Trends in Natural Nutrients for Oxidative Stress and Cell Senescence
Bibliographic record
Abstract
Due to the increase in the aged population and increased life expectancy, the underlying mechanisms involved in the aging process and cell senescence and the ways for modulating these processes in age-related diseases become important. One of the main mechanisms involved in aging and cell senescence, especially in the diseases related to aging, is the oxidative stress process and the following inflammation. Hence, the effects of antioxidants are highlighted in the literature due to their beneficial impacts on inhibiting telomere shortening or DNA damage and other processes related to aging and cell senescence in age-related diseases. Dietary components, foods, and dietary patterns rich in antioxidants can modulate the aging process and delay the progression of some chronic diseases such as cardiovascular diseases, diabetes, and Alzheimer's disease. Foods high in polyphenols, vitamin C, or carotenoids, olive oil, seeds, nuts, legumes, dietary supplements such as CoQ10, and some other dietary factors are the most important nutritional sources that have high antioxidant contents which can positively affect cell senescence and disease progression. Plant dietary patterns including Mediterranean diets can also inhibit telomere shortening following oxidative damages, and this can delay cell aging and senescence in age-related diseases. Further, olive oil can inhibit protein aggregation in Alzheimer's disease. It can be concluded that nutrition can delay the process of cell senescence in age-related diseases via inhibiting oxidative and inflammatory pathways. However, more studies are needed to better clarify the underlying mechanisms of nutrition and dietary components on cell senescence, aging, and disease progression, especially those related to age.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".