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Record W3148311172

Molecular mimicry and multiple sclerosis

2011· article· en· W3148311172 on OpenAlexaff
Michael Namaka, Sabina Kapoor, Leann Simms, Christine Leong, Amy Grossberndt, Michael Prouta, Emma E. Frost, Farid Esfahani, Andrew Gomori, Michael R. Mulvey

Bibliographic record

VenueNeural Regeneration Research · 2011
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMolecular mimicryMultiple sclerosisMimicryMyelinAntigenNeuroscienceBiologyMechanism (biology)DiseaseImmunologyMedicineCentral nervous systemPathologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system. Although the exact underlying mechanism leading to myelin destruction is unknown, the molecular mimicry theory is the most commonly acknowledged elucidation of MS pathology. Although various antigens have been associated with MS induction, this review presents studies focused on key bacterial and viral antigens that lead to the development of MS. The research specific to a molecular mimicry theory of MS via each implicated agent is weak; however, collectively the reports provide credible support for this theory. Given that homologous sequences are not required to lead to antigenic cross-reactivity, it is reasonable to conclude that certain viral and bacterial antigens with 5-10 similar amino acids in sequence can lead to self destruction of similar myelin sequences. Thus, this literature review has provided insight to further the understanding of the etiology of multiple sclerosis.

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 score0.169
Threshold uncertainty score0.452

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 science0.0000.000
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 head0.367
GPT teacher head0.388
Teacher spread0.021 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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