Optical Method to Quantify Mechanical Contraction and Calcium Transients of Human Pluripotent Stem Cell-Derived Cardiomyocytes
Bibliographic record
Abstract
Differentiation of human pluripotent stem cells into cardiomyocytes (hPS-CMs) holds promise for myocardial regeneration therapies, drug discovery, and models of cardiac disease. Potential cardiotoxicities may affect hPS-CM mechanical contraction independent of calcium signaling. Herein, a method using an image capture system is described to measure hPS-CM contractility and intracellular calcium concurrently, with high spatial and temporal resolution. The image capture system rapidly alternates between brightfield and epifluorescent illumination of contracting cells. Mechanical contraction is quantified by a speckle tracking algorithm applied to brightfield image pairs, whereas calcium transients are measured by a fluorescent calcium reporter. This technique captured changes in contractile strain, calcium transients, and beat frequency of hPS-CMs over 21 days in culture, as well as acute responses to isoproterenol and Cytochalasin D. The technique described above can be applied without the need to alter the culture platform, allowing for determination of hPS-CM behavior over weeks in culture for drug discovery and myocardial regeneration 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".