Effects of feeding or abomasal infusion of canola oil in Holstein cows. 2. Gene expression and plasma concentrations of cholecystokinin and leptin
Bibliographic record
Abstract
We determined the relative importance of cholecystokinin (CCK), leptin, and fatty acid concentrations in plasma in mediating the satiety effects of supplemental fat in lactating cows. Five ruminally and duodenally cannulated Holstein cows in late lactation were used in a 3 x 5 incomplete Latin square design with three treatments: 1) CONTROL: basal diet (CON), 2) CONTROL+supplementation of canola oil at 1 kg/d in the feed (FED) and 3) CONTROL+abomasal infusion of canola oil at 1 kg/d (INF). Relative to CON, feed intake was reduced by INF but not by FED. We provide evidence that both FED and INF treatments stimulated CCK gene expression in the duodenum and elevated plasma CCK concentrations. However, our results did not support a role for CCK in mediating satiety through an endocrine mechanism of action. We speculate that CCK might be acting either through paracrine and/or neurocrine routes to influence feed intake in cattle. Both FED and INF had no effect on the mRNA abundance of leptin, lipoprotein lipase, or acetyl-CoA carboxylase in adipose tissue. Plasma concentrations of leptin, insulin and IGF-I were not altered by FED or INF, indicating that these signals may not be involved in mediating short-term hypophagic effects of dietary fat. Plasma concentrations of 18:1n-9 and 18:2n-6 were significantly greater for INF than for FED or CON. We conclude that the hypophagic effects of supplemental fat in cattle depend on the amount of unsaturated fatty acids reaching the intestine and that this satiety effect is mediated through CCK, oleic acid and (or) linoleic acid, but leptin is not involved.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".