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Record W2007730758 · doi:10.1002/jnr.21730

Astrocyte‐only Npc1 reduces neuronal cholesterol and triples life span of <i>Npc1</i><sup>–/–</sup> mice

2008· article· en· W2007730758 on OpenAlexaff
Min Zhang, Diana Strnatka, Carolyn Donohue, Janice L. Hallows, Inez Vincent, Robert P. Erickson

Bibliographic record

VenueJournal of Neuroscience Research · 2008
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Biomedical Imaging and BioengineeringNational Cancer InstituteGladstone Institutes
KeywordsNPC1AstrocyteNeurodegenerationGlial fibrillary acidic proteinBiologyNeuroscienceMyelinCell biologyEndocrinologyInternal medicineImmunologyCentral nervous systemMedicineDiseaseEndosomeImmunohistochemistry

Abstract

fetched live from OpenAlex

Niemann-Pick type C (NPC) disease is an autosomal recessive, lethal neurodegenerative disorder. Although neurodegeneration of Purkinje cells in the mouse model (Npc1(-/-)) is thought to be autonomous, the basis of neuronal death in other regions of the brain remains elusive. We addressed this issue in vivo by using the glial fibrillary acidic protein (GFAP) promoter to direct astrocyte-specific, replacement expression of Npc1 in Npc1(-/-) mice. These mice showed enhanced survival, decreased neuronal storage of cholesterol associated with less accumulation of axonal spheroids, lower numbers of degenerated neurons and reactive astrocytes, and restoration of myelin tracts. Their death was not associated with the usual terminal decline in weight but instead with a loss of Purkinje cells and motor coordination. We conclude that neurodegeneration of Npc1(-/-) mice is greatly affected by the loss of fibrillary astrocyte function.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.122
GPT teacher head0.379
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

Citations87
Published2008
Admission routes1
Has abstractyes

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