Engineered Heart Tissue Model of Diabetic Myocardium
Bibliographic record
Abstract
Myocardial infarction resulting in irreversible loss of cardiomyocytes (CMs) is a leading cause of heart failure. Previously, we reported an in vitro test-bed for screening cell integration between injected test cells and host CM using the engineered heart tissue as a recipient. The objective of this study is to expand our system to diabetic cardiomyopathy conditions. Patients with diabetes show dysfunction of CMs independent of myocardial infarction, indicating that diabetes directly affects CMs. However, the underlying mechanisms are not fully understood, and developing a diabetic CM test-bed could enable drug screening studies specific to the diabetic heart. Diabetic cardiac conditions were mimicked by cultivating neonatal rat CMs seeded onto collagen scaffolds in normal or high glucose with or without insulin. Our results show that high glucose conditions, which mimic diabetic hearts, display poor electrical properties. Gene expression profiles from diabetic, adult, and neonatal rat hearts as well as engineered heart tissues under different conditions were compared. The diabetic rat heart and high glucose conditions increased the ratio of myosin heavy-chain isoform β to α indicative of diseased states; thus, this model system captures some molecular aspects of diabetic cardiomyopathy. Moreover, thiazolidinedione diabetic drug treatment improved electrical excitabilities and exhibited anti-apoptotic effects.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".