Activation of Neutrophil Extracellular Trap Formation in Patients with Heart Failure and a Preserved Ejection Fraction
Bibliographic record
Abstract
INTRODUCTION: Pathophysiological differences between heart failure (HF) with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF) remain poorly understood. Therefore, we performed pathway analyses from whole-blood transcriptomics to distinguish HFpEF from HFrEF. METHODS AND RESULTS: Lexogen's QuantSeq was used to carry out whole-blood transcriptomics in 500 patients with HF (HFpEF n = 250, HFrEF n = 250). Differential gene expression analysis was performed on all protein-coding genes that met a predefined minimum expression level. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology over-representation analysis was utilized to identify upregulated and downregulated biological pathways. The findings were validated in an independent cohort of 727 patients with HF. Out of 7672 protein-coding transcripts, 217 were upregulated and 110 were downregulated in patients with HFpEF compared with HFrEF. The 3 most significantly upregulated genes were neutrophil-expressed elastase, defensin alpha 4, and pro-platelet basic protein. The 3 most significantly downregulated genes were lymphotoxin beta, bridging integrator 1, and V-set pre-B cell surrogate light chain 3. Translation of differentially expressed genes into biological pathways demonstrated that the most significantly activated KEGG pathway in HFpEF was neutrophil extracellular trap formation. DISCUSSION: Transcriptomics analyses suggest activation of neutrophil extracellular trap formation pathways in patients with HFpEF. This pathway is associated with endothelial and coronary microvascular dysfunction and might be a target for future drug development in patients with HFpEF.
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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".