Targeted deletion of endothelial lipase increases HDL particles with anti-inflammatory properties both in vitro and in vivo
Bibliographic record
Abstract
Previous studies have shown that targeted deletion of endothelial lipase (EL) markedly increases the plasma high density lipoprotein cholesterol (HDL-C) level in mice. However, little is known about the functional quality of HDL particles after EL inhibition. Therefore, the present study assessed the functional quality of HDL isolated from EL(-/-) and wild-type (WT) mice. Anti-inflammatory functions of HDL from EL(-/-) and WT mice were evaluated by in vitro assays. The HDL functions such as PON-1 or PAF-AH activities, inhibition of cytokine-induced vascular cell adhesion molecule-1 expression, inhibition of LDL oxidation, and the ability of cholesterol efflux were similar in HDL isolated from WT and EL(-/-) mice. In contrast, the lipopolysaccharide-neutralizing capacity of HDL was significantly higher in EL(-/-) mice than that in WT mice. To evaluate the anti-inflammatory actions of HDL in vivo, lipopolysaccharide-induced systemic inflammation was generated in these mice. EL(-/-) mice showed higher survival rate and lower expression of inflammatory markers than WT mice. Intravenous administration of HDL isolated from EL(-/-) mice significantly improved the mortality after lipopolysaccharide injection in WT mice. In conclusion, targeted disruption of EL increased HDL particles with preserved anti-inflammatory and anti-atherosclerotic functions. Thus, EL inhibition would be a useful strategy to raise 'good' cholesterol in the plasma.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".