Apoptosis perturbations and expression of regulatory inflammatory factors in cisplatin-depleted rat livers under <scp>l</scp>-arginine protection
Bibliographic record
Abstract
Hepatic injury is one of the most common complications associated with cisplatin (CIS) use. Recently, liver protection lines are being discovered to stop the hepatic cell death due to inflammatory and apoptotic perturbations. l-arginine has protective effects in several models of liver injury. This study was designed to investigate the possible protective effect of l-arginine against CIS-induced acute hepatic injury in rats. Rats were divided into 4 groups: control, l-arginine, CIS, l-arginine + CIS. Liver function, oxidative stress, inflammatory cytokines, and apoptosis markers were assessed. l-arginine pretreatment protected the liver against CIS-induced toxicity as indicated by significantly alleviating the changes in liver function along with restoration of the antioxidant status. This finding was confirmed with the markedly improved pathological changes. l-arginine showed anti-inflammatory effect through the reduction of liver expression of iNOS, TNF-α, and NF-κβ, which were ameliorated to significant levels. Furthermore, l-arginine administration downregulated the liver expression of the apoptotic marker, caspase-3. The results recommend l-arginine as a hepatoprotective agent against CIS toxicity. Mostly, this hepatoprotective effect of l-arginine involved anti-inflammatory and anti-apoptotic activities.
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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 | 0.001 |
| 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".