The effects of nanocurcumin supplementation on inflammation in hemodialysis patients: A randomized controlled trial
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Serum levels of several pro-inflammatory cytokines are higher in hemodialysis patients compared to healthy people. Curcumin has been shown to be able to decrease cytokines levels in nonuremic subjects. Our goal was to evaluate the effect of nanocurcumin administration on cytokines levels in hemodialysis patients. METHODS: The study was performed over a 3 months period on 54 hemodialysis patients who had been randomized to receive either nanocurcumin or placebo. Serum levels and gene expressions of tumor necrosis factor-alpha (TNF-α) and interleukin 6 (IL-6) were evaluated using enzyme-linked immunosorbent assay (ELISA) and real-time polymerase chain reaction (RT-PCR). FINDINGS: Serum levels of IL-6 and TNF-α were similar in the two groups at baseline but were lower after 12 weeks of treatment with nanocurcumin compared to placebo (P = 0.024 for IL-6 and 0.02 for TNF). In the group given nanocurcumin, serum levels of both cytokines decreased substantially (P < 0.001 for each), whereas they were unchanged in the group given placebo. Gene expression for each cytokine in peripheral blood mononuclear cells (PBMCs) was reduced at 12 weeks vs. baseline in the group given nanocurcumin, and changes in gene expression correlated with changes in serum level for each of the two cytokines. DISCUSSION: The results indicate that nanocurcumin supplementation reduces both serum levels and gene expression of IL-6 and TNF-α in hemodialysis patients. The feasibility and potential clinical benefits of nanocurcumin treatment to reduce inflammation in hemodialysis patients warrant further study.
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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 | 0.001 | 0.004 |
| 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 it