Comparative Studies of Fibrin-Based Engineered Vascular Tissues and Notch Signaling from Progenitor Cells
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Bibliographic record
Abstract
The main impetus of vascular tissue engineering is clinical translation, but an equally appealing and impactful use of engineered vascular tissues is as preclinical testing platforms for studying vascular disease and developing therapeutic drugs and understanding of physiologically relevant vascular biology. Developing model engineered tissues will aid in narrowing the significant knowledge gaps in functional tissue formation, which is regulated by intricate cell signaling in a three-dimensional space. In this study, we fabricated tubular engineered vascular tissues using cross-linked fibrinogen as a scaffold and nondifferentiated embryonic rat vascular smooth muscle cell line (A10 cells) and mouse embryonic multipotent mesenchymal progenitor cell line (10T1/2 cells) as model vascular cells. Fibrin gel dimensional contraction kinetics study showed that A10 cells embedded in the gel were unable to significantly contract the tissue compared to fibrin-only gels because of their undifferentiated state. In contrast, 10T1/2 cells differentiated with TGF-β1 to a vascular lineage were able to contract the tubular gel significantly owing to the contractile cytoskeletal stress fibers. Because of its vital role in vascular morphogenesis, tissue specification, and maturation, Notch signaling studies in engineered vascular tissues from A10 cells demonstrated cis-inhibition, whereas 10T1/2 cells activated Notch and its downstream targets Hes-1 and the smooth muscle α-actin genes. Taken together, this study showed that (i) contrary to the previously accepted notion, cell-type is important to gel contractions, and (ii) in engineered vascular tissues, Notch signaling is highly context-dependent, where cis-inhibition muted signal activation in A10 vascular cells, whereas Notch was fully activated in 10T1/2 cells. These findings may provide insights to fabricate functional vascular tissues.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it