A simple and precise method for measuring HDL-cholesterol subfractions by a single precipitation followed by homogenous HDL-cholesterol assay
Bibliographic record
Abstract
HDL consists of two major subfractions, HDL2 and HDL3. This paper describes a simple method for assaying HDL subspecies by combining a single precipitation with a direct high density lipoprotein-cholesterol (HDL-C) assay. A precipitation reagent (0.06 ml) containing 1,071 U/ml heparin, 500 mmol/l MnCl2) and 12 mg/ml dextran sulfate was added to a serum (0.3 ml). The sample was incubated and centrifuged at 10,000 rpm for 10 min. HDL3-C was measured by a homogenous HDL-C assay in the supernatant, and HDL2-C was estimated by subtracting the HDL3-C from the direct HDL-C. The HDL3-C and HDL2-C values determined by the precipitation method were identical to those determined by ultracentrifugation, and there were excellent correlations between the methods in the measurements of HDL3-C and HDL2-C (r = 0.933 and 0.978, respectively; n = 102). The two methods also proved to be highly correlated in the measurement of apolipoprotein A-I and A-II in HDL subfractions. The HDL-C subfractions determined by ultracentrifugation were more closely associated with the homogenous HDL-C assay than with the total cholesterol assay, especially in the hypertriglyceridemic samples. Our method is far simpler and more precise than the classical dual precipitation method for HDL-C subfractions, and it can be easily performed in a routine chemical laboratory.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".