Bibliographic record
Abstract
In creating an allelic variant of mouse Apoe designed to resemble human apolipoprotein E4 (apoE4), we generated hypomorphic apoE (hypoE) mice that express only approximately 5% of normal apoE mRNA levels in all tissues. Insertion of a neo cassette flanked by loxP sites in the third intron of Apoe reduced expression of the Arg-61 allelic variant in hypoE mice and resulted in plasma apoE levels that were approximately 2-5% of normal. Unlike other mouse models with low levels of circulating apoE, hypoE mice had a nearly normal lipoprotein cholesterol profile when fed a chow diet. Further reduction of apoE expression in hypoE/Apoe(-/-) heterozygous mice led to an increase in remnant lipoprotein-associated cholesterol levels, demonstrating that hypoE mice express close to the threshold level of Arg-61 apoE required for a normal lipoprotein profile. Unlike wild type mice, hypoE mice were susceptible to diet-induced hypercholesterolemia, which was fully reversed within 3 weeks after resumption of a chow diet. In Mx1-Cre transgenic hypoE mice, plasma apoE levels returned to normal within 10 days after gene repair and removal of the neo cassette following induction of Cre recombinase. HypoE mice provide the opportunity for conditional gene repair by crossing with inducible or lineage/cell type-specific Cre transgenic mice, generating new models to dissect the roles of apoE in atherosclerosis regression, immunoregulation, and neurodegeneration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".