A Porcine Model of Heart Failure With Preserved Ejection Fraction Induced by Chronic Pressure Overload Characterized by Cardiac Fibrosis and Remodeling
Bibliographic record
Abstract
Heart failure is induced by multiple pathological mechanisms, and current therapies are ineffective against heart failure with preserved ejection fraction (HFpEF). As there are limited animal models of HFpEF, its underlying mechanisms have not yet been elucidated. Here, we employed the descending aortic constriction (DAC) technique to induce chronic pressure overload in the left ventricles of Tibetan minipigs for 12 weeks. Cardiac function, pathological and cellular changes, fibrotic signaling activation, and gene expression profiles were explored. The left ventricles developed concentric hypertrophy from weeks 4 to 6 and transition to dilation starting in week 10. Notably, the left ventricular ejection fraction was maintained at >50% in the DAC group during the 12-week period. Pathological examination, biochemical analyses, and gene profile analysis revealed evidence of inflammation, fibrosis, cell death, and myofilament dephosphorylation in the myocardium of HFpEF model animals, together with gene expression shifts promoting cardiac remodeling and downregulating metabolic pathways. Furthermore, we noted the activation of several signaling proteins that impact cardiac fibrosis and remodeling, including transforming growth factor-β/SMAD family members 2/3, type I/III/V collagens, phosphatidylinositol 3-kinase, extracellular signal-regulated kinase, matrix metalloproteinases 2 and 9, tissue inhibitor of metalloproteinases 1 and 2, interleukins 6 and 1β, and inhibitor of κBα/nuclear factor-κB. Our findings demonstrate that this chronic pressure overload-induced porcine HFpEF model is a powerful tool to elucidate the mechanisms of this disease and translate preclinical findings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".