Angiopoietin like‐2 knock‐down improves the lipid profile of high‐fat diet‐fed mice and maintains vascular endothelial function
Bibliographic record
Abstract
Angiopoietin like‐2 (angptl2), a member of the angiopoietin‐like protein family, can contribute to insulin resistance in obesity (Tabata 2009). However, angptl2 involvement in lipid profiling and vascular endothelial function remain largely unknown. Objective An angptl2 knock‐down (KD) mouse was used to study the role of angptl2 in lipid handling and endothelial function in the femoral artery. Methods/results 3 month‐old wild‐type (WT) and KD mice were fed a regular diet (RD) or high‐fat diet (HFD) until 6 months of age. WT and KD mice displayed similar lipid profiles under the RD. While total cholesterol and high‐density lipoprotein (HDL) levels increased similarly in WT and KD mice after a HFD, low‐density lipoprotein (LDL) levels increased 2.7‐fold in WT and only 1.9‐fold in KD so that total cholesterol‐to‐HDL and LDL‐to‐HDL ratios increased significantly (P<0.05) only in WT but not in KD following HFD. Under a RD, KD mice displayed improved endothelial function as measured by the EC 50 of the acetylcholine‐induced dose‐response curve. Although this difference in sensitivity between the two strains was lost after a HFD, contribution by nitric oxide (NO) was maintained in KD but not in WT. Conclusion Angptl2 knock‐down in mice may improve lipid handling when challenged with a HFD, and lead to maintenance of endothelial function, specifically NO contribution to vasorelaxation. Supported by CIHR 14496 and NSERC.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".