Interactive effect of exercise training with ω-3 supplementation on resting levels of TNF-α and IL-10 in Karat Men
Bibliographic record
Abstract
: Introduction: Perform heavy exercise training, causing a variety of changes including a reduction in performance. Few human studies have been examined of supplemental ω-3 and exercise, then the aim of this study was to study the interactive effect of exercise and ω-3 on resting levels of TNF-α and IL-10 in elite Karate and compared with untrained. Methods: In this quasi-experimental study 42 healthy young male elite karate and non-athletes, were randomly divided into study groups. Athletes: 1- ω -3 and exercise, 2- placebo and exercise,3- exercise and non-athletes: 1- ω -3, 2- placebo,3- control. Athletes groups performed pre-season practice in 65% to 80% VO2 max. Consumption of ω -3 was 1800mg/day for 4 weeks. Blood sampling done 48 hours before, 12 hours fasting after protocol. TNF-α and IL-10 were measured by ELISA and LDL, HDL Enzymatic methods. Results: Exercise training with ω-3 for 4 weeks do not have a significant effect on resting levels of IL-10، TNF- α ،HDL و LDL (P>0.05). Then ω-3 do not have a significant effect in non-athletes (P>0.05). Conclusion: The results showed that Although the consumption of ω-3 do not have significant changes in TNF-α and IL-10, but can slightly reduced TNF-α and increase in the IL-10, that confirm its positive effects on inflammatory factors. However, more research seems necessary in this context.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".