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Record W2113777921 · doi:10.1073/pnas.97.22.12306

Reduction of axonal caliber does not alleviate motor neuron disease caused by mutant superoxide dismutase 1

2000· article· en· W2113777921 on OpenAlexaff
Minh Dang Nguyen, Roxanne Larivière, Jean‐Pierre Julien

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2000
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsSOD1Amyotrophic lateral sclerosisAxonNeurofilamentMotor neuronSuperoxide dismutaseBiologyCell biologyContext (archaeology)MutantKnockout mouseNeurosciencePathologyGeneticsBiochemistryOxidative stressGeneMedicineImmunologyDiseaseImmunohistochemistrySpinal cord

Abstract

fetched live from OpenAlex

It is well established that motor neurons with large axon caliber are selectively affected in amyotrophic lateral sclerosis (ALS). To investigate whether high neurofilament (NF) content and large axonal caliber are factors that predispose motor neurons to selective degeneration in ALS, we generated mice expressing a mutant form of superoxide dismutase 1 (SOD1(G37R)) linked to familial ALS in a context of one allele for each NF gene being disrupted. A approximately 40% decrease of NF protein content detected in triple heterozygous knockout mice shifted the calibers of large axons in L5 ventral root from 5-9 microm to 1-5 microm, altering neither the normal subunit stoichiometry and morphological distribution of NFs nor levels of other cytoskeletal proteins. This considerable reduction in NF burden and caliber of axons did not extend the life span of SOD1(G37R) mice nor did it alleviate the loss of motor axons. Moreover, increasing the density of NFs in axons by overexpressing a NF-L transgene did not accelerate disease in SOD1(G37R) mice. These results do not support the current view that high NF content and large caliber of axons may account for the selective vulnerability of motor neurons in ALS caused by mutant SOD1.

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)0.000
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 score0.485
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.311
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

Citations69
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicAmyotrophic Lateral Sclerosis ResearchFrench-language works237,207