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Record W2096423240 · doi:10.1084/jem.20141656

Inefficient clearance of myelin debris by microglia impairs remyelinating processes

2015· article· en· W2096423240 on OpenAlexafffund
Antoine Lampron, Antoine Larochelle, Nathalie Laflamme, Paul Préfontaine, Marie-Michèle Plante, Maria Gabriela Sánchez, V. Wee Yong, Peter K. Stys, Marie‐Ève Tremblay, Serge Rivest

Bibliographic record

VenueThe Journal of Experimental Medicine · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversity of CalgaryUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsRemyelinationMyelinMicrogliaMultiple sclerosisCX3CR1AxonDemyelinating DisorderDemyelinating diseaseImmunologyNeuroscienceBiologyMedicineCentral nervous systemInflammationChemokine

Abstract

fetched live from OpenAlex

An imbalance between remyelinating and demyelinating rates underlies degenerative processes in demyelinating diseases such as multiple sclerosis. An optimal therapeutic strategy would be to stimulate remyelination while limiting demyelination. Although accumulation of myelin debris impairs remyelination, the mechanisms regulating the clearance of such debris by mononuclear phagocytic cells are poorly understood. We demonstrate that after cuprizone intoxication, CCR2-dependent infiltration of mouse bone marrow-derived cells is abundant in demyelinating areas, but that these cells do not impact demyelination. However, in CX3CR1-deficient mice, the clearance of myelin debris by microglia was blocked greatly, affecting the integrity of the axon and myelin sheaths and thus preventing proper remyelination. These results highlight the crucial role played by CX3CR1 in myelin removal and show that there can be no efficient remyelination after a primary demyelinating insult if myelin clearance by microglia is impaired.

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)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.313
Teacher spread0.261 · 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

Citations604
Published2015
Admission routes2
Has abstractyes

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Same venueThe Journal of Experimental MedicineSame topicNeuroinflammation and Neurodegeneration MechanismsFrench-language works237,207