days. Then the microfluidic structure and the biopolymer are cultured together in a bioreactor where degradation of the top layer of the microfluidic device allows inosculation in this model. We have shown such an inosculation phenomenon in our model by injecting fluorescent microspheres in the microfluidic device, separated the biopolymer from the device and performed confocal microscopy study on 30um thick cryostat sliced sample. Those are promising results for various applications such as tissue engineered vascularized organ substitutes and in vitro pharmacodynamics and angiogenesis studies. Financial support (NRC‐CIHR grant 66473)
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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".