ROLE OF OCIMUM CANUM IN PREVENTION OF REPERFUSION-INDUCED RENAL ISCHEMIA IN WISTAR ALBINO RATS
Bibliographic record
Abstract
Acute renal failure is defined as rapid loss of renal function and has been associated with a high mortality rate. Ischemia and reperfusion (I/R) injury of the kidney is the most prominent cause of intrinsic acute renal failure. Activation of reactive oxygen species is implicated in renal ischemia/reperfusion (I/R) injury. This study investigated the anti-ischemic effect of hydro-alcoholic leaf extract of Ocimum canum (OC) against renal I/R injury by its effect on reactive oxygen species. Wistar albino rats were administered different doses of hydro-alcoholic leaf extract of Ocimum canum (OC) before renal ischemia, followed by reperfusion for 24 hours. Serum creatinine, Serum Cystatin C, serum oxaloacetate transaminase (SGOT), serum pyruvate transaminase (SGPT), blood urea nitrogen (BUN) and Lactate dehydrogenase (LDH) were measured for renal dysfunction. Serum and tissue Malondialdehyde (MDA), reduced glutathione (GSH) levels, catalase (CAT), and superoxide dismutase (SOD) levels were measured. Renal sections were analyzed for histological grading of renal injury. Hydro-alcoholic leaf extract of Ocimum canum (OC) significantly reduced increased creatinine, Cystatin C, serum oxaloacetate transaminase (SGOT), serum pyruvate transaminase (SGPT), blood urea nitrogen (BUN) and Lactate dehydrogenase (LDH). Ocimum canum also increased kidney superoxide dismutase activity, catalase and reduced glutathione levels and reduced the malondialdehyde levels. Hydro-alcoholic leaf extract of Ocimum canum reduced histological renal damage. These results suggest that the hydro-alcoholic leaf extract of Ocimum canum reduces renal dysfunction and injury caused by renal I/R.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".