Endothelial extracellular vesicles enhance vascular self-assembly in engineered human cardiac tissues
Why this work is in the frame
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Bibliographic record
Abstract
The fabrication of complex and stable vasculature in engineered cardiac tissues represents a significant hurdle towards building physiologically relevant models of the heart. Here, we implemented a 3D model of cardiac vasculogenesis, incorporating endothelial cells (EC), stromal cells, and human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (CM) in a fibrin hydrogel. The presence of CMs disrupted vessel formation in 3D tissues, resulting in the upregulation of endothelial activation markers and altered extracellular vesicle (EV) signaling in engineered tissues as determined by the proteomic analysis of culture supernatant. miRNA sequencing of CM- and EC-secreted EVs highlighted key EV-miRNAs that were postulated to play differing roles in cardiac vasculogenesis, including the let-7 family and miR-126-3p in EC-EVs. In the absence of CMs, the supplementation of CM-EVs to EC monolayers attenuated EC migration and proliferation and resulted in shorter and more discontinuous self-assembling vessels when applied to 3D vascular tissues. In contrast, supplementation of EC-EVs to the tissue culture media of 3D vascularized cardiac tissues mitigated some of the deleterious effects of CMs on vascular self-assembly, enhancing the average length and continuity of vessel tubes that formed in the presence of CMs. Direct transfection validated the effects of the key EC-EV miRNAs let-7b-5p and miR-126-3p in improving the maintenance of continuous vascular networks. EC-EV supplementation to biofabricated cardiac tissues and microfluidic devices resulted in tissue vascularization, illustrating the use of this approach in the engineering of enhanced, perfusable, microfluidic models of the myocardium.
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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 | 0.000 | 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 it