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Record W2081012618 · doi:10.1017/s0007114510000097

Dietary supplementation with medium-chain TAG has long-lasting cognition-enhancing effects in aged dogs

2010· article· en· W2081012618 on OpenAlexaff
Yuanlong Pan, Brian Larson, Joseph A. Araujo, Winnie Lau, Christina de Rivera, Ruben Santana, Asa M. Gore, Norton W. Milgram

Bibliographic record

VenueBritish Journal Of Nutrition · 2010
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsCognitionMedicineFood scienceGerontologyPhysiologyBiologyPsychiatry

Abstract

fetched live from OpenAlex

The present study focused on the hypothesis that dietary supplementation with medium-chain TAG (MCT) will improve cognitive function in aged dogs by providing the brain with energy in the form of ketones. Aged Beagle dogs were subjected to a baseline battery of cognitive tests, which were used to establish cognitively equivalent control or treatment groups. The dogs in the treatment group were maintained on a diet supplemented with 5.5 % MCT. After an initial wash-in period, all the dogs were tested with a battery of cognitive test protocols, which assessed sequentially landmark discrimination learning ability, egocentric visuospatial function and attention. The groups were maintained on the diets for 8 months. The MCT-supplemented group showed significantly better performance in most of the test protocols than the control group. The group differences also varied as a function of task difficulty, with the more difficult task showing greater supplementation effects than the easier tasks. The group given the MCT supplement showed significantly elevated levels of beta-hydroxybutyrate, a ketone body. These results indicate, first, that long-term supplementation with MCT can have cognition-improving effects, and second, that MCT supplementation increases circulating levels of ketones. The results support the hypothesis that brain function of aged dogs can be improved by MCT supplementation, which provides the brain with an alternative energy source.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.525

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 integrity0.0000.001
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 head0.014
GPT teacher head0.262
Teacher spread0.248 · 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 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

Citations138
Published2010
Admission routes1
Has abstractyes

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