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Record W1994856266 · doi:10.1038/jcbfm.2015.29

Rapid Adaptation of Rat Brain and Liver Metabolism to a Ketogenic Diet: An Integrated Study Using <sup>1</sup> H- and <sup>13</sup> C-NMR Spectroscopy

2015· article· en· W1994856266 on OpenAlexaff
Maggie Roy, Marie-Christine Beauvieux, Jérôme Naulin, Dounia El Hamrani, Jean‐Louis Gallis, Stephen C. Cunnane, Anne‐Karine Bouzier‐Sore

Bibliographic record

VenueJournal of Cerebral Blood Flow & Metabolism · 2015
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsKetogenic dietKetone bodiesLeucineMetabolismChemistryEndocrinologyInternal medicineGlutamate receptorMetaboliteIsoleucineEpilepsyAmino acidBiochemistryBiologyMedicineReceptor

Abstract

fetched live from OpenAlex

The ketogenic diet (KD) is an effective alternative treatment for refractory epilepsy in children, but the mechanisms by which it reduces seizures are poorly understood. To investigate how the KD modifies brain metabolism, we infused control (CT) and 7-day KD rats with either [1-(13)C]glucose (Glc) or [2,4-(13)C2]β-hydroxybutyrate (β-HB). Specific enrichments of amino acids (AAs) measured by (1)H- and (13)C-NMR in total brain perchloric acid extracts were similar between CT and KD rats after [1-(13)C]Glc infusion whereas they were higher in KD rats after [2,4-(13)C2]β-HB infusion. This suggests better metabolic efficiency of ketone body utilization on the KD. The relative rapid metabolic adaptation to the KD included (1) 11%-higher brain γ-amino butyric acid (GABA)/glutamate (Glu) ratio versus CT, (2) liver accumulation of the ketogenic branched-chain AAs (BCAAs) leucine (Leu) and isoleucine (ILeu), which were never detected in CT, and (3) higher brain Leu and ILeu contents. Since Glu and GABA are excitatory and inhibitory neurotransmitters, respectively, higher brain GABA/Glu ratio could contribute to the mechanism by which the KD reduces seizures in epilepsy. Increased BCAA on the KD may also contribute to better seizure control.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.286
Teacher spread0.249 · 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.

Study designQualitative
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

Citations31
Published2015
Admission routes1
Has abstractyes

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