Effect of labeling of plasma lipoproteins with [3H]cholesterol on values of esterification rate of cholesterol in apolipoprotein B-depleted plasma
Bibliographic record
Abstract
The fractional esterification rate of cholesterol in apolipoprotein B (apoB)-depleted plasma (FER HDL ) is a good indicator of particle size distribution in high density lipoprotein (HDL) and low density lipoprotein (LDL). However, there has been a discrepancy in the absolute values of FER HDL published by different laboratories. Because the main difference between the methods was in the labeling of lipoproteins with [ 3 H]cholesterol we investigated the effect of using Corning immunoplates and paper discs as carriers of the labeled unesterified cholesterol. We found that Corning plates trap some 3 H-labeled free cholesterol, which is released during incubation at 37°C. This means that this additional 3 H-labeled free cholesterol is exposed to lecithin: cholesterol acyltransferase (LCAT) for a shorter time and artificially decreases FER HDL . Using paper discs discarded before incubation as carriers of the 3 H-labeled free cholesterol results in complete labeling of HDL and thus yields higher values of FER HDL . —Dobiášová, M., L. Adler, T. Ohta, and J. Frohlich. Effect of labeling of plasma lipoproteins with [ 3 H]cholesterol on values of esterification rate of cholesterol in apolipoprotein B-depleted plasma. J. Lipid Res. 2000. 41: 1356–1357.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".