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How do infants acquire creatine?

2012· article· en· W3176428003 on OpenAlexafffund
Margaret E. Brosnan, Erica E. Edison, John T. Brosnan

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsCreatineBreast milkArginineInfant formulaMethionineAnimal scienceAmino acidMedicineGlycineBreast feedingPediatricsEndocrinologyChemistryBiologyBiochemistry

Abstract

fetched live from OpenAlex

A growing infant acquires creatine (CR) from mother's milk (or infant formula) and/or de novo synthesis. We have measured CR and guanidinoacetate (GAA) content of mother's milk and of a number of commonly used infant formulas. We used these data to estimate CR intake by breast‐fed and formula‐fed infants and to compare these estimates to the infants’ daily requirements for CR. Our data reveal that diet is a relatively minor source of CR in breast‐fed infants; therefore, the great bulk of new CR must be synthesized by the infant. The GAA concentration in milk and formulas is extremely low. The situation with formula‐fed infants is more complex. The CR levels in cows’ milk‐based formulas are higher than in mothers’ milk so that the burden of CR synthesis is less in infants fed these formulas than in breast‐fed infants. Soy‐based formulas, however, contain essentially no CR; infants fed these formulas must synthesize all of their CR endogenously. These data have implications for infants suffering from CR‐deficiency syndromes as well as for the burden that CR synthesis places on infants’ glycine, arginine, and methionine metabolism (the amino acids required for CR synthesis). Funded by CIHR

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2012
Admission routes2
Has abstractyes

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