The anti-anginal ranolazine does not confer beneficial actions against hepatic steatosis in male mice subjected to high-fat diet and streptozotocin-induced type 2 diabetes
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is characterized by the accumulation of excess fat in the liver in the absence of alcohol and increases one's risk for both diabetes and cardiovascular disease (e.g., angina). We have shown that the second-line anti-anginal therapy, ranolazine, mitigates obesity-induced NAFLD, and our aim was to determine whether these actions of ranolazine also extend to NAFLD associated with type 2 diabetes (T2D). Eight-week-old male C57BL/6J mice were fed either a low-fat diet or a high-fat diet for 15 weeks, with a single dose of streptozotocin (STZ; 75 mg/kg) administered in the high-fat diet-fed mice at 4 weeks to induce experimental T2D. Mice were treated with either vehicle control or ranolazine during the final 7 weeks (50 mg/kg once daily). We assessed glycemia via monitoring glucose tolerance, insulin tolerance, and pyruvate tolerance, whereas hepatic steatosis was assessed via quantifying triacylglycerol content. We observed that ranolazine did not improve glycemia in mice with experimental T2D, while also having no impact on hepatic triacylglycerol content. Therefore, the salutary actions of ranolazine against NAFLD may be limited to obese individuals but not those who are obese with T2D.
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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".