Branched-chain amino acids alleviate nonalcoholic steatohepatitis in rats
Bibliographic record
Abstract
Nonalcoholic steatohepatitis (NASH) is a prevalent disease in countries around the world. The branched-chain amino acids (BCAAs) leucine, isoleucine, and valine cannot be synthesized by the body and have been shown to promote muscle buildup; thus, it is logical to suggest that BCAAs can reduce fat deposition in the body. We used gonadectomized rats fed a high-fat diet to investigate the effects of BCAAs on lipid metabolism over an 8-week experimental period. Body composition, tissue histology, plasma lipid indices, and hormone levels were examined. We demonstrated that the body weights of rats were not significantly decreased but the mesenteric fat was significantly decreased (p < 0.05) in BCAA-treated rats. In addition, BCAAs decreased plasma lipid levels and fat deposition in the liver. At week 4, when the untreated rats displayed macrovesicular steatosis, BCAA-treated rats had only macrovesicular droplets in their hepatocytes. At week 8, when the untreated rat livers displayed profound inflammation and cirrhosis, BCAA-treated rat livers remained in the macrovesicular stage of steatosis. BCAAs induced higher blood glucose and plasma insulin levels (p < 0.05). BCAAs also improved liver blood flow by increasing mean arterial blood pressure and decreasing portal pressure, which helped delay the change in blood flow pattern to that of cirrhosis. BCAAs also induced the skeletal muscle to express higher levels of branched-chain α-keto acid dehydrogenase E1α, which indicates an enhanced metabolic capacity of BCAAs in muscle tissue. This study clearly demonstrates the effects of BCAAs on the amelioration of fat deposition in rats fed a high-fat diet.
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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 | 0.002 | 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) | 0.002 | 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".