Spatiotemporal tracking of cells in tissue-engineered cardiac organoids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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