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Record W2107809088 · doi:10.1194/jlr.m700406-jlr200

Chronic N-methyl-d-aspartate administration increases the turnover of arachidonic acid within brain phospholipids of the unanesthetized rat

2007· article· en· W2107809088 on OpenAlexaff
Ho‐Joo Lee, Jagadeesh S. Rao, Lisa Chang, Stanley I. Rapoport, Richard P. Bazinet

Bibliographic record

VenueJournal of Lipid Research · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Toronto
FundersNational Institutes of Health
KeywordsArachidonic acidChemistryArachidonic acid metabolismEndocrinologyMetabolismInternal medicinePharmacologyBiochemistryMedicineEnzyme

Abstract

fetched live from OpenAlex

Whereas antibipolar drug administration to rats reduces brain arachidonic acid turnover, excessive N-methyl-d-aspartate (NMDA) signaling is thought to contribute to bipolar disorder symptoms and may increase arachidonic acid turnover in rat brain phospholipids. To determine whether chronic NMDA would increase brain arachidonic acid turnover, rats were daily administered NMDA (25 mg/kg, ip) or vehicle for 21 days. In unanesthetized rats, on day 21, [1-(14)C]arachidonic acid was infused intravenously and arterial blood plasma was sampled until the animal was euthanized at 5 min and its microwaved brain was subjected to chemical and radiotracer analysis. Using equations from our in vivo fatty acid model, we found that compared with controls, chronic NMDA increased the net rate of incorporation of plasma unesterified arachidonic acid into brain phospholipids (25-34%) as well as the turnover of arachidonic acid within brain phospholipids (35-58%). These changes were absent at 3 h after a single NMDA injection. The changes, opposite to those after chronic administration of antimanic drugs to rats, suggest that excessive NMDA signaling via arachidonic acid may be a model of upregulated arachidonic acid turnover in brain phospholipids.

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)0.012
metaresearch head score (Gemma)0.007
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 score0.007
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.002
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 head0.080
GPT teacher head0.411
Teacher spread0.331 · 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

Citations45
Published2007
Admission routes1
Has abstractyes

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