Nutritional and physiological role of medium-chain triglycerides and medium-chain fatty acids in piglets
Bibliographic record
Abstract
Medium-chain fatty acids (MCFAs) are found at higher levels in milk lipids of many animal species and in the oil fraction of several plants, including coconuts, palm kernels and certain Cuphea species. Medium-chain triglycerides (MCTs) and fatty acids are efficiently absorbed and metabolized and are therefore used for piglet nutrition. They may provide instant energy and also have physiological benefits beyond their energetic value contributing to several findings of improved performance in piglet-feeding trials. MCTs are effectively hydrolyzed by gastric and pancreatic lipases in the newborn and suckling young, allowing rapid provision of energy for both enterocytes and intermediary hepatic metabolism. MCFAs affect the composition of the intestinal microbiota and have inhibitory effects on bacterial concentrations in the digesta, mainly on Salmonella and coliforms. However, most studies have been performed in vitro up to now and in vivo data in pigs are still scarce. Effects on the gut-associated and general immune function have been described in several animal species, but they have been less studied in pigs. The addition of up to 8% of a non-esterified MCFA mixture in feed has been described, but due to the sensory properties this can have a negative impact on feed intake. This may be overcome by using MCTs, allowing dietary inclusion rates up to 15%. Feeding sows with diets containing 15% MCTs resulted in a lower mortality of newborns and better development, particularly of underweight piglets. In conclusion, MCFAs and MCTs offer advantages for the improvement of energy supply and performance of piglets and may stabilize the intestinal microbiota, expanding the spectrum of feed additives supporting piglet health in the post-weaning period.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".