Hemodiafiltration Attenuates NETosis Compared With High-Flux Hemodialysis in End-Stage Kidney Disease Patients
Bibliographic record
Abstract
Introduction: Patients with chronic kidney disease (CKD) and diabetes mellitus face a heightened risk of cardiovascular complications and infections, potentially exacerbated by dysregulated NETosis. Given the superior survival rates observed with hemodiafiltration (HDF) over high-flux hemodialysis (HD; HFHD) and the documented NETosis dysregulation in HD and in patients with diabetes mellitus, this study aimed to investigate the impact of dialysis modality on NETosis activity in patients on HD, stratified by diabetes status. Methods: A total of 20 patients on HD (10 with diabetes, 10 without diabetes) undergoing HDF treatment were recruited. Blood samples were collected before and after HDF. After transition to HFHD treatment, blood samples were taken again after 1 and 3 weeks of HDFD treatment. Neutrophils were isolated, stimulated with phorbol-12-myristate-13-acetate, and stained for the following NETosis markers: peptidyl arginine deiminase 4 (PAD4), neutrophil elastase (NE), myeloperoxidase (MPO), histone H3, and double-stranded DNA (dsDNA). Data were acquired using a flow cytometer. In addition, serum levels of citrullinated histone H3 (citH3), MPO, and NE were measured using enzyme-linked immunosorbent assay. Results: Our results demonstrate a significant increase in NETosis activation and markers after HFHD treatment compared with HDF treatment. NETosis markers significantly increased in serum after 3 weeks of HFHD treatment. In addition, significantly lower NETosis markers were observed in patients with diabetes than in patients without diabetes. Conclusion: The increase in NETosis markers after 3 weeks of HFHD compared with HDF highlights the role of HDF in mitigating dysregulated NETosis. Further research is needed to explore differences in NETosis profiles across patient populations and assess their clinical implications based on dialysis modality.
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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".