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Record W1978683702 · doi:10.1002/syn.20211

Increased expression of calcium/calmodulin-dependent protein kinase IIβ in frontal cortex in schizophrenia and depression

2005· article· en· W1978683702 on OpenAlexaff
Gabriela Novak, Philip Seeman, Teresa Tallerico

Bibliographic record

VenueSynapse · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Toronto
FundersNational Institute on Drug Abuse
KeywordsSchizophrenia (object-oriented programming)Synaptic pruningCalmodulinDepression (economics)Gene expressionspliceNeuroscienceCerebral cortexCortex (anatomy)KinasePrefrontal cortexFrontal lobeProtein kinase AInternal medicineBiologyEndocrinologyGeneCalciumMedicineCell biologyPsychiatryBiochemistryCognition

Abstract

fetched live from OpenAlex

In searching for genes dysregulated in schizophrenia, we measured the expression of the two splice variants of calcium/calmodulin-dependent protein kinase II (CaMKIIalpha and CaMKIIbeta) in postmortem frontal cerebral cortex tissues from patients who had died with schizophrenia, bipolar disorder, or severe depression. The mRNA levels of expression of these two splice variants were measured by real-time Quantitative PCR, using an Mx4000 instrument. The values for the expression of CaMKIIalpha and CaMKIIbeta were normalized by the expression of beta-glucuronidase in the tissues. The expression of CaMKIIalpha was significantly elevated in the depression tissues by 29%. The expression of CaMKIIbeta was significantly elevated in the schizophrenia tissues by 27%, and in the depression tissues by 36%. Because CaMKIIbeta influences the expression of many neuroreceptors and influences neural outgrowth and pruning, its altered expression in the cerebral cortex in schizophrenia or depression may contribute to these diseases.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

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 head0.028
GPT teacher head0.314
Teacher spread0.286 · 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

Citations79
Published2005
Admission routes1
Has abstractyes

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