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Record W2009594179 · doi:10.1096/fj.05-5628com

Increased BACE1 maturation contributes to the pathogenesis of Alzheimer's disease in Down syndrome

2006· article· en· W2009594179 on OpenAlexafffund
Xiulian Sun, Yigang Tong, Hong Qing, Chia-Hsiung Chen, Weihong Song

Bibliographic record

VenueThe FASEB Journal · 2006
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsNeuropathologySenile plaquesPathogenesisAmyloid precursor protein secretaseGolgi apparatusBACE1-ASAlzheimer's diseaseAmyloid precursor proteinDiseaseDown syndromeCell biologyNeuroscienceChemistryBiologyMedicinePathologyGenetics

Abstract

fetched live from OpenAlex

Almost all Down syndrome (DS) patients develop characteristic Alzheimer's disease (AD) neuropathology, including neuritic plaques and neurofibrillary tangles, after middle age. The mechanism underlying AD neuropathology in DS has been unknown. Abeta is the central component of neuritic plaques and is generated from APP by cleavage by the beta- and gamma-secretases. Here we show that beta-secretase activity is markedly elevated in DS. The ratio of mature to immature forms of BACE1 is altered in DS. DS has significantly higher levels of mature BACE1 proteins in Golgi than normal controls. Time-lapse live image analysis showed that BACE1 proteins were predominantly immobile in Golgi in DS cells, while they underwent normal trafficking in controls. Thus, overproduction of Abeta in DS is caused by abnormal BACE1 protein trafficking and maturation. Our results provide a novel molecular mechanism by which AD develops in DS and support the therapeutic potential of inhibiting BACE1 in AD and DS.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.016
GPT teacher head0.273
Teacher spread0.257 · 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 designBench or experimental
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

Citations62
Published2006
Admission routes2
Has abstractyes

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