The Development of High-fidelity In Vitro Cardiomyocyte Models for Physiological Investigation and Pharmaceutical Testing
Bibliographic record
Abstract
Cardiomyopathies, heart failure, and arrhythmias or conduction blockages impact millions of patients worldwide and are associated with marked increases in sudden cardiac death, decreased quality of life, and sequelae or complications. These pathologies stem from dysfunction in the contractile or conductive properties of the cardiomyocyte (CM), which is a focus of fundamental investigation, drug discovery, and therapeutic development. Separate from direct disease impact, pharmaceutical cardiotoxicity or lack of efficacy during both clinical trials and marketing is a leading reason for clinical trial failure or post-release drug recalls, ostensibly due to inadequate predictivity by existing animal or in vitro models. In terms of in vitro models, both animals and pluripotent stem cells offer sources of cardiomyocytes for such specific applications. Murine adult cardiomyocytes are a workhorse model for basic science but are physiologically delicate and degrade rapidly in culture; pluripotent stem cell-derived cardiomyocytes (PSC-CMs) offer an inexhaustible source of human CMs but are immature in all aspects of functional physiology. Furthermore, with the advent of engineered tissues and lab-on-a-chip systems, certain well-established experimental workflows to address advanced or emergent physiology in situ or ex vivo have yet to be adapted to high-throughput or microfluidic applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".