Intravenous glucagon like peptide-1 infusion does not affect dry matter intake or hypothalamic mRNA expression of neuropeptide Y, agouti related peptide and proopiomelatnocortin in wethers
Bibliographic record
Abstract
Relling, A. E., Loerch, S. C. and Reynolds, C. K. 2014. Intravenous glucagon like peptide-1 infusion does not affect dry matter intake or hypothalamic mRNA expression of neuropeptide Y, agouti related peptide and proopiomelanocortin in wethers. Can. J. Anim. Sci. 94: 357–362. The objectives of the present study were to determine the effects of jugular vein infusions of glucagon like peptide-1 (GLP-1) and dietary fat inclusion on dry matter intake, nutrient digestibility and hypothalamic mRNA concentration of neuropeptide Y, agouti related peptide, and proopiomelanocortin in growing sheep. Thirty-six wethers were used (40.7±3.3 kg BW). Treatments were a control diet (n=11), dietary addition (6% of dry matter) of Ca salts of palm oil fatty acids (n=12), or 6-d jugular vein infusions of 0.155 µg kg−1 body weight/day of GLP-1 (n=11). Hormone concentrations were measured in jugular vein plasma from samples taken on days 1, 4 and 6. On day 7, the wethers were slaughtered for hypothalamus collection to measure mRNA concentration. The dietary addition of 6% of Ca salts of palm oil increased plasma GLP-1 concentration (P<0.01) and decreased dry matter intake on day 1, but not on day 6 (time×treatment interaction, P<0.05). The infusion of GLP-1 did not change dry matter intake (P>0.20), but increased neutral detergent fibre digestibility (P<0.01). In conclusion, glucagon like peptide-1 infusion or feeding fat did not decrease dry matter intake or affect hypothalamic neuropeptide mRNA concentrations of sheep.
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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) | 0.001 | 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 | 0.000 | 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".