Therapeutic effect of ouabagenin, a novel liver X receptor agonist, on atherosclerosis in nonalcoholic steatohepatitis in SHRSP5/Dmcr rat model
Bibliographic record
Abstract
The liver X receptor (LXR) can enhance cholesterol transporters, which could remove excess cholesterol from foam cells in atheromas. LXR has two subtypes: LXRα, which aggravates hepatic lipid accumulation, and LXRβ, which does not. In 2018, ouabagenin (OBG) was reported as a potential LXRβ-specific agonist. We aimed to examine whether OBG specifically affects LXRβ in nonalcoholic steatohepatitis (NASH); it did not aggravate hepatic steatosis and can suppress the development of atherosclerosis. SHRSP5/Dmcr rats fed a high-fat and high-cholesterol diet were divided into four groups as follows: (I) L-NAME group, (II) L-NAME/OBG group, (III) OBG (-) group, and (IV) OBG (+) group. All groups' rats were intraperitoneally administered L-NAME. The L-NAME/OBG group's rats were intraperitoneally administered OBG and L-NAME simultaneously. After L-NAME administration, the OBG (+) group's rats were administered OBG, while the OBG (-) group's rats were not. Although all rats developed NASH, OBG did not exacerbate steatosis (L-NAME/OBG and OBG (+) groups). In addition, endothelial cells were protected in the L-NAME/OBG group and foam cells in the atheroma were reduced in the OBG (+) group. OBG is an LXRβ-specific agonist and has a potential therapeutic effect on atherosclerosis without developing lipid accumulation in the liver.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".