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Neprilysin Gene Transfer Reduces Human Amyloid Pathology in Transgenic Mice

2003· article· en· W2155133303 on OpenAlexfundno aff
Robert A. Marr, Edward Rockenstein, Atish Mukherjee, Mark S. Kindy, Louis B. Hersh, Fred H. Gage, Inder M. Verma, Eliezer Masliah

Bibliographic record

VenueJournal of Neuroscience · 2003
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
FundersNational Institute on AgingParkinsonfondenAlzheimer's AssociationWayne and Gladys Valley FoundationCanadian Institutes of Health ResearchNational Parkinson FoundationNational Institutes of Health
KeywordsNeprilysinGenetically modified mouseTransgeneAmyloid (mycology)HippocampusAmyloidosisAmyloid precursor proteinAmyloid betaAlzheimer's diseaseNeurosciencePathologyBiologyMedicineDiseaseGeneBiochemistryEnzyme

Abstract

fetched live from OpenAlex

The degenerative process of Alzheimer's disease is linked to a shift in the balance between amyloid-beta (Abeta) production, clearance, and degradation. Neprilysin has recently been implicated as a major extracellular Abeta degrading enzyme in the brain. However, there has been no direct demonstration that neprilysin antagonizes the deposition of amyloid-beta in vivo. To address this issue, a lentiviral vector expressing human neprilysin (Lenti-Nep) was tested in transgenic mouse models of amyloidosis. We show that unilateral intracerebral injection of Lenti-Nep reduced amyloid-beta deposits by half relative to the untreated side. Furthermore, Lenti-Nep ameliorated neurodegenerative alterations in the frontal cortex and hippocampus of these transgenic mice. These data further support a role for neprilysin in regulating cerebral amyloid deposition and suggest that gene transfer approaches might have potential for the development of alternative therapies for Alzheimer's disease.

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 score0.002
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.058
GPT teacher head0.327
Teacher spread0.269 · 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

Citations387
Published2003
Admission routes1
Has abstractyes

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