Protective effects of molsidomine against doxorubicin-induced renal damage in rats
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the therapeutic and protective effects of molsidomine (MLS) against doxorubicin (DOX)-induced renal damage in rats. METHODS: Forty rats were randomly divided into five groups (control, MLS, DOX, DOX+MLS and MLS+DOX groups). Thiobarbituric acid reactive substance (TBARS), reduced glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), nitric oxide (NO) and glutathione peroxidase (GPx) levels were determined from kidney tissues and blood urea nitrogen (BUN), creatinine (Cr) and albumin (Alb) levels also determined. RESULTS: DOX treatment caused a significant increase in TBARS levels and a significant decrease in the GSH and CAT levels compared with the control group. In comparison, MLS administration before DOX injection caused a significant decrease in TBARS levels and also increases in GSH and CAT levels, whereas treatment of MLS after DOX injection did not show any beneficial effect on these parameters. All groups showed a significant increase in NO levels compared to the control group. There were no significant differences among the all groups for BUN and Cr levels. Serum level of Alb decreased in the DOX-treated groups when compared with control and MLS groups. The histopathological findings were in accordance with the biochemical results. MLS treatment reversed the DOX-induced kidney damage in group 4. MLS treatment before DOX injection exerted a protective effect against DOX-induced kidney damage. CONCLUSIONS: MLS shows promise as a possible therapeutic intervention for the prevention of kidney injury associated with DOX treatment. Additional studies are warranted.
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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".