116 The Effects of Dietary Phenylalanine on Gastric Emptying, Macronutrient Metabolism and Feed Intake in Healthy Adult Cats
Bibliographic record
Abstract
Abstract Phenylalanine (Phe) consumption may delay gastric emptying (GE) and feed intake. Two studies were conducted using a 2*2 latin square design with 12 healthy male cats to evaluate effects of 1) dietary Phe (PHE, 44mg/kg-BW) compared with an isonitrogenous amount of alanine (ALA, 23.7 mg/kg-BW) on feed intake, and 2) PHE and ALA on GE and GE rate (GER). In study 1, cats were given PHE or ALA 15 minutes before 120% of their daily food and intake was measured. Treatment, day, and their interaction were evaluated using PROC GLIMMIX in SAS. Treatment did not affect rate of food consumption (p >0.05). For study 2, cats were provided PHE or ALA 15 minutes before receiving 100% of their daily food intake for 3 days. On d4, cats received PHE or ALA and subsequently received [1-13C] octanoic acid (5mg/kg BW) on 10g of feed before being fed 50% of their daily food intake. Breath samples were collected to measure 13CO2 enrichment for 12 hours post-meal using indirect calorimetry. Data from cats that returned to baseline enrichment were evaluated for total AUC (GE) and time to peak enrichment (GER). The effect of treatment, body condition score (lean vs. overweight), and their interaction were evaluated using PROC GLIMMIX in SAS. Cats receiving PHE had later peaks (445±71 min) in 13CO2 enrichment (p< 0.05) than cats receiving ALA (244±71 min) but had similar total GE (p >0.10). Obese cats tended (P=0.09) to have greater total GE than lean cats, regardless of treatment. Lean cats on PHE had slower (p< 0.05) emptying rates than lean cats on ALA, but both were similar to obese cats (p >0.05). Overall, Phe did not reduce feed intake or total GE but did delay GE and thus reduced GER. A longer study is warranted to understand whether PHE can control food intake in cats.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".