Assessment of fibrin-collagen co-gels for generating microvessels <i>ex vivo</i> using endothelial cell-lined microfluidics and multipotent stromal cell (MSC)-induced capillary morphogenesis
Bibliographic record
Abstract
is the generation of perfusable microvessels of varying diameters. In this work, we take the approach of using hydrogel-based microfluidics seeded with endothelial cells (ECs) to form small artery/vein-like vessels, in conjunction with using the self-assembly behavior of ECs to form capillary-like vessels when co-cultured with multipotent stromal cells (MSCs). In exploring this approach, we focused on investigating collagen, fibrin, and various collagen-fibrin co-gel formulations for their potential suitability as serving as scaffold materials by surveying their angiogencity and mechanical properties. Fibrin and co-gels successfully facilitated multicellular EC sprouting, whereas collagen elicited a migration response of individual ECs, unless supplemented with the protein kinase C (PKC)-activator, phorbol 12-myristate 13-acetate. Collagen scaffolds were also found to severely contract when embedded with mesenchymal cells, but this contraction could be abrogated with the addition of fibrin. Increasing collagen content within co-gel formulations, however, imparted a higher compressive modulus and allowed for the reliable formation of intact hydrogel-based microchannels which could then be perfused. Given the bioactivity and mechanical benefits of fibrin and collagen, respectively, collagen-fibrin co-gels are a promising scaffold option for generating vascularized tissue 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) | 0.000 | 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".