Curcumin (CurcuWIN <sup>TM</sup> ) Improves Antioxidant Capacity and Reduces Inflammation Following Downhill Running‐Induced Muscle Damage
Bibliographic record
Abstract
Exercise (Ex) increases ROS and impairs antioxidant defense systems. Recent data suggest curcumin [CurcuWIN TM Turmeric Extract, minimum 20% curcuminoids] possesses PPAR gamma activity and anti‐inflammatory properties. Therefore, the present study was undertaken to investigate the effects of curcumin (Cur) supplementation on Ex performance, changes in serum and muscle proteins in rats after exhaustive Ex. Twenty‐eight (28) male Wistar rats (8 wk. old, BW:180 ± 20 g) were divided into four treatment groups (i) control [no Ex, Group I (C)] (ii) C + Cur [no Ex, Group II] (iii) C + Ex [Group III] and (iv) C + Ex+ Cur [Group IV]. Cur [100 mg/ kg, contains approximately 20 mg of curcuminoids] was administered daily for 6 wks. The Ex protocols were performed on a motor‐driven rodent treadmill. The animals in the chronic Ex groups were habituated by treadmill Ex over a 5‐d period such as: 1 st day 10 m/min, 10 min, 2 nd day 20 m/min; 10 min, 3 rd day 25 m/min, 10 min, 4 th day 25 m/min, 20 min and 5 th day 25 m/min, 30 min. Animals were exercised at 25 m/min, 45 min/d, 5 d/ week for 6 wks. Blood and muscle samples were analyzed. Running time was improved in Cur groups. Lactate and MDA significantly decreased, lipid profile, SOD, GPx and GSH were improved significantly in Cur treated groups. Significant decrease in NF‐kB and increase in I‐kB, HSP70 and PGC‐1 α in Cur treated rats were observed. These results suggest Curcumin (CurcuWIN TM ) is a potential ingredient of preventing muscle damage and improved exercise performance.
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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) | 0.001 | 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".