CETP IN RARE DYSLIPIDEMIAS
Bibliographic record
Abstract
Objective To determines CETP mass and activity in rare disorders of HDL metabolism. Methods Patients: Fasting plasma samples were obtained from two individuals with TD and from patients with LCAT deficiency, analphalipoproteinemia, Hepatic lipase deficiency (HL), vasculitis, and hypoalphalipoprteinemia. Their clinical and laboratory findings have been previously reported. Assay CETP activity was measured using commercial CETP Fluorescence kit (Roar Biomedical Inc., New York, NY). The kit includes donor (without apoA-I) and acceptor lipoprotein particles. Incubation of donor and acceptor with a CETP source results in the CETP mediated transfer of fluorescent neutral lipid from donor to acceptor, rate of which is determined by the increase in fluorescence intensity as the fluorescent neutral lipid is removed from the donor to the acceptor. The amount of fluorescent substrate transferred was expressed as pmoles of fluorescent substrate transferred within 3 hours. CETP concentration was measured by ELISA using specific rabbit antibody against human CETP. Results While CETP mass correlated with serum HDL-C levels, the activity decreased in patients with LCAT deficiency, FED, HL deficiency and vascultis, but increased in TD. We speculate that the composition of HDL particles in these disorders differs and results in the different CE transfer rates. Conclusion CETP mass correlates with the HDL-C concentration but the activity differ widely among the patients with rare disorders of HDL metabolism, probably due to the changes in HDL quality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".