Protective Effect of aqueous bark extract of Terminalia Arjuna against Alcohol-Induced Hepato and Nephrotoxicity in Rats
Bibliographic record
Abstract
Present study is an attempt to forward a locally available aqueous bark powder extract of Terminalia arjuna (AETA) as potential therapeutic agent against alcohol-induced oxidative/nitrosative stress mediated hepato and nephrotoxicity in rats. Alcohol administration significantly raised the plasma concentrations of nitrogenous compounds and increased activities of alcoholic marker enzymes, gamma glutamyl transferase (γGT), plasma transaminases (AST and ALT), alkaline phosphatase (ALP) and lactate dehydrogenase (LDH). Besides, we found abnormalities in the levels of plasma lipids, lipoproteins in alcohol administered rats along with increased lipid peroxidation and nitric oxide (NOx) levels. Moreover, significantly decreased hepatic and kidney antioxidant enzymes, superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx) and the content of reduced glutathione (GSH) in alcohol administered rats were noticed. Administration of AETA to alcoholic rats significantly brought these alterations in plasma to normal and also significantly reduced the levels of lipid peroxidation and restored the enzymic and nonenzymatic antioxidants in liver. These findings were further confirmed by hepatic and kidney histopathological studies. Co-administration of alcohol along with AETA offers protective effect against alcohol-induced stress and these protective effects are due to its multiple actions of its bioactive compounds.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".