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Gene expression differences in bipolar disorder revealed by cDNA array analysis of post‐mortem frontal cortex

2001· article· en· W1483572459 on OpenAlexaff
Yarema Bezchlibnyk, Junfeng Wang, Glenda M. McQueen, L. Trevor Young

Bibliographic record

VenueJournal of Neurochemistry · 2001
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBipolar disorderNeuropathologyBiologyPathophysiologyGene expressionNeuroprotectionMood disordersInternal medicinePathologyNeuroscienceEndocrinologyGeneMedicineGeneticsDiseasePsychiatry

Abstract

fetched live from OpenAlex

Previous studies have implicated a number of biochemical pathways in the etiology of bipolar disorder (BD). However, the precise abnormalities underlying this disorder remain to be established. To investigate novel factors that may be important in the pathophysiology of BD, we utilized cDNA expression arrays to examine differences in expression of up to 1200 genes known to be involved in potentially relevant biochemical processes. This investigation was undertaken in post-mortem samples of frontal cortex tissue from patients with BD and matched controls, obtained (n = 10/group) from the Stanley Foundation Neuropathology Consortium. Results include significant (greater than 35% change in signal intensity) differences between BD and controls in a number of genes (n = 24). Selected targets were analyzed by RT-PCR, which confirmed a decrease in transforming growth factor-beta1 (TGF-beta 1), and an increase in both caspase-8 precursor (casp-8) and transducer of erbB2 (Tob) expression in BD. We further observed a significant decrease of TGF-beta 1 mRNA levels in BD by RT-PCR in individual post-mortem samples. Given the neuroprotective role attributed to this inhibitory cytokine, our results suggest that the down-regulation of TGF-beta 1 may lead to various neurotoxic insults potentially involved in the etiology of certain mood disorders.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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

Citations97
Published2001
Admission routes1
Has abstractyes

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