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Protein Requirements in Children with Phenylketonuria (PKU)

2015· article· en· W978641683 on OpenAlexafffund
Abrar Turki, Keiko Ueda, Barbara Cheng, Alette Giezen, Sylvia Stöckler, Rajavel Elango

Bibliographic record

VenueThe FASEB Journal · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsUniversity of British ColumbiaBC Children's Hospital
FundersRare Disease Foundation
KeywordsPhenylalanineChristian ministryLeucinePhenylketonuriasMedicineAmino acidInternal medicinePediatricsChemistryEndocrinologyBiochemistry

Abstract

fetched live from OpenAlex

Phenylketonuria (PKU) is an inherited inborn error of phenylalanine (PHE) metabolism caused by deficiency of the hepatic enzyme, phenylalanine hydroxyls (PAH). Therefore PHE accumulates in plasma leading to developmental delay. The major mode of treatment is nutritional management with dietary restriction of PHE and provision of sufficient protein. The dietary protein requirement in children with PKU is unknown, and currently predicted by a factorial calculation.Our objective was to determine the protein requirements in children with PKU using the indicator amino acid oxidation (IAAO) technique. Four PKU children (5-18y) have been recruited to participate in test protein intakes ranging from deficiency to excess (0.2 – 3.2 g/kg/d) with the IAAO protocol. Each study day, the test protein was provided as 8 hourly meals. With the fifth meal L-[1-13C]leucine was provided orally with collection of breath and urine samples.The protein requirement was determined by using a two-phase linear regression analysis on the change in breath 13C enrichment, reflecting protein synthesis.The mean protein requirement was determined to be 1.93g/kg/d and is significantly higher than the most recent 2014 recommendations (1.14 – 1.33g/kg/d, based on 120-140% above current RDA). These initial findings are the first to directly define a quantitative requirement for protein in children with PKU, and indicate that current recommendations may be underestimated. (Supported by Rare Disease Foundation; Ministry of Higher Education in Saudi Arabia and Saudi Arabian Cultural Bureau)

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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