Role of turmeric in oxidative modulation in end‐stage renal disease patients
Bibliographic record
Abstract
Oxidative stress is considered as a major player in uremia-associated morbidity and mortality in hemodialysis (HD) patients. The aim of this study was to evaluate the effects of turmeric on oxidative stress markers in HD patients. This study was a prospective and double-blind randomized clinical trial. Fifty HD patients aged 18-60 years were recruited after fulfilling the inclusion criteria. Patients were randomly categorized into 2 groups: trial group received turmeric and control group received placebo for 8 weeks. Each patient in the trial group received turmeric, whereas the control group received starch for the same 8 weeks. Plasma malondialdehyde (MDA), red blood cell (RBC) antioxidant enzyme activities as glutathione peroxidase (GPX), glutathione reductase (GR), and catalase (CAT), cholesterol, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, triglyceride, albumin, and hemoglobin were also measured before and after study. Although MDA level was reduced in both groups, the ratio of decrease was significantly higher in the turmeric group (0.2 vs. 0.1, P = 0.040). Three enzymes of GPX, GR, and CAT levels were increased in both groups; the ratio of increased was significantly higher in the turmeric group for the CAT enzyme (0.73 vs. 0.54; P = 0.02). Also, significant elevation of albumin level in the turmeric group compared with the control group was observed (P = 0.001). Regular ingestion of turmeric reduces plasma MDA and increases RBC CAT activity and plasma albumin levels in HD patients. Turmeric showed no adverse effects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".