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Record W2749992110 · doi:10.1038/s41598-017-09405-9

Nogo receptor 1 regulates Caspr distribution at axo-glial units in the central nervous system

2017· article· en· W2749992110 on OpenAlexaff
Jae Young Lee, Min Joung Kim, Lijun Li, Alexander A. Velumian, Pei M. Aui, Michael G. Fehlings, Steven Petratos

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsUniversity Health NetworkKrembil FoundationUniversity of Toronto
FundersTrish Multiple Sclerosis Research FoundationMultiple Sclerosis AustraliaInternational Progressive MS AllianceMultiple Sclerosis SocietyBethlehem Griffiths Research FoundationYale University
KeywordsCentral nervous systemExperimental autoimmune encephalomyelitisNeuroscienceMyelinBiologyMultiple sclerosisSpinal cordNervous systemSchwann cellEncephalomyelitisPathologyCell biologyImmunologyMedicine

Abstract

fetched live from OpenAlex

Abstract Axo-glial units are highly organised microstructures propagating saltatory conduction and are disrupted during multiple sclerosis (MS). Nogo receptor 1 (NgR1) has been suggested to govern axonal damage during the progression of disease in the MS-like mouse model, experimental autoimmune encephalomyelitis (EAE). Here we have identified that adult ngr1−/− mice, previously used in EAE and spinal cord injury experiments, display elongated paranodes, and nodes of Ranvier. Unstructured paranodal regions in ngr1−/− mice are matched with more distributed expression pattern of Caspr. Compound action potentials of optic nerves and spinal cords from naïve ngr1−/− mice are delayed and reduced. Molecular interaction studies revealed enhanced Caspr cleavage. Our data suggest that NgR1 may regulate axo-myelin ultrastructure through Caspr-mediated adhesion, regulating the electrophysiological signature of myelinated axons of central nervous system (CNS).

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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.260
Teacher spread0.223 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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