Dietary amylose and amylopectin ratio and resistant starch content affects plasma glucose, lactic acid, hormone levels and protein synthesis in splanchnic tissues
Bibliographic record
Abstract
Experiments were conducted to determine the effects of feeding different starch sources on piglets. Four diets were formulated with maize, brown rice, sticky rice and Hi-Maize 1043 as starch sources, with resistant starch (RS) contents of 2.3%, 0.9%, 0.0%, 20.6%, and amylose and amylopectin ratio of 0.23%, 0.21%, 0.18%, 0.06% respectively. Fifty-six pigs weaned at 28 days of age were randomly assigned to one of the four diets. In Exp. 1, six piglets in each group were fitted with an indwelling jugular catheter. After 25 days of feeding trial, venous blood samples were obtained at time zero and every 1 h for 4 h. In Exp. 2, the remaining piglets were used to determine the effects of different starch sources on the fractional synthesis rate (FSR). The results indicated that feeding the Hi-Maize 1043 diet decreased (p < 0.05) plasma contents of glucose, insulin, lactic acid and T(3), while sticky rice increased plasma contents of glucose and insulin. The insulin contents in piglets fed the sticky rice diet was 69.2 microIU/ml at 1 h post-feeding which was highest among the starch diets. The FSR in the pancreas, spleen, duodenum, jejunum, ileum and colon in the corn group were much higher (p < 0.05) than that in the sticky rice group. These results suggest that RS is potentially beneficial for improving insulin sensitivity in young pigs and that the ratio of amylose and amylopectin have significantly effects on the FSR in splanchnic tissues in weaned piglets. Another finding of this study indicated maize with a ratio of amylose and amylopectin of 0.23 has the best starch sources for pig 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".