New model for kinetic studies of HDL metabolism in humans
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to develop a new model for kinetic studies of Apolipoprotein A-I of HDL (Apo A-I-HDL) labelled with stable isotope by using HDL subclasses isolated with fast protein liquid chromatography (FPLC). MATERIALS AND METHODS: Apo A-I-HDL kinetics were studied by infusing [5.5.5-(2)H(3)]-leucine for 14 h in six healthy subjects. Prebeta(1) and alphaHDL were separated by FPLC and total HDL by ultracentrifugation (HDL-UC). RESULTS: The tracer-to-tracee ratios were higher in prebeta(1) HDL than in HDL-UC or alphaHDL. Leucine enrichments found in HDL-UC were higher compared with alphaHDL, suggesting that HDL-UC were composed of a mixture of Apo A-I-alphaHDL and Apo A-I-prebeta(1) HDL. Kinetic analysis of data obtained from FPLC was achieved using a multicompartmental model, including a conversion between prebeta(1) and alphaHDL compartments. The production rate of prebeta(1) HDL was 7.72 +/- 2.86 mg kg(-1) d(-1) (mean +/- SD). Prebeta(1) HDL were converted to alphaHDL at a rate of 96.24 +/- 42.99 pool d(-1), and the synthesis rate of prebeta(1) HDL from alphaHDL was 10-fold slower: 7.09 +/- 4.51 pool d(-1). Apo A-I-FCR of HDL-UC was estimated using a one-compartment model (0.165 +/- 0.074 pool d(-1)), and was higher but not significantly compared with FCR of Apo A-I-alphaHDL (0.112 +/- 0.026 pool d(-1)) calculated with the new model. CONCLUSIONS: This study reports for the first time a model involving enrichments of Apo A-I in prebeta(1) and alphaHDL which allowed the measure of Apo A-I cycling within HDL fraction and will aid better understanding of kinetics of HDL in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".