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Record W2119363419 · doi:10.1186/s12917-014-0177-8

Phenylalanine flux and gastric emptying are not affected by replacement of casein with whey protein in the diet of adult cats consuming frequent small meals

2014· article· en· W2119363419 on OpenAlexaff
Tanya J Tycholis, J.P. Cant, V.R. Osborne, Anna K. Shoveller

Bibliographic record

VenueBMC Veterinary Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Guelph
FundersProcter and Gamble
KeywordsCaseinGastric emptyingPhenylalanineStomachAmino acidWhey proteinFood scienceCATSChemistryInternal medicineBiochemistryMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Decreasing the rate of protein emptying from the stomach may improve efficiency of utilization of dietary amino acids for protein deposition. Some studies in rats and humans have shown casein to be more slowly released from the stomach than whey protein. To test if casein induces a slower rate of gastric emptying in cats than whey protein, L-[1-(13)C]phenylalanine (Phe) was dosed orally into 9 adult cats to estimate gastric emptying and whole-body Phe flux. RESULTS: Concentrations of indispensable amino acids in plasma were not significantly affected by dietary protein source. First-pass splanchnic extraction of Phe was not different between diets and averaged 50% (SEM = 3.8%). The half-time for gastric emptying averaged 9.9 min with casein and 10.3 min with whey protein, and was not significantly different between diets (SEM = 1.7 min). Phenylalanine fluxes were 45.3 and 46.5 μmol/(min · kg) for casein- and whey-based diets, respectively (SEM = 4.7 μmol/(min · kg)). CONCLUSIONS: In adult cats fed frequent small meals, the replacement of casein with whey protein in the diet does not affect supply or utilization of amino acids. These two milk proteins appear to be equally capable of meeting the dietary amino acid needs of cats.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.320
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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