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Record W2218888203 · doi:10.1038/srep15791

Plasma non-esterified docosahexaenoic acid is the major pool supplying the brain

2015· article· en· W2218888203 on OpenAlexafffund
Chuck T. Chen, Alex P. Kitson, Kathryn E. Hopperton, Anthony F. Domenichiello, Marc‐Olivier Trépanier, Lauren Lin, Leonardo Ermini, Martin Post, Frank Thiès, Richard P. Bazinet

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsNEFADocosahexaenoic acidLysophosphatidylcholineChemistryInternal medicineFatty acidMetabolismEndocrinologyBlood plasmaBiochemistryBiologyMedicinePolyunsaturated fatty acidPhosphatidylcholinePhospholipid

Abstract

fetched live from OpenAlex

Despite being critical for normal brain function, the pools that supply docosahexaenoic acid (DHA) to the brain are not agreed upon. Using multiple kinetic models in free-living adult rats, we first demonstrate that DHA uptake from the plasma non-esterified fatty acid (NEFA) pool predicts brain uptake of DHA upon oral administration, which enters the plasma NEFA pool as well as multiple plasma esterified pools. The rate of DHA loss by the brain is similar to the uptake from the plasma NEFA pool. Furthermore, upon acute iv administration, although more radiolabeled lysophosphatidylcholine (LPC)-DHA enters the brain than NEFA-DHA, this is due to the longer plasma half-life and exposure to the brain. Direct comparison of the uptake rate of LPC-DHA and NEFA-DHA demonstrates that uptake of NEFA-DHA into the brain is 10-fold greater than LPC-DHA. In conclusion, plasma NEFA-DHA is the major plasma pool supplying the brain.

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)0.000
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.043
GPT teacher head0.322
Teacher spread0.278 · 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

Citations132
Published2015
Admission routes2
Has abstractyes

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