Low density neutrophils and neutrophil extracellular traps (NETs) are new inflammatory players in heart failure
Bibliographic record
Abstract
Abstract Background Heart failure with reduced (HFrEF) or preserved ejection fraction (HFpEF) is characterized by low-grade chronic inflammation. Circulating neutrophils regroup two subtypes termed high- and low-density neutrophils (HDNs and LDNs). LDNs represent less than 2% of total neutrophil under physiological conditions, but their count increase in multiple pathologies, releasing more inflammatory cytokines and neutrophil extracellular traps (NETs). Objectives Assess the differential count and role of HDNs, LDNs and NETs-related activities in HF patients. Methods HDNs and LDNs were isolated from human blood by density gradient and purified by FACS and their counts obtained by flow cytometry. NETs formation (NETosis) was quantified by confocal microscopy. Circulating inflammatory and NETosis biomarkers were measured by 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 (HV), 41 HFrEF (19 stable patients and 22 presenting acute decompensated HF; ADHF) and 66 HFpEF patients (36 stable patients and 30 presenting HF decompensation). HDNs and LDNs counts were significantly increased up to 39% and 2740% respectively in HF patients compared to HV. In HF patients, the correlations between LDNs counts and circulating inflammatory (CRP, IL-6 and -8), Troponin T, NT-proBNP and NETosis components were all significant. In vitro, LDNs expressed more H3Cit and NETs and were more pro-adhesive, with ADHFpEF patients presenting the highest pro-inflammatory profile. Conclusions HFpEF patients present higher levels of circulating LDNs and NETs related activities, which are the highest in the context of acute HF decompensation. Clinical Perspective In comparison to HFrEF, HFpEF patients have higher levels of circulation LDNs and NETs-associated inflammatory cytokines, peaking in acute decompensated clinical condition. Furthermore, LDNs are producing more NETs and are more adhesive than HDNs, which can contribute to pro-thrombogenesis status described in HF patients Measurement of circulating NETs-associated biomarkers could become a novel tool to assess the the risk of acute thrombogenesis in hospitalized ADHFpEF patients. These measurements could lead to future clinical treatments using NETosis inhibitors alone or combined with NETs degradation enzymes (e.g. DNase I). At this time, additional preclinical studies are required to determine specific cell surface markers that could distinguish LDNs from HDNs in whole blood. Once available, circulating LDNs levels would be routinely measured and integrated in the complete blood count analysis to better assess patients’ inflammatory status.
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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".