High density lipoprotein metabolism in low density lipoprotein receptor-deficient mice
Bibliographic record
Abstract
The LDL receptor (LDLR) and scavenger receptor class B type I (SR-BI) play physiological roles in LDL and HDL metabolism in vivo. In this study, we explored HDL metabolism in LDLR-deficient mice in comparison with WT littermates. Murine HDL was radiolabeled in the protein ((125)I) and in the cholesteryl ester (CE) moiety ([(3)H]). The metabolism of (125)I-/[(3)H]HDL was investigated in plasma and in tissues of mice and in murine hepatocytes. In WT mice, liver and adrenals selectively take up HDL-associated CE ([(3)H]). In contrast, in LDLR(-/-) mice, selective HDL CE uptake is significantly reduced in liver and adrenals. In hepatocytes isolated from LDLR(-/-) mice, selective HDL CE uptake is substantially diminished compared with WT liver cells. Hepatic and adrenal protein expression of lipoprotein receptors SR-BI, cluster of differentiation 36 (CD36), and LDL receptor-related protein 1 (LRP1) was analyzed by immunoblots. The respective protein levels were identical both in hepatic and adrenal membranes prepared from WT or from LDLR(-/-) mice. In summary, an LDLR deficiency substantially decreases selective HDL CE uptake by liver and adrenals. This decrease is independent from regulation of receptor proteins like SR-BI, CD36, and LRP1. Thus, LDLR expression has a substantial impact on both HDL and LDL metabolism in mice.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".