Therapeutic activity of green synthesized selenium nanoparticles from turmeric against cisplatin-induced oxido-inflammatory stress, and cell death in mice kidney
Bibliographic record
Abstract
Abstract Cisplatin (CDDP) is a commonly prescribed chemotherapeutic agent; however, its associated nephrotoxicity limits its clinical efficacy and sometimes requires discontinuation of its use. The existing study was designed to explore the reno-therapeutic efficacy of turmeric (Tur) alone or conjugated with selenium nanoparticles (Tur-SeNPs) against CDDP-mediated renal impairment in mice and the mechanisms underlying this effect. Mice were orally treated with Tur extract (200 mg/kg) or Tur-SeNPs (0.5 mg/kg) for 7 days after administration of a single dose of CDDP (5 mg/kg, i.p.). N-acetyl cysteine NAC (100 mg/kg) was used as a standard antioxidant compound. The results revealed that Tur-SeNPs counteracted CDDP-mediated serious renal effects in treated mice. Compared with the controls, Tur or Tur-SeNPs therapy remarkably decreased the kidney index along with the serum levels of urea, creatinine, Kim-1, and NGAL of the CDDP-injected mice. Furthermore, Tur-SeNPs ameliorated the renal oxidant status of CDDP group demonstrated by decreased MDA and NO levels along with elevated levels of SOD, CAT, GPx, GR, GSH, and gene expression levels of HO-1. Noteworthy, lessening of renal inflammation was exerted by Tur-SeNPs via lessening of IL-6 and TNF-α besides down-regulation of NF-κB gene expression in mouse kidneys. Tur-SeNPs treatment also restored the renal histological features attained by CDDP challenge and hindered renal apoptosis through decreasing the Bax levels and increasing Bcl-2 levels. Altogether, these outcomes suggest that the administration of Tur conjugated with SeNPs is effective neoadjuvant chemotherapy to guard against the renal adverse effects that are associated with CDDP therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".