Impact of serum and dialysates obtained from chronic hemodialysis patients maintained on high cut‐off membranes on inflammation profile in human THP‐1 monocytes
Bibliographic record
Abstract
INTRODUCTION: Patients with chronic kidney disease maintained on intermittent hemodialysis suffer from systemic chronic inflammation which is causally associated with high mortality. Inflammation mediators of 15-45 kDa range cannot be effectively removed by conventional dialysis membranes. In this study, we tested the influence of serum and dialysates obtained from patients maintained on High cut-off or High flux membranes on the inflammation profile of THP-1 monocytes. METHODS: THP-1 monocytes were treated with serum or dialysates obtained from patients maintained on High cut-off and High flux membranes within a randomized crossover pilot trial. Serum-treated cells were subjected to qPCR analyses with TaqMan probes specific for IL6, TNFa, osteopontin and osteocalcin, and transcriptional screening with Inflammatory Array. Apoptosis assay was performed flow cytometrically with 7-AAD and Annexin V staining. FINDINGS: Treatment of the cells with High cut-off serum led to significant reduction of TNFa and IL-6 expression as well as inflammation-related osteopontin and osteocalcin as compared to High flux membrane treatment. As a complementary finding, treatment with High cut-off dialysates induced a pro-apoptotic phenotype in the cells as demonstrated by a significantly increased percentage of 7-AAD and Annexin V positivity. Global screening of serum-treated cells revealed noticeably decreased inflammation profile under High cut-off serum as compared to High flux treatment. DISCUSSION: Taken together, these data demonstrate that High cut-off -membranes eliminate a spectrum of mediators from serum into the dialysate that possess proinflammatory properties and may impair cellular viability.
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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 | 0.001 | 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".