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Record W2905045032 · doi:10.1002/glia.23576

miR‐223 promotes regenerative myeloid cell phenotype and function in the demyelinated central nervous system

2018· article· en· W2905045032 on OpenAlexafffund
Dylan A. Galloway, Stephanie N. Blandford, T. H. Berry, John B. Williams, Mark Stefanelli, Michelle Ploughman, Craig S. Moore

Bibliographic record

VenueGlia · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMultiple Sclerosis Society of Canada
KeywordsRemyelinationNeuroinflammationMicrogliaBiologyMultiple sclerosisNeuroscienceCentral nervous systemMyelinPhenotypeImmunologyMyeloidCell biologyDownregulation and upregulationInflammation

Abstract

fetched live from OpenAlex

In the injured central nervous system, myeloid cells, including macrophages and microglia, are key contributors to both myelin injury and repair. This immense plasticity emphasizes the need to further understand the precise molecular mechanisms that contribute to the dynamic regulation of myeloid cell polarization and function. Herein, we demonstrate that miR-223 is upregulated in multiple sclerosis (MS) patient monocytes and the alternatively-activated and tissue-regenerating M2-polarized human macrophages and microglia. Using miR-223 knock-out mice, we observed that miR-223 is dispensable for maximal pro-inflammatory responses, but is required for efficient M2-associated phenotype and function, including phagocytosis. Using the lysolecithin animal model, we further demonstrate that miR-223 is required to efficiently clear myelin debris and promote remyelination. These results suggest miR-223 constrains neuroinflammation while also promoting repair, a finding of important pathophysiological relevance to MS as well as other neurodegenerative diseases.

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 score0.019
Threshold uncertainty score0.432

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 head0.023
GPT teacher head0.229
Teacher spread0.206 · 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

Citations64
Published2018
Admission routes2
Has abstractyes

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