Ces3/TGH Deficiency Improves Dyslipidemia and Reduces Atherosclerosis in <i>Ldlr</i> <sup>−/−</sup> Mice
Bibliographic record
Abstract
RATIONALE: Carboxylesterase 3/triacylglycerol hydrolase (TGH) has been shown to participate in hepatic very low-density lipoprotein (VLDL) assembly. Deficiency of TGH in mice lowers plasma lipids and atherogenic lipoproteins without inducing hepatic steatosis. OBJECTIVE: To investigate the contribution of TGH to atherosclerotic lesion development in mice that lack low-density lipoprotein receptor (LDLR). METHODS AND RESULTS: Mice deficient in LDL receptor (Ldlr(-/-)) and mice lacking both TGH and LDLR (Tgh(-/-)/Ldlr(-/-)) were fed with a Western-type diet for 12 weeks. Analysis of Tgh(-/-)/Ldlr(-/-) plasma showed an atheroprotective lipoprotein profile with decreased cholesterol in the VLDL and the LDL fractions, concomitant with elevated high-density lipoprotein cholesterol. Significantly reduced plasma apolipoprotein B levels were also observed in Tgh(-/-)/Ldlr(-/-) mice. Consequently, Tgh(-/-)/Ldlr(-/-) mice presented with a significant reduction (54%, P<0.01) of the high-fat, high-cholesterol dieteninduced atherosclerotic plaques when compared with Tgh(+/+)/Ldlr(-/-) mice in the cross-sectional aortic root analysis. TGH deficiency did not further increase liver steatosis despite lowering plasma lipids, mainly due to reduced hepatic lipogenesis. The ameliorated dyslipidemia in Tgh(-/-)/Ldlr(-/-) mice was accompanied with significantly improved insulin sensitivity. CONCLUSIONS: Inhibition of TGH activity ameliorates atherosclerosis development and improves insulin sensitivity in Ldlr(-/-) mice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".