Overexpression of apoC-I in apoE-null mice
Bibliographic record
Abstract
Apolipoprotein C-I (apoC-I) has been proposed to act primarily via interference with apoE-mediated lipoprotein uptake. To define actions of apoC-I that are independent of apoE, we crossed a moderately overexpressing human apoC-I transgenic, which possesses a minimal phenotype in the WT background, with the apoE-null mouse. Surprisingly, apoE-null/C-I mice showed much more severe hyperlipidemia than apoE-null littermates in both the fasting and non-fasting states, with an almost doubling of cholesterol, primarily in IDL+LDL, and a marked increase in triglycerides; 3-fold in females to 260 +/- 80 mg/dl and 14-fold in males to 1409 +/- 594 mg/dl. HDL lipids were not significantly altered but HDL were apoC-I-enriched and apoA-II-depleted. Production rates of VLDL triglyceride were unchanged as was the clearance of post-lipolysis remnant particles. Plasma post-heparin hepatic lipase and lipoprotein lipase levels were undiminished as was the in vitro hydrolysis of apoC-I transgenic VLDL. However, HDL from apoC-I transgenic mice had a marked inhibitory effect on hepatic lipase activity, as did purified apoC-I. LPL activity was minimally affected. Atherosclerosis assay revealed significantly increased atherosclerosis in apoE-null/C-I mice assessed via the en face assay. Inhibition of hepatic lipase may be an important mechanism of the decrease in lipoprotein clearance mediated by apoC-I.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".