Regulation of redox enzymes by nutraceuticals: a review of the roles of antioxidant polyphenols and peptides
Bibliographic record
Abstract
Redox enzymes are essential components of the cellular defence system against oxidative stress, which is a common factor in various diseases. Therefore, understanding the role of bioactive nutraceuticals in modulating the activity of these enzymes holds immense therapeutic potential. This paper provides a comprehensive review of the regulation of redox enzymes in cell and animal models by food-derived bioactive nutraceuticals, focusing on polyphenols and peptides. Specifically, this paper discusses the regulation of superoxide dismutase (SOD), glutathione peroxidase (GPx), catalase (CAT), NAPDH oxidase, xanthine oxidase (XO), myeloperoxidase (MPO), and haem oxygenase (HO) in cell and animal models. Polyphenols, which are abundant in fruits, vegetables, and beverages, have diverse antioxidant properties, including direct scavenging of reactive oxygen species and regulation of transcription factors such as nuclear factor erythroid 2-related factor 2, which leads to the increased expression of the redoxenzymes SOD, HO, and GPx. Similarly, bioactive peptides from various food proteins can enhance antioxidative enzyme activity by regulating gene expression and directly activating the enzyme CAT. In other cases, an antioxidative response requires the downregulation or inhibition of the redox enzymes XO, MPO, and NAPDH oxidase. This paper highlights the potential of bioactive nutraceuticals in mitigating oxidative stress-related diseases and their mechanisms in modulating the redox enzyme expression or activity. Furthermore, the review highlights the need for further research to uncover new therapeutic strategies using nutraceuticals for enhancing cellular antioxidant defence mechanisms and improving health outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".