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Record W2792304822 · doi:10.3389/fnmol.2018.00060

Into the Fourth Dimension: Dysregulation of Genome Architecture in Aging and Alzheimer’s Disease

2018· review· en· W2792304822 on OpenAlexafffund
Warren Winick‐Ng, R. Jane Rylett

Bibliographic record

VenueFrontiers in Molecular Neuroscience · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsEpigeneticsNeuroscienceBiologyChromatinDiseaseContext (archaeology)Alzheimer's diseaseGeneticsGeneMedicinePathology

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by synapse dysfunction and cognitive impairment. Understanding the development and progression of AD is challenging, as the disease is highly complex and multifactorial. Both environmental and genetic factors play a role in AD pathogenesis, highlighted by observations of complex DNA modifications at the single gene level, and by new evidence that also implicates changes in genome architecture in AD patients. The four-dimensional structure of chromatin in space and time is essential for context-dependent regulation of gene expression in post-mitotic neurons. Dysregulation of epigenetic processes have been observed in the aging brain and in patients with AD, though there is not yet agreement on the impact of these changes on transcription. New evidence shows that proteins involved in genome organization have altered expression and localization in the AD brain, suggesting that the genomic landscape may play a critical role in the development of AD. This review discusses the role of the chromatin organizers and epigenetic modifiers in post-mitotic cells, the aging brain, and in the development and progression of AD. How these new insights can be used to help determine disease risk and inform treatment strategies will also be discussed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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 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

Citations28
Published2018
Admission routes2
Has abstractyes

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