Removal of neutrophil gelatinase‐associated lipocalin by extracorporeal therapies
Bibliographic record
Abstract
Neutrophil gelatinase-associated lipocalin (NGAL) protein is an early biomarker for acute kidney injury (AKI). It is unknown if extracorporeal therapies (EC) have an effect on circulating NGAL levels. This study was designed to describe the kinetics of NGAL molecule in different EC techniques and to evaluate NGAL clearance in different operational conditions. A mock hemofiltration (HF) and hemoperfusion (HP) setup was used. NGAL was added to the blood reservoir and then measured at 30-minute intervals from arterial, venous, and ultrafiltrate (UF) lines. Removal kinetics and NGAL sieving coefficient were calculated. In our experiments, baseline NGAL concentration averaged 452 microg/L. There was a consistent downward trend throughout the experiment. NGAL concentration in the UF was between 80 and 90 microg/L, though it showed a slight increase in the second hour. The sieving coefficient of NGAL ranged from 0.2 to 0.4 during HF and it appeared to increase with time, suggesting an initial effect of membrane adsorption. HP proved clearly that there was adsorption of NGAL by the membrane and the point of saturation occured at approximately 60 minutes from the start of circulation. Our evaluation demonstrates that NGAL can be adsorbed and ultrafiltrated with polysulfone membranes. This should be taken into consideration when using NGAL as an AKI biomarker in patients undergoing EC circulation.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".