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Record W2595924333 · doi:10.1038/s41537-017-0014-8

Vulnerability to omega-3 deprivation in a mouse model of NMDA receptor hypofunction

2017· article· en· W2595924333 on OpenAlexafffund
Rehnuma Islam, Marc‐Olivier Trépanier, Marija Milenković, Wendy Horsfall, Ali Salahpour, Richard P. Bazinet, Amy J. Ramsey

Bibliographic record

VenueSchizophrenia · 2017
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsNMDA receptorVulnerability (computing)NeuroscienceOmegaPsychologyReceptorMedicineInternal medicineComputer sciencePhilosophyComputer security

Abstract

fetched live from OpenAlex

Several studies have found decreased levels of ω-3 polyunsaturated fatty acids in the brain and blood of schizophrenia patients. Furthermore, dietary ω-3 supplements may improve schizophrenia symptoms and delay the onset of first-episode psychosis. We used an animal model of NMDA receptor hypofunction, NR1KD mice, to understand whether changes in glutamate neurotransmission could lead to changes in brain and serum fatty acids. We further asked whether dietary manipulations of ω-3, either depletion or supplementation, would affect schizophrenia-relevant behaviors of NR1KD mice. We discovered that NR1KD mice have elevated brain levels of ω-6 fatty acids regardless of their diet. While ω-3 supplementation did not improve any of the NR1KD behavioral abnormalities, ω-3 depletion exacerbated their deficits in executive function. Omega-3 depletion also caused extreme mortality among male mutant mice, with 75% mortality rate by 12 weeks of age. Our studies show that alterations in NMDAR function alter serum and brain lipid composition and make the brain more vulnerable to dietary ω-3 deprivation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.049
GPT teacher head0.328
Teacher spread0.279 · 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 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

Citations17
Published2017
Admission routes2
Has abstractyes

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