Clearance of inflammatory cytokines in Patients with Septic Acute Kidney Injury During Renal Replacement Therapy Using the EMiC2 Filter (Clic-AKI study)
Bibliographic record
Abstract
Abstract Background: The EMiC2 membrane is a medium cutoff hemofilter (45 kiloDalton). Little is known regarding its efficacy in eliminating medium-sized cytokines in sepsis. This study aimed to explore the effects of continuous veno-venous hemodialysis (CVVHD) using the EMiC2 filter on cytokine clearance.Methods: This was a prospective observational study conducted in critically ill patients with sepsis and acute kidney injury requiring kidney replacement therapy. We measured concentrations of 12 cytokines [Interleukin (IL) IL-1β, IL-1α, IL-2, IL-4, IL-6, IL-8, IL-10, interferon (IFN)-ƴ, tumor necrosis factor (TNF)-α, vascular endothelial growth factor (VEGF), monocyte chemoattractant protein (MCP)-1, epidermal growth factor (EGF)] in plasma at baseline (T0) and pre- and post-dialyzer at 1, 6, 24, and 48 hours after CVVHD initiation and in the effluent fluid at corresponding time points. Outcomes were the effluent and adsorptive clearance rates, mass balances, and changes in serial serum concentrations.Results: Twelve patients were included in the final analysis. All cytokines except EGF concentrations declined over 48 hours (p<0.001). The effluent clearance rates were variable and ranged from negligible values for IL-2, IFN-ƴ, IL-1α, IL-1β, and EGF, to 19.0 ml/min for TNF-α. Negative or minimal adsorption was observed. The effluent and adsorptive clearance rates remained steady over time. The percentage of cytokine removal was low for most cytokines throughout the 48-hour period. Conclusion: EMiC2-CVVHD achieved modest removal of most cytokines and demonstrated small to no adsorptive capacity despite a decline in plasma cytokine concentrations. This suggests that changes in plasma cytokine concentrations may not be solely influenced by extracorporeal removal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".