The Effect of Fish Oil Supplement on Serum Antioxidant Level in Patient with Rheumatoid Arthritis
Bibliographic record
Abstract
Purpose:Rheumatoid arthritis (RA) is a common autoimmune disease characterized by inflammation and over-growth of the synovium. RA is accompanied with increased levels of free radical and stress oxidative. In recent years, there has been an increasing interest in nutritional factors on disease and autoimmune system. More recently literatures have emerged that offers contradictory findings about fish oil on antioxidant levels. So far however, there has been little discussion about fish oil as antioxidant on Rheumatoid arthritis. This paper will focus on effect of fish oil over serum antioxidant levels and activity disease of RA.Methods: A randomized double blinded control trial 90 patients from a population of Rheumatoid Arthritis who were selected. Forty five patients received Fish oil (FO) (1gr /day) in addition of conventional therapy for RA versus 45 patients received placebo. And serum levels of plasma antioxidant capacity (TAC) and the activity of superoxide dismutase (SOD) and Glutathione peroxides (GPX) were measured.Results:There was no statistically difference between groups in plasma antioxidant capacity and the activity of superoxide dismutase, Glutathione peroxides. There weren’t any correlation among DAS and antioxidant serum levels.Conclusion: The findings emerging from the present inquiry suggested that FO with 1 gram daily dose didn’t have effect on serum antioxidant level and activity of disease in RA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".