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Neuropathy in a mouse model of CD8+ T cell-mediated CNS demyelination (P4176)

2013· article· en· W4313353967 on OpenAlexaff
Anthony Rainone, Sylvie Fournier

Bibliographic record

VenueThe Journal of Immunology · 2013
Typearticle
Languageen
FieldMedicine
TopicPeripheral Neuropathies and Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsCD8Flow cytometryDemyelinating diseaseT cellImmunologyPathogenesisCentral nervous systemEffectorBiologyCytotoxic T cellNeuroscienceAntigenImmune systemGeneticsIn vitro

Abstract

fetched live from OpenAlex

Abstract Several lines of evidence suggest that CD8+ T cells could contribute to the pathogenesis of many autoimmune diseases of the nervous system. Our group studies a mouse model that spontaneously develops neurological symptoms associated with demyelinated lesions in the CNS (herein referred to as L31 mice). We have shown that the demyelinating disease in L31 mice is T cell-dependent with CD8+ T cells acting as effector T cells in the pathological process while CD4+ T cells play a regulatory role. Because L31 mice exhibit motor dysfunction indicated by clasping of the limbs and difficulty walking, we wanted to determine whether the PNS was also affected. A flow cytometry approach was used to determine T cell kinetic accumulation in the CNS and PNS. Data demonstrate that CD8+ T cells accumulate in the CNS of L31 mice from early on in life while they only appear in the PNS once mice display neurological symptoms. Furthermore, immunofluoresecent studies of the CNS and PNS of these mice recapitulate the flow cytometry data and demonstrate the large extent of demyelination in the PNS of symptomatic L31 mice, which is associated with CD8+ T cell accumulation. We hypothesize that epitope spreading during the course of this CD8+ mediated demyelinating disease is the cause of PNS pathology. It will be of further interest to determine the T cell receptor specificity of the CNS and PNS infiltrating lymphocytes.

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)0.000
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 score0.599
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.228
Teacher spread0.215 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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