Living myocardial slices retain patient-specific features: Insights into etiology and therapeutic history
Bibliographic record
Abstract
Background: findings correlate with clinical characteristics is unknown. This study aimed to evaluate whether LMS retain patient-specific functional and pathological characteristics, reflecting diverse etiologies, pharmacological regimens, and clinical interventions. Methods: = 138). Functional assessment of freshly prepared LMS included refractory period, stimulation threshold, force-frequency relationship, post pause potentiation, contractile force, alongside simultaneous optical recordings of calcium transients and action potentials. Variability and grouping analyses were conducted to identify features linked to patient-specific parameters, such as etiology and therapeutic history, including prior left ventricular assist device (LVAD) implantation and amiodarone usage. Results: LMS exhibited lower intrapatient variability (LMS from the same patient) compared to interpatient variability (LMS from different patients), confirming their ability to retain patient-specific functional properties. LMS from LVAD-treated patients exhibited reduced intrapatient variability and reduced diastolic tension, correlated with lower N-terminal pro-B-type natriuretic peptide levels. Stratification by etiology revealed distinct functional characteristics, including enhanced contractile force in titin-mutant LMS and a positive force-frequency relationship in ischemic cardiomyopathy-derived LMS. LMS derived from amiodarone-treated patients demonstrated prolonged action potential duration, reduced excitability at higher pacing frequencies, and enhanced post pause potentiation, reflecting the drug's established pharmacological effects. Conclusions: LMS effectively capture distinct functional parameters associated with patient-specific features. These findings establish LMS as a valuable translational platform for personalized cardiac research, therapeutic testing, and precision 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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".