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Record W2404454656 · doi:10.1093/jnen/61.10.849

Declining Expression of Neprilysin in Alzheimer Disease Vasculature: Possible Involvement in Cerebral Amyloid Angiopathy

2002· article· en· W2404454656 on OpenAlexafffund
Mélanie Carpentier, Yves Robitaille, Luc DesGroseillers, Guy Boileau, Mieczysław Marcinkiewicz

Bibliographic record

VenueJournal of Neuropathology & Experimental Neurology · 2002
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalUniversité de Montréal
FundersUniversity of Ottawa
KeywordsNeprilysinCerebral amyloid angiopathySenile plaquesParenchymaAlzheimer's diseaseHippocampusAmyloid (mycology)AmyloidosisEndocrinologyInternal medicinePathologyMedicineBiologyDiseaseBiochemistryDementia

Abstract

fetched live from OpenAlex

Molecular, genetic, and pharmacological studies have shown that neprilysin (also called NEP) catabolizes amyloid beta peptides (A beta) in healthy conditions. However, in Alzheimer disease (AD), A beta accumulates forming senile plaques in brain parenchyma and amyloid deposition around blood vessels. In this study, we tested at cellular level the relationship between neprilysin and A beta in human healthy and AD brain. Our results provided evidence for declining levels of neprilysin in AD brains as compared to healthy controls in parallel with increasing deposition of A beta. In hippocampus of AD individuals we observed a significant down-regulation of neprilysin expression in pyramidal neurons, consistent with the possibility that neprilysin controls the level of A beta accumulation and plaque formation in this area. In the cortex and leptomeninges, neprilysin was expressed in the smooth muscle cells of blood vessels. In sections from AD patients we observed a clear inverse relationship between neprilysin and A beta peptide levels in the vasculature, implicating neprilysin in cerebral amyloid angiopathy.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.316
Teacher spread0.275 · 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 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

Citations114
Published2002
Admission routes2
Has abstractyes

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