Biofabrication enables efficient interrogation and optimization of sequential culture of endothelial cells, fibroblasts and cardiomyocytes for formation of vascular cords in cardiac tissue engineering
Bibliographic record
Abstract
We previously reported that preculture of fibroblasts (FBs) and endothelial cells (ECs) prior to cardiomyocytes (CMs) improved the structural and functional properties of engineered cardiac tissue compared to culture of CMs alone or co-culture of all three cell types. However, these approaches did not result in formation of capillary-like cords, which are precursors to vascularization in vivo. Here we hypothesized that seeding the ECs first on Matrigel and then FBs 24 h later to stabilize the endothelial network (sequential preculture) would enhance cord formation in engineered cardiac organoids. Three sequential preculture groups were tested by seeding ECs (D4T line) at 8%, 15% and 31% of the total cell number on Matrigel-coated microchannels and incubating for 24 h. Cardiac FBs were then seeded (32%, 25% and 9% of the total cell number, respectively) and incubated an additional 24 h. Finally, neonatal rat CMs (60% of the total cell number) were added and the organoids were cultivated for seven days. Within 24 h, the 8% EC group formed elongated cords which eventually developed into beating cylindrical organoids, while the 15% and 31% EC groups proliferated into flat EC monolayers with poor viability. Excitation threshold (ET) in the 8% EC group (3.4 ± 1.2 V cm(-1)) was comparable to that of the CM group (3.3 ± 1.4 V cm(-1)). The ET worsened with increasing EC seeding density (15% EC: 4.4 ± 1.5 V cm(-1); 31% EC: 4.9 ± 1.5 V cm(-1)). Thus, sequential preculture promoted vascular cord formation and enhanced architecture and function of engineered heart tissues.
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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".