The Importance of Dietary Antioxidants on Oxidative Stress, Meat and Milk Production, and Their Preservative Aspects in Farm Animals: Antioxidant Action, Animal Health, and Product Quality—Invited Review
Bibliographic record
Abstract
The biological effects of oxidative stress and associated free radicals on farm animal performance, productivity, and product quality may be managed via dietary interventions-specifically, the provision of feeds, supplements, and forages rich in antioxidants. To optimize this approach, it is important first to understand the development of free radicals and their contributions to oxidative stress in tissue systems of farm animals or the human body. The interactions between prooxidants and antioxidants will impact redox homeostasis and, therefore, the well-being of farm animals. The impact of free radical formation on the oxidation of lipids, proteins, DNA, and biologically important macromolecules will likewise impact animal performance, meat and milk quality, nutritional value, and longevity. Dietary antioxidants, endogenous antioxidants, and metal-binding proteins contribute to the 'antioxidant defenses' that control free radical formation within the biological systems. Different bioactive compounds of varying antioxidant potential and bio-accessibility may be sourced from tailored feeding systems. Informed and successful provision of dietary antioxidants can help alleviate oxidative stress. However, knowledge pertaining to farm animals, their unique biological systems, and the applications of novel feeds, specialized forages, bioactive compounds, etc., must be established. This review summarized current research to direct future studies towards more effective controls for free radical formation/oxidative stress in farm animals so that productivity and quality of meat and milk can be optimized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".