A synbiotic supplement for inflammation and oxidative stress and lipid abnormalities in hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: Among the most important risk factors for cardiovascular disease in hemodialysis patients are high concentrations of serum inflammation markers, lipid profiles, and oxidative stress. The present study aimed to investigate the effects of a synbiotic supplement on serum systemic inflammation, oxidative stress markers, and lipid profile in hemodialysis patients. METHODS: Fifty hemodialysis patients were randomly allocated to synbiotic and placebo groups. The synbiotic group received 2 tablets per day of a synbiotic supplement (100 mg) Lactobacillus coagulans and fructo-oligosaccharides for 8 weeks; whereas the placebo group received a similar appearing placebo. At the beginning and end of the study, 5 mL blood was taken after 12-14 hours of fasting. FINDINGS: Mean values of serum C-reactive protein (hs-CRP) and malondialdehyde (MDA) significantly decreased in the synbiotic group at the end compared to the beginning of the study (P = 0.01). This reduction was significant in comparison with changes in the placebo group (P = 0.01). The synbiotic supplement also reduced serum total cholesterol (P = 0.001) and low-density lipoprotein cholesterol (LDL-c; P = 0.001) compared to the placebo group. DISCUSSION: The synbiotic supplement used improves serum hs-CRP and MDA, total cholesterol and LDL-c in hemodialysis patients, which are known risk factors for cardiovascular disease.
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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) | 0.002 | 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".