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Record W1975772742 · doi:10.1002/jnr.1100

Neurofilament cytoskeleton disruption does not modify accumulation of trophic factor mRNA

2001· article· en· W1975772742 on OpenAlexaff
Patrick Robert, Alan C. Peterson, Joël Eyer

Bibliographic record

VenueJournal of Neuroscience Research · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsMcGill UniversityRoyal Victoria Hospital
FundersFondation pour l'Aide à la Recherche sur la Sclérose en Plaques
KeywordsNeurofilamentCiliary neurotrophic factorNeurotrophinBiologyCytoskeletonCell biologyNerve growth factorNeuroscienceNeurotrophic factorsNeurotrophin-3Genetically modified mouseReceptorTransgeneBrain-derived neurotrophic factorCellImmunologyGeneticsImmunohistochemistry

Abstract

fetched live from OpenAlex

Previously we described a transgenic mouse model in which neurofilaments are sequestered in neuronal cell bodies and withheld from the axonal compartment. This model and other transgenic models with disrupted neurofilaments are used widely to investigate the role of the neurofilament cytoskeleton in normal neurons and in inherited or acquired diseases. To interpret such studies, it is important to establish whether the maldistribution of neurofilaments has major secondary consequences on the cell biology of the affected neurons. Notably, multiple perturbations of the nervous system simultaneously affect both the neuronal cytoskeleton and neurotrophin expression. To determine whether the expression of neurotrophic factors or their receptors is perturbed by a primary disruption in neurofilaments, we compared the accumulated mRNA levels for ciliary neuroptrophic factor (CNTF), nerve growth factor, neurotrophin 3, and the alpha CNTF receptor in mature transgenic mice and their littermate controls. Consistently with the prolonged survival of neurons expressing atypical or abnormally distributed neurofilaments, no obvious changes were observed for any of the mRNA species examined.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.110
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.170
GPT teacher head0.441
Teacher spread0.271 · 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 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

Citations3
Published2001
Admission routes1
Has abstractyes

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