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Regulation of β‐site APP‐cleaving enzyme 1 gene expression and its role in Alzheimer’s Disease

2011· review· en· W1495483872 on OpenAlexafffund
Xiulian Sun, Kelley Bromley‐Brits, Weihong Song

Bibliographic record

VenueJournal of Neurochemistry · 2011
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsSenile plaquesBACE1-ASAmyloid precursor proteinPathogenesisAlzheimer's diseaseAmyloid precursor protein secretaseTranslation (biology)NeuropathologyBiologyGene expressionNeuroscienceAlpha secretaseRegulation of gene expressionDiseaseGeneMedicineMessenger RNAGeneticsPathologyImmunology

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) is the most common neurodegenerative disorder leading to dementia. Neuritic plaques are the hallmark neuropathology in AD brains. Proteolytic processing of amyloid-β precursor protein at the β site by beta-site amyloid-β precursor protein-cleaving enzyme 1 (BACE1) is essential to generate Aβ, a central component of the neuritic plaques. BACE1 is increased in some sporadic AD brains, and dysregulation of BACE1 gene expression plays an important role in AD pathogenesis. This review will focus on the regulation of BACE1 gene expression at the transcriptional, post-transcriptional, translation initiation, translational and post-translational levels, and its role in AD pathogenesis. Further studies on BACE1 gene expression regulation will greatly contribute to our understanding of AD pathogenesis and reveal potential novel approaches for AD prevention and drug development.

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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.061
GPT teacher head0.342
Teacher spread0.281 · 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

Citations90
Published2011
Admission routes2
Has abstractyes

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