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Record W2184148524 · doi:10.3389/fncel.2015.00451

Advancements in the Underlying Pathogenesis of Schizophrenia: Implications of DNA Methylation in Glial Cells

2015· review· en· W2184148524 on OpenAlexaff
Xingshu Chen, Nanxin Huang, Michael Namaka, Lan Xiao

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsDNA methylationEpigeneticsPathogenesisMechanism (biology)Schizophrenia (object-oriented programming)MethylationNeuroscienceDysfunctional familyEpigenesisBiologyMedicineBioinformaticsDNAGeneticsGenePsychiatryImmunologyGene expression

Abstract

fetched live from OpenAlex

Schizophrenia (SZ) is a chronic and severe mental illness for which currently there is no cure. At present, the exact molecular mechanism involved in the underlying pathogenesis of SZ is unknown. The disease is thought to be caused by a combination of genetic, biological, psychological, and environmental factors. Recent studies have shown that epigenetic regulation is involved in SZ pathology. Specifically, DNA methylation, one of the earliest found epigenetic modifications, has been extensively linked to modulation of neuronal function, leading to psychiatric disorders such as SZ. However, increasing evidence indicates that glial cells, especially dysfunctional oligodendrocytes undergo DNA methylation changes that contribute to the pathogenesis of SZ. This review primarily focuses on DNA methylation involved in glial dysfunctions in SZ. Clarifying this mechanism may lead to the development of new therapeutic interventional strategies for the treatment of SZ and other illnesses by correcting abnormal methylation in glial cells.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.074
GPT teacher head0.343
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2015
Admission routes1
Has abstractyes

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