Differential Impact of Familial Hypercholesterolemia and Combined Hyperlipidemia on Vascular Wall and Network Remodeling in Mice
Bibliographic record
Abstract
Genetic familial hypercholesterolemia (FH) and combined hyperlipidemia (FCH) are characterized by elevated plasma low-density lipoprotein (LDL) (FH) and LDL/triglycerides (FCH), with mouse models represented by LDL receptor (LDLR) and apolipoprotein E (ApoE) gene deletion mice, respectively. Given the impact of FH and FCH on health outcomes, we determined the impact of FH/FCH on vascular structure in LDLR and ApoE mice. LDLR, ApoE and control mice were utilized at 12-13 and 22-23 weeks when gracilis arteries were studied for wall mechanics and gastrocnemius muscles were harvested for microvessel density measurements. Conduit arteries and plasma samples were harvested for biochemical analyses. Arteries from ApoE and LDLR exhibited blunted expansion versus control, reduced distensibility and left-shifted stress versus strain relation (LDLR > ApoE). Microvessel density was reduced in ApoE and LDLR (ApoE > LDLR). Secondary analyses suggested that wall remodeling in LDLR was associated with cholesterol and MCP-1, while rarefaction in ApoE was associated with tumor necrosis factors-alpha, triglycerides and vascular production of TxA(2). Remodeling in ApoE and LDLR appears distinct; as that in LDLR is preferential for vascular walls, while that for ApoE is stronger for rarefaction. Remodeling in LDLR may be associated with cellular adhesion, while that in ApoE may be associated with pro-apoptotsis and constrictor prostanoid generation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".