Oregano (Origanum vulgare) Consumption Reduces Oxidative Stress and Markers of Muscle Damage after Combat Readiness Tests in Soldiers
Bibliographic record
Abstract
Military activities often involve high-intensity exercise that can disrupt antioxidant capacity. We investigated the effects of oregano supplementation on muscle damage, oxidative stress, and plasma antioxidant markers of soldiers performing the army combat readiness test (ACRT). Twenty-four healthy male soldiers (age: 24 ± 3 years, height: 167 ± 14 cm, mass: 66 ± 3 kg) were randomized into a placebo group (n = 12) or an oregano supplementation group (n = 12). The participants consumed a capsule containing 500 mg Origanum vulgare immediately after completing the ACRT. Blood sampling was taken before exercise, immediately after exercise, and 60 and 120 min after oregano consumption. Plasma levels of creatine kinase (CK), lactate dehydrogenase (LDH), malondialdehyde (MDA), superoxide dismutase (SOD), total antioxidant capacity (TAC), and glutathione peroxidase (GPX) were measured at the four time points. The time × group interactions were found for CK (p < 0.0001, d = 3.64), LDH (p < 0.0001, d = 1.64), MDA (p < 0.0001, d = 9.94), SOD (p < 0.0001, d = 1.88), TAC (p < 0.0001, d = 5.68) and GPX (p < 0.0001, d = 2.38). In all variables, the difference between placebo and oregano groups were significant at 60 (p < 0.0001) and 120 (p < 0.0001) minutes after ACRT test. The main effect of time was also significant for all the variables (p < 0.0001). Our results suggest that oregano supplementation has the potential to reduce muscle damage and increase oxidative capacity following ACRT. Supplementation with oregano may serve as a dietary strategy to increase preparedness and promote recovery in military recruits.
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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".