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Record W2969469001 · doi:10.1002/glia.23711

Inhibitory milieu at the multiple sclerosis lesion site and the challenges for remyelination

2019· review· en· W2969469001 on OpenAlexafffund
Dylan A. Galloway, Elizabeth Gowing, Solmaz Setayeshgar, Rashmi Kothary

Bibliographic record

VenueGlia · 2019
Typereview
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsUniversity of OttawaUniversité de MontréalOttawa HospitalUniversity of British ColumbiaMemorial University of Newfoundland
FundersMultiple Sclerosis Society of CanadaUniversity of Pittsburgh
KeywordsRemyelinationNeuroscienceMultiple sclerosisBiologyContext (archaeology)OligodendrocyteLesionMyelinCentral nervous systemMedicineImmunologyPathology

Abstract

fetched live from OpenAlex

Regeneration of myelin, following injury, can occur within the central nervous system to reinstate proper axonal conductance and provide trophic support. Failure to do so renders the axons vulnerable, leading to eventual degeneration, and neuronal loss. Thus, it is essential to understand the mechanisms by which remyelination or failure to remyelinate occur, particularly in the context of demyelinating and neurodegenerative disorders. In multiple sclerosis, oligodendrocyte progenitor cells (OPCs) migrate to lesion sites to repair myelin. However, during disease progression, the ability of OPCs to participate in remyelination diminishes coincident with worsening of the symptoms. Remyelination is affected by a broad range of cues from intrinsic programming of OPCs and extrinsic local factors to the immune system and other systemic elements including diet and exercise. Here we review the literature on these diverse inhibitory factors and the challenges they pose to remyelination. Results spanning several disciplines from fundamental preclinical studies to knowledge gained in the clinic will be discussed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.274
GPT teacher head0.318
Teacher spread0.045 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations21
Published2019
Admission routes2
Has abstractyes

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