Sesame Lignans and Vitamin E Supplementation Improve Subjective Statuses and Anti-Oxidative Capacity in Healthy Humans With Feelings of Daily Fatigue
Bibliographic record
Abstract
Sesamin has anti-oxidative functions in vivo. Fatigue is caused in part by oxidative stress. We evaluated whether sesame lignans (sesamin/episesamin=1/1, 10 mg) with vitamin E (55 mg of alpha-tocopherol) (SVE) could improve subjective statuses and anti-oxidative capacity in humans using questionnaires on fatigue, sleep and physical appearance, as well as low-density lipoprotein oxidation lag time. A placebo-controlled, double-blind, parallel-group study was conducted with subjects experiencing daily fatigue. After a run-in period, subjects were administered oral SVE or a placebo (P) for 8 weeks. A questionnaire regarding fatigue, sleep and physical appearance was conducted at 0, 4, and 8 weeks. Plasma low-density lipoprotein oxidation lag time was measured as an indicator of anti-oxidative capacity. The per-protocol analysis revealed significant improvements in fatigue status at 4 and 8 weeks compared to 0 weeks in both groups (p<0.01), and sleep and physical appearance at 8 weeks compared to 0 weeks only in the SVE group (p<0.01). There were no significant differences observed between the groups. According to the 72-subject subgroup analysis (aged 40 and over), the sleep and physical appearance significantly improved compared to the P group (p<0.05), and fatigue status showed a tendency for improvement compared to the P group. Anti-oxidative capacity in the SVE group significantly increased compared to the P group (p<0.01). No adverse events relating to SVE supplementation were confirmed. These results suggest SVE supplementation could safely alleviate daily fatigue and oxidative stress.
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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".