Cystic fibrosis transmembrane regulator correction attenuates heart failure-induced lung inflammation
Bibliographic record
Abstract
Heart failure (HF) affects 64 million people worldwide. Despite advancements in prevention and therapy, quality of life remains poor for many HF patients due to associated target organ damage. Pulmonary manifestations of HF are well-established. However, difficulties in the treatment of HF patients with chronic lung phenotypes remain as the underlying patho-mechanistic links are still incompletely understood. Here, we aim to investigate the cystic fibrosis transmembrane regulator (CFTR) involvement in lung inflammation during HF, a concept that may provide new mechanism-based therapies for HF patients with pulmonary complications. In a mouse model of HF, pharmacological CFTR corrector therapy (Lumacaftor (Lum)) was applied systemically or lung-specifically for 2 weeks, and the lungs were analyzed using histology, flow cytometry, western blotting, and qPCR. Experimental HF associated with an apparent lung phenotype characterized by vascular inflammation and remodeling, pronounced tissue inflammation as evidenced by infiltration of pro-inflammatory monocytes, and a reduction of pulmonary CFTR+ cells. Moreover, the elevation of a classically-activated phenotype of non-alveolar macrophages coincided with a cell-specific reduction of CFTR expression. Pharmacological correction of CFTR with Lum mitigated the HF-induced downregulation of pulmonary CFTR expression and increased the proportion of CFTR+ cells in the lung. Lum treatment diminished the HF-associated elevation of classically-activated non-alveolar macrophages, while promoting an alternatively-activated macrophage phenotype within the lungs. Collectively, our data suggest that downregulation of CFTR in the HF lung extends to non-alveolar macrophages with consequences for tissue inflammation and vascular structure. Pharmacological CFTR correction possesses the capacity to alleviate HF-associated lung inflammation.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| 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".