Low-Density Neutrophils and Neutrophil Extracellular Traps (NETs) Are New Inflammatory Players in Heart Failure
Bibliographic record
Abstract
BACKGROUND: Heart failure with reduced (HFrEF) or preserved ejection fraction (HFpEF) is characterized by low-grade chronic inflammation. Circulating neutrophils regroup 2 subtypes termed high- and low-density neutrophils (HDNs and LDNs). LDNs represent less than 2% of total neutrophil under physiological conditions, but their counts increase in multiple pathologies, releasing more inflammatory cytokines and neutrophil extracellular traps (NETs). The aims of this study were to assess the differential count and role of HDNs, LDNs, and NETs-related activities in patients with heart failure (HF). METHODS: HDNs and LDNs were isolated from human blood by density gradient and purified by fluorescence-activated cell sorting (FACS) and their counts obtained by flow cytometry. Formation of NETs (NETosis) was quantified by confocal microscopy. Circulating inflammatory and NETosis biomarkers were measured by enzyme-linked immunosorbent assay (ELISA). Neutrophil adhesion onto human extracellular matrix (hECM) was assessed by optical microscopy. RESULTS: A total of 140 individuals were enrolled, including 33 healthy volunteers (HVs), 41 HFrEF (19 stable patients and 22 presenting acute decompensated HF [ADHF]), and 66 patients with HFpEF (36 stable patients and 30 presenting HF decompensation). HDNs and LDNs counts were significantly increased up to 39% and 2740%, respectively, in patients with HF compared with HVs. In patients with HF, the correlations among LDNs counts and circulating inflammatory (CRP, IL-6 and -8), troponin T, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and NETosis components were significant. In vitro, LDNs expressed more citrullinated histone H3 (H3Cit) and NETs and were more proadhesive, with ADHFpEF patients presenting the highest proinflammatory profile. CONCLUSIONS: Patients with HFpEF present higher levels of circulating LDNs- and NETs-related activities, which are the highest in the context of acute HF decompensation.
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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) | 0.000 | 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".