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Record W2024102173 · doi:10.1194/jlr.m055590

Chronic dietary n-6 PUFA deprivation leads to conservation of arachidonic acid and more rapid loss of DHA in rat brain phospholipids

2014· article· en· W2024102173 on OpenAlexafffund
Lauren Lin, Chuck T. Chen, Kayla D. Hildebrand, Zhen Liu, Kathryn E. Hopperton, Richard P. Bazinet

Bibliographic record

VenueJournal of Lipid Research · 2014
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaHospital for Sick Children
KeywordsArachidonic acidPolyunsaturated fatty acidDocosahexaenoic acidEndocrinologyInternal medicineMetabolismChemistryPhospholipidBiologyFatty acidBiochemistryMedicineEnzyme

Abstract

fetched live from OpenAlex

To determine how the level of dietary n-6 PUFA affects the rate of loss of arachidonic acid (ARA) and DHA in brain phospholipids, male rats were fed either a deprived or adequate n-6 PUFA diet for 15 weeks postweaning, and then subjected to an intracerebroventricular infusion of (3)H-ARA or (3)H-DHA. Brains were collected at fixed times over 128 days to determine half-lives and the rates of loss from brain phospholipids (J out). Compared with the adequate n-6 PUFA rats, the deprived n-6-PUFA rats had a 15% lower concentration of ARA and an 18% higher concentration of DHA in their brain total phospholipids. Loss half-lives of ARA in brain total phospholipids and fractions (except phosphatidylserine) were longer in the deprived n-6 PUFA rats, whereas the J out was decreased. In the deprived versus adequate n-6 PUFA rats, the J out of DHA was higher. In conclusion, chronic n-6 PUFA deprivation decreases the rate of loss of ARA and increases the rate of loss of DHA in brain phospholipids. Thus, a low n-6 PUFA diet can be used to target brain ARA and DHA metabolism.

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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.253
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.058
GPT teacher head0.383
Teacher spread0.326 · 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 designBench or experimental
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

Citations48
Published2014
Admission routes2
Has abstractyes

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