Bibliographic record
Abstract
Cardiac tissue engineering promises to create therapeutic tissue replacements for repair of diseased native myocardium. The main goals of this thesis were four-fold: 1) to evaluate cardiac tissues engineered using multiple cell types including endothelial cells (EC), fibroblasts (FB), and cardiomyocytes (CM); 2) to spatiotemporally track cells in organoids and optimize their seeding percentages for improved function; 3) to enhance vascular cord formation through sequential versus simultaneous seeding of ECs and FBs; and 4) to perform mechanistic studies to elucidate the role of soluble factors in cell-cell communication. Microscale templates fabricated from photocrosslinkable poly(ethylene glycol) diacrylate (PEG-DA) were used for all studies for rapid screening. When ECs and FBs were precultured for two days prior to seeding enriched CMs, cells self-assembled into three-dimensional, beating organoids, compared to simultaneously tricultured EC/ FB / CM which formed non-contractile clusters. Fluorescent dyes were used to label and track each cell type for up to 4 days, demonstrating an even distribution of cells within precultured organoids versus EC clustering in simultaneous triculture. When ECs were seeded first, followed by FBs 24 hours later and CMs 48 hours later, vascular-like cords formed that persisted with time in a seeding density-dependent manner. Vascular endothelial growth factor (VEGF) signaling was quantified, showing higher endogenous VEGF secretion rates in sequential preculture (16.6 ng/mL/hr) compared to undetectable VEGF secretion in simultaneous triculture. Blocking of endogenous VEGF signaling through addition of VEGF antibody / VEGFR2 inhibitor resulted in a significant decrease in mRNA and protein expression of the key cardiac gap junctional marker connexin-43. These findings provide a foundation for future work into the mechanisms governing functional cardiac tissue engineering performance and may aid in the development of novel therapies for heart failure based on growth factor signaling and engineering of vascularized, clinically relevant cardiac tissue patches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".