Comparison of two low‐density lipoprotein apheresis systems in patients with homozygous familial hypercholesterolemia
Bibliographic record
Abstract
Low-density lipoprotein (LDL) apheresis (LA) is a reliable method to decrease LDL-C concentrations and remains the gold standard therapy in homozygous familial hypercholesterolemia (HoFH). The objective of this study was to compare the efficacy of two LA systems [heparin-induced extracorporeal LDL precipitation (HELP) vs. dextran sulfate adsorption (DS) on the reduction of lipids, inflammatory markers, and adhesion molecules in a sample of genetically defined HoFH subjects (n = 9)]. Fasting blood samples were collected before and after LA. All subjects served as their own control and were first treated with the HELP system then with DS in this single sequence study. Compared with HELP, DS led to significantly greater reductions in total cholesterol (-63.3% vs. -59.9%; P = 0.05), LDL-C (-70.5% vs. -63.0%; P = 0.02), CRP (-75.3% vs. -48.8%; P < 0.0001), and TNF-α (-23.7% vs. +14.7%; P = 0.003). Reductions in the plasma levels of PCSK9 (-45.3% vs. -63.4%; P = 0.31), lipoprotein (a) (-70.6% vs. -65.0%; P = 0.30), E-selectin (-16.6% vs. -18.3%; P = 0.65), ICAM-1 (-4.0 vs. 5.6%; P = 0.56), and VCAM-1 (8.3% vs. -1.8%; P = 0.08) were not different between the two systems. For the same volume of filtered plasma (3,000 mL), however, HELP led to greater reductions in plasma apoB (-63.1% vs. -58.3%; P = 0.04), HDL-C (-20.6% vs. -6.5%; P = 0.003), and PCSK9 (-63.4% vs. -28.5%; P = 0.02) levels. These results suggest that both LA systems are effective in reducing plasma lipids and inflammatory markers in HoFH. Compared with HELP, greater reductions in lipid levels and inflammatory markers were achieved with DS, most likely because this method allows for a larger plasma volume to be filtered. J. Clin. Apheresis 31:359-367, 2016. © 2015 Wiley Periodicals, Inc.
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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) | 0.001 | 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".