<scp>l</scp>-Carnitine-induced amelioration of HFD-induced hepatic dysfunction is accompanied by a reduction in hepatic TNF-α and TGF-β1
Bibliographic record
Abstract
In this study, we evaluated the possible mechanisms through which l-carnitine ameliorates the adverse effects from obesity in rats, induced with a high-fat diet (HFD). For this, 56 albino Wister rats were randomly assigned to 7 groups. The control group was fed a basal diet and injected with saline. The second group was fed the basal diet and injected with l-carnitine (200 mg/kg body mass, by intraperitoneal injection; i.p.). The third group were fed the HFD. The fourth group was fed the HFD and injected with l-carnitine (200 mg/kg body mass, i.p.) for 8 weeks. The fifth group was fed the HFD for 10 weeks. The sixth group were fed the HFD for 10 weeks and were also injected with l-carnitine (200 mg/kg body mass, i.p.) during the final 2 weeks. The seventh group was fed the HFD diet for 8 weeks then the basal diet for 2 weeks. The HFD induced significantly increased levels of hyperglycemia, lipid peroxidation, pathological changes, TNF-α and TGF-β1 protein expression in hepatic tissue, food intake, body weight gain, serum levels of total and non-high-density lipoprotein cholesterol, ketone bodies, triacylglycerol, urea, creatinine, AST, and ALT. However, the HFD diet significantly decreased serum levels of high-density lipoprotein (HDL) and hepatic levels of reduced glutathione. l-Carnitine ameliorated the effects of the HFD on the above-mentioned parameters. This study indicated that l-carnitine had protective and curative effects against HFD-induced hepatosteatosis by reducing hepatic oxidative stress and protein expression of TNF-α and TGF-β1.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".