Spatiotemporal tracking of cells in tissue-engineered cardiac organoids
Bibliographic record
Abstract
Cardiac tissue engineering aims to create myocardial patches for repair of defective or damaged native heart muscle. The inclusion of non-myocytes in engineered cardiac tissues has been shown to improve the properties of cardiac tissue compared to tissues engineered from enriched populations of myocytes alone. While attempts have been made to mix non-myocytes (fibroblasts, endothelial cells) with cardiomyocytes, very little is understood about how the tissue properties are affected by varying the respective ratios of the three cell types and how these cells assemble into functional tissues with time. The goal of this study was to investigate the effects of modulating the ratios of the three cell types and to spatially and temporally track cardiac tri-cultures of cells. Primary neonatal cardiac fibroblasts and D4T endothelial cells were incubated in 5 microM CellTracker green dye and CellTracker red dye, respectively, while neonatal cardiomyocytes were labelled with 20 microg/mL DAPI. The non-myocytes were seeded either sequentially (pre-culture) or simultaneously (tri-culture) in Matrigel-coated microchannels and allowed to form organoids, as in our previous studies. We also varied the seeding percentage of cardiomyocytes while keeping the total cell number constant in an attempt to improve the functional properties of the organoids. Organoids were imaged on days 1 and 4. Endothelial cells were seen to aggregate into clusters when simultaneously tri-cultured with myocytes and fibroblasts, while pre-cultures contained elongated cells. Functional properties of organoids were improved by increasing the seeding percentage of enriched cardiomyocytes from 40% to 80%.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".