Antisenescence Therapy Improves Function in a Human Model of Cardiac Fibrosis-on-a-Chip
Bibliographic record
Abstract
Cardiac fibrosis is a significant contributor to heart failure and is characterized by abnormal ECM deposition and impaired contractile function. We have previously developed a model of cardiac fibrosis via TGF-β treatment of engineered microtissues using heart-on-a-chip technology which incorporates human induced pluripotent stem cell-derived cardiomyocytes and cardiac fibroblasts. Here, we describe that these cardiac fibrotic tissues expressed markers associated with cellular senescence via transcriptomic analysis. Treatment of fibrotic tissues with the senolytic drugs dasatinib and quercetin (D+Q) led to an improvement of contractile function, reduced passive tension, and downregulated senescence-related gene expression, an outcome we were previously unable to achieve using standard-of-care drugs. The improvement in functional parameters was also associated with a reduction in fibroblast density, though no changes in absolute collagen deposition were observed. This study demonstrates the benefit of senolytic treatment for cardiac fibrosis in a human-relevant model, supporting data in animal models, and will enable the further elucidation of cell-specific effects of senolytics and how they impact cardiac fibrosis and senescence.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".