Assessment of Opuntia ficus-indica supplementation on enhancing antioxidant levels
Bibliographic record
Abstract
Opuntia ficus-indica (OFi) is a major fruit source prevalent in semiarid and arid regions across various countries worldwide. It is widely recognised for its potential health benefits; however, most studies investigating its effects have been limited to pre-clinical models, highlighting the need for further validation through clinical trials. This study aimed to evaluate the effectiveness of OFi supplementation in enhancing antioxidant levels. Fifty healthy participants, aged 18 years and older, including males and females, received a daily OFi supplement of 1500 mg for 3 months. These findings revealed a significant 48.1% increase in salivary total antioxidant capacity (TAC) (P < 0.001), indicating improved antioxidant activity. Simultaneously, oxidative stress biomarkers showed substantial reductions: malondialdehyde (MDA) decreased by 28.3%, nitrotyrosine (3-NT) decreased by 51.5%, and 8-hydroxy-2'-deoxyguanosine (8-OHdG) decreased by 59.8% (P < 0.001). Furthermore, participants reported a 20.1% improvement (P < 0.001) in Visual Analogue Scales (VAS), reflecting a notable enhancement in overall well-being. In conclusion, OFi exhibited promising efficacy in elevating antioxidant levels and mitigating oxidative stress. These findings suggest its potential as an adjuvant therapy for managing chronic conditions associated with 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".