L-ergothioneine and metformin alleviates liver injury in experimental type-2 diabetic rats via reduction of oxidative stress, inflammation, and hypertriglyceridemia
Bibliographic record
Abstract
Type-2 diabetes (T2D) is associated with liver toxicity. L-ergothioneine (L-egt) has been reported to reduce toxicity in tissues exposed to injury, while metformin is commonly prescribed to manage T2D. Hence, this study evaluates the hepatoprotective role of L-egt, with or without metformin, in T2D male rats. A total of 36 adult male Sprague–Dawley rats were randomly divided into non-diabetic (n = 12) and diabetic (n = 24) groups. After induction of diabetes, animals were divided into six groups (n = 6) and treated orally either with deionized water, L-egt (35 mg/kg bodyweight (bwt)), metformin (500 mg/kg bwt), or a combination of L-egt and metformin for 7 weeks. Body weight and blood glucose were monitored during the experiment. Thereafter, animals were euthanized and liver tissue was excised for biochemical, ELISA, real-time quantitative PCR, and histopathological analysis. L-egt with or without metformin reduced liver hypertrophy, liver injury, triglycerides, oxidative stress, and inflammation. Also, L-egt normalized mRNA expression of SREBP-1c, fatty acid synthase, nuclear factor kappa B, transforming growth factor β1, nuclear factor erythroid 2–related factor 2, and sirtuin-1 in diabetic rats. Furthermore, co-administration of L-egt with metformin to diabetic rats reduced blood glucose and insulin resistance. These results provide support to the therapeutic benefits of L-egt in the management of liver complications associated with T2D.
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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".