Reply to ‘Are atrial human pluripotent stem cell-derived cardiomyocytes ready to identify drugs that beat atrial fibrillation?’
Bibliographic record
Abstract
In our recent report 1 , we combined developmental biology-inspired differentiation strategies of human pluripotent stem cells (hPSCs) to derive chamber-specific cardiomyocytes 2 and a collagen-hydrogel-based tissue engineering strategy 3 to generate ring-shaped ventricular and atrial-specific engineered heart tissues (EHTs). Detailed molecular, ultrastructural, and functional phenotyping, together with targeted pharmacology, confirmed the chamber-specific identity of the atrial/ventricular EHTs, and demonstrated the potential of these models for disease modeling and drug testing applications. The latter included the ability to induce reentrant arrhythmias in the atrial EHTs and the ability to terminate such arrhythmias with established anti-arrhythmic agents (flecainide and vernakalant). In the accompanying comment, Christ et al. 4 raise concerns with regards to the relative immature properties of the chamber-specific EHTs and their different response to some of the anti-arrhythmic drugs tested (vernakalant and lidocaine) in comparison to their reported effects in adult human atrial and ventricular heart tissues.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.068 | 0.063 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".