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Record W2118998411 · doi:10.1503/jpn.100175

Kynurenine 3-monooxygenase polymorphisms: relevance for kynurenic acid synthesis in patients with schizophrenia and healthy controls

2011· article· en· W2118998411 on OpenAlexvenueno aff
M. Holtze, Peter Saetre, Göran Engberg, Lilly Schwieler, Thomas Werge, Ole A. Andreassen, Håkan Hall, Lars Terenius, Ingrid Agartz, Erik G. Jönsson, Martin Schalling, Sophie Erhardt

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2011
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsnot available
Fundersnot available
KeywordsKynurenic acidKynurenineKynurenine pathwaySchizophrenia (object-oriented programming)Relevance (law)MedicinePsychologyInternal medicinePsychiatryChemistryPharmacologyBiochemistryTryptophanAmino acidPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with schizophrenia show increased brain and cerebrospinal fluid (CSF) concentrations of the endogenous N-methyl-D-aspartate receptor antagonist kynurenic acid (KYNA). This compound is an end-metabolite of the kynurenine pathway, and its formation indirectly depends on the activity of kynurenine 3-monooxygenase (KMO), the enzyme converting kynurenine to 3-hydroxykynurenine. METHODS: We analyzed the association between KMO gene polymorphisms and CSF concentrations of KYNA in patients with schizophrenia and healthy controls. Fifteen single nucleotide polymorphisms (SNPs) were selected covering KMO and were analyzed in UNPHASED. RESULTS: We included 17 patients with schizophrenia and 33 controls in our study. We found an association between a KMO SNP (rs1053230), encoding an amino acid change of potential importance for substrate interaction, and CSF concentrations of KYNA. LIMITATIONS: Given the limited sample size, the results are tentative until replication. CONCLUSION: Our results suggest that the nonsynonymous KMO SNP rs1053230 influences CSF concentrations of KYNA.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.020
GPT teacher head0.239
Teacher spread0.219 · 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

Citations78
Published2011
Admission routes1
Has abstractyes

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