Interfacial self-assembly of endothelial cells toward angiogenic network formation in the composite hydrogel culture systems
Bibliographic record
Abstract
Fabrication of a pre-vascularized tissue in vitro is an extensive research activity. The idea behind this approach is that a network of newly formed micro-vessels may be engineered in vitro by the seeding of a scaffold with endothelial cells. To this aim, understanding the effect of physicochemical properties of the scaffolding material and the method of cell seeding, in regulating endothelial cells’ behavior in the in vitro constructs, is an emerging requirement. In this study, the effect of interfacial self-assembly and contact guidance for the endothelial cell behavior and angiogenic network formation have been studied. This has been done by the fabrication of in vitro three-dimensional tissue constructs, using multilayer surface-modified polymer fibers, and two different methods of cell seeding by human umbilical vein endothelial cells. In the first method, human umbilical vein endothelial cells, fibers, and fibrin gel matrix were combined simultaneously. In the second method, the human umbilical vein endothelial cells and fibers, having various surface coatings, were sandwiched between two layers of fibrin gel matrix with or without fibroblast cell monolayer over the fibrin gel. In the optimal conditions, the effect of fibers in conjunction with the interfacial self-assembly enhanced a tube-like and interconnected network structure formation. This design could therefore have a major impact in the generation of the pre-vascularized tissue-engineered constructs.
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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".