Increased plasma non-esterified fatty acids and platelet-activating factor acetylhydrolase are associated with susceptibility to atherosclerosis in mice
Bibliographic record
Abstract
Animal models provide vital tools to explicate the pathogenesis of atherosclerosis. Accordingly, we established two atherosclerosis-prone mice models: (i) mice lacking the LDL (low-density lipoprotein) receptor (LDLR) and the ability to edit apo (apolipoprotein) B mRNA (Apobec1; designated LDb : LDLR-/- Apobec1-/-), and (ii) mice with the LDb background, who also overexpressed human apoB100 (designated LTp : LDLR-/- Apobec1-/- ERhB+/+). Both LDb and LTp mice had markedly elevated levels of LDL and increased levels of NEFAs (non-esterified fatty acids) compared with C57BL/6 wild-type mice. However, fasting glucose and insulin levels in both animals were not different than those in C57BL/6 wild-type mice. It has been suggested that PAF-AH (platelet-activating factor acetylhydrolase) increases susceptibility to vascular disease. Both LDb and LTp mice had significantly higher PAF-AH mRNA levels compared with C57BL/6 wild-type mice. PAF-AH gene expression was also significantly influenced by age and sex. Interestingly, PAF-AH mRNA levels were significantly higher in both LTp male and female mice than in the LDb mice. This increased PAF-AH gene expression was associated with elevated plasma PAF-AH enzyme activities ( LTp > LDb > C57BL/6 ). Moreover, a greater proportion of PAF-AH activity was associated with the apoB-containing lipoproteins: 29% in LTp and 13% in LDb mice compared with C57BL/6 wild-type animals (6.7%). This may explain why LTp mice developed more atherosclerotic lesions than LDb mice by 8 months of age. In summary, increased plasma NEFAs, PAF-AH mRNA and enzyme activities are associated with accelerated atherogenesis in these animal models.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".