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Record W2028340688 · doi:10.3390/nu5072811

Peculiarities of One-Carbon Metabolism in the Strict Carnivorous Cat and the Role in Feline Hepatic Lipidosis

2013· review· en· W2028340688 on OpenAlexafffund
Adronie Verbrugghe, Marica Bakovic

Bibliographic record

VenueNutrients · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsUniversity of Guelph
FundersOntario Veterinary College, University of Guelph
KeywordsCATSMethionineBiologyGluconeogenesisMetabolismBiochemistryAmino acidTaurineBetaineInternal medicineEndocrinologyMedicine

Abstract

fetched live from OpenAlex

Research in various species has indicated that diets deficient in labile methyl groups (methionine, choline, betaine, folate) produce fatty liver and links to steatosis and metabolic syndrome, but also provides evidence of the importance of labile methyl group balance to maintain normal liver function. Cats, being obligate carnivores, rely on nutrients in animal tissues and have, due to evolutionary pressure, developed several physiological and metabolic adaptations, including a number of peculiarities in protein and fat metabolism. This has led to specific and unique nutritional requirements. Adult cats require more dietary protein than omnivorous species, maintain a consistently high rate of protein oxidation and gluconeogenesis and are unable to adapt to reduced protein intake. Furthermore, cats have a higher requirement for essential amino acids and essential fatty acids. Hastened use coupled with an inability to conserve certain amino acids, including methionine, cysteine, taurine and arginine, necessitates a higher dietary intake for cats compared to most other species. Cats also seemingly require higher amounts of several B-vitamins compared to other species and are predisposed to depletion during prolonged inappetance. This carnivorous uniqueness makes cats more susceptible to hepatic lipidosis.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations71
Published2013
Admission routes2
Has abstractyes

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