CRP Induces NETosis in Heart Failure Patients with or without Diabetes
Bibliographic record
Abstract
C-reactive protein (CRP) is recognized as a biomarker of chronic, low-grade inflammation associated with vascular disorders. Lately, the role of neutrophils and neutrophil extracellular traps (NETs) has been investigated as a potential source of chronic inflammation and cardiovascular complications. This study investigated NETs as a marker of inflammation in patients with symptomatic heart failure (HF) with or without type 2 diabetes (T2DM) and examined the correlation between NETs and CRP. We performed a noninterventional study including patients with HF with or without T2DM, T2DM, and a healthy control (HC) group. NETs and other inflammatory markers in serum were measured by ELISA. The release of NETs (NETosis) in vitro under various stimuli was measured by confocal microscopy. The levels of NETs in the serum of HF patients were significantly higher compared with HC (112%). Serum CRP concentrations were significantly increased in HF and HF plus T2DM patients compared with HC, and a positive correlation was observed between serum CRP and NETs levels. Neutrophils from HF and HF plus T2DM patients underwent in vitro NETs release faster than T2DM and HC without any stimuli. In vitro, serum collected from the HF and the HF plus T2DM group induced NETosis in healthy neutrophils significantly more when compared with HC and T2DM, which was prevented by depletion from CRP. We confirmed in vitro that CRP induces a concentration-dependent NETs synthesis. This study proposes a mechanism by which CRP increases the risk of future cardiovascular events and supports mounting evidences on the role of neutrophils in chronic low-grade inflammation associated with HF.
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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 | 0.001 |
| 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".