Biowire Model of Interstitial and Focal Cardiac Fibrosis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Myocardial fibrosis is a severe global health problem due to its prevalence in all forms of cardiac diseases and direct role in causing heart failure. The discovery of efficient antifibrotic compounds has been hampered due to the lack of a physiologically relevant disease model. Herein, we present a disease model of human myocardial fibrosis and use it to establish a compound screening system. In the Biowire II platform, cardiac tissues are suspended between a pair of poly(octamethylene maleate (anhydride) citrate) (POMaC) wires. Noninvasive functional readouts are realized on the basis of the deflection of the intrinsically fluorescent polymer. The disease model is constructed to recapitulate contractile, biomechanical, and electrophysiological complexities of fibrotic myocardium. Additionally, we constructed a heteropolar integrated model with fibrotic and healthy cardiac tissues coupled together. The integrated model captures the regional heterogeneity of scar lesion, border zone, and adjacent healthy myocardium. Finally, we demonstrate the utility of the system for the evaluation of antifibrotic compounds. The high-fidelity in vitro model system combined with convenient functional readouts could potentially facilitate the development of precision medicine strategies for cardiac fibrosis modeling and establish a pipeline for preclinical compound screening.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".