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Mitogen‐activated protein kinase activity may not be necessary for the neuropathology of Niemann–Pick type C mice

2008· article· en· W1980580505 on OpenAlexaff
Min Zhang, Janice L. Hallows, Xuezhen Wang, Bitao Bu, Wei Wang, Inez Vincent

Bibliographic record

VenueJournal of Neurochemistry · 2008
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Aging
KeywordsHyperphosphorylationNeuropathologyMAPK/ERK pathwayKinaseCytoskeletonMitogen-activated protein kinaseBiologyNeuroscienceProtein kinase AInternal medicineNeurofilamentCell biologyEndocrinologyMedicineCancer researchDiseaseBiochemistryCellImmunohistochemistry

Abstract

fetched live from OpenAlex

Hyperphosphorylation of neurofilament and tau, and formation of cytoskeletal lesions, are notable features of several human neurodegenerative diseases, including Niemann-Pick Disease Type C (NPC). Previous studies suggested that the MAPKs, extracellular signal regulated kinase 1 and 2 (ERK1/2) may play a significant role in this aspect of NPC. To test this idea, we treated npc mice with PD98059, a specific and potent inhibitor of MAPK activation. Although activity of ERK1/2 was inhibited by 40%, a 2-week intracerebroventricular infusion of PD98059 just prior to onset of cytoskeletal pathology and symptoms in npc mice did not delay or inhibit prominent hallmarks of NPC. Unexpectedly, ERK1/2 inhibition led to aggravation of tau hyperphosphorylation, particularly in oligodendroctyes, in a manner similar to that of certain human tauopathies. Our results suggest that ERK1/2 does not play a major role in NPC neuropathology, and therefore, that MAPK inhibition is unlikely to be a useful strategy for managing the 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 score0.008

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 studies0.0000.001
Scholarly communication0.0010.001
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 head0.060
GPT teacher head0.324
Teacher spread0.264 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

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