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Record W2992199908 · doi:10.1038/s41537-019-0089-5

Mitochondrial DNA content and oxidation in bipolar disorder and its role across brain regions

2019· article· en· W2992199908 on OpenAlexaff
David F. Bodenstein, Harry K.W. Kim, Nicole C. Brown, Bushra Navaid, L. Trevor Young, Ana C. Andreazza

Bibliographic record

VenueSchizophrenia · 2019
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsBipolar disorderHippocampusCerebellumSchizophrenia (object-oriented programming)Mitochondrial DNAPrefrontal cortexNeurosciencePsychologyBiologyPsychiatryCognitionGeneticsGene

Abstract

fetched live from OpenAlex

The underlying pathology of bipolar disorder remains unknown, though evidence is accumulating to support a role of mitochondrial dysfunction. In this study, we aim to investigate electron transport chain complex I subunit NDUFS7 protein expression; mtDNA content; common deletion; and oxidation in the Broadmann area 24 (BA24), cerebellum, hippocampus, and prefrontal cortex from patients with bipolar disorder, schizophrenia, and non-psychiatric controls. Here, we demonstrate no changes in NDUFS7 in BA24, cerebellum or hippocampus, increases in mtDNA content in hippocampus of patients with bipolar disorder, and decreases in mtDNA oxidation in patients with bipolar disorder and schizophrenia, respectively. Paired analysis between BA24 and cerebellum reveal increases within NDUFS7 levels and mtDNA content in cerebellum of patients with bipolar disorder or schizophrenia. We found a positive correlation between NDUFS7 and mtDNA content (ND4 and ND5) when combining brain regions. Our study supports the involvement of mitochondrial dysfunction in bipolar disorder and schizophrenia.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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

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 head0.014
GPT teacher head0.259
Teacher spread0.245 · 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

Citations33
Published2019
Admission routes1
Has abstractyes

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