Defining the parameter space for construction of cardiac ventricle models
Bibliographic record
Abstract
The human heart is essential for circulation of nutrients, oxygen, and signalling molecules, with the left ventricle (LV) ejecting approximately 2000 gallons of blood per day. At the μm-scale, the myocardium is comprised of diverse cell types, with multinucleated and elongated cardiomyocytes being the main driver of cardiac contractions. At the mm scale, the myocardium is composed of cardiac myofibers aligned in directions ranging from 60° to –60°. This unique fiber orientation drives the cardiac wringing motion, with the base rotating clockwise and the apex counterclockwise, to enhance the ejection fraction. Novel fabrication techniques have enabled creation of sophisticated and functionally relevant cardiac structures capturing various aspects of heart architecture, such as conical shape of the ventricle as well as multilayered and helical fiber directionality. Designing models of LV cavity faces complex challenges such as consideration of cell type and density, scaling, biomaterials of choice, fabrication technique and vascularization. Advancements in adjacent fields such as organoid biology, heart-on-a-chips and biomaterial design could inspire high-throughput fabrication of LV models for enhanced drug screening and enhanced insight into impacts of anatomical abnormalities. LV models also hold promise for upsizing them to human-sized transplantable ventricles that aid therapeutic strategies in cardiovascular medicine.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".