(47) Dietary amino acid and protein content and effects on pig health and growth
Bibliographic record
Abstract
Abstract Diet composition can have significant impacts on the health and productivity of pigs. In particular, dietary protein and amino acid content affect pig gastrointestinal health, immune status, inflammation, and antioxidant status in addition to growth performance and environmental and economic sustainability of pork production. Excess dietary protein can negatively impact gastrointestinal health, as a result of increased protein fermentation resulting in production of harmful metabolites that alter the intestinal epithelium and increase susceptibility to enteric pathogens. In addition, due to the inherent inefficient of nitrogen utilization, high protein diets increase nitrogen excretion into the environment. In an effort to reduce the negative impact of dietary protein, diets are generally formulated to reduce total protein while maintaining essential amino acid content to meet requirements for growth. This strategy has generally mitigated the negative impact of high protein while maintaining animal performance, however, in some instances, reduced protein diets have negatively impacted growth, even though essential amino acid requirements are met. Studies have demonstrated that this may be due to insufficient nitrogen or non-essential amino acid content, limiting protein deposition. Moreover, the effects of reducing dietary protein content on gastrointestinal health has been inconsistent, with some studies showing little or no benefit, suggesting that a factor other than total protein, such as the indigestible protein fraction, is responsible. While the importance of essential amino acids is widely known, current amino acid requirements are based on growth in healthy pigs. More recently, the roles amino acids play in non-growth functions, such as immune response and gastrointestinal health, have garnered considerable attention. Some amino acids, such as methionine, tryptophan, threonine, glutamine, and arginine, among others, are referred to as functional amino acids and have been shown to have a significant impact on animal health and development, with studies demonstrating an increase in the requirement for these amino acids under certain challenge conditions, such as disease. It is becoming increasingly important to understand that nutrient requirements, including amino acids, differ depending on the desired outcome (e.g., mortality, intestinal barrier function, immune status) and, therefore, requirements developed in healthy, growing pigs are likely not appropriate in all situations. Overall, an understanding of how dietary protein and amino acids impact pig health in addition to growth will be necessary to maintain productivity of pork production.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".