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Niacin produces an inconsistent treatment response in the EAE model of multiple sclerosis

2024· article· en· W4401055393 on OpenAlexafffund
Emily Wuerch, Reza Mirzaei, V. Wee Yong

Bibliographic record

VenueJournal of Neuroimmunology · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchCumming School of Medicine, University of CalgarySchool of Medicine, Indiana University
KeywordsRemyelinationNiacinMultiple sclerosisExperimental autoimmune encephalomyelitisPhagocytosisImmunologyEncephalomyelitisGlatiramer acetateMicrogliaMedicineNeuropathologyAcquired immune systemImmune systemCentral nervous systemMyelinPathologyDiseaseInternal medicineInflammation

Abstract

fetched live from OpenAlex

Niacin was found in the lysolecithin model of multiple sclerosis (MS) to promote the phagocytic clearance of debris and enhance remyelination. Lysolecithin lesions have prominent microglia/macrophages but lack lymphocytes that populate plaques of MS or its experimental autoimmune encephalomyelitis (EAE) model. Thus, the current study assessed the efficacy of niacin in EAE. We found that niacin inconsistently affects EAE clinical score, and largely does not ameliorate neuropathology. In culture, niacin enhances phagocytosis by macrophages, but does not reduce T cell proliferation. We suggest that studies of niacin for potential remyelination in MS should include a therapeutic that targets adaptive immunity.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.204
GPT teacher head0.359
Teacher spread0.155 · 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

Citations1
Published2024
Admission routes2
Has abstractno

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