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Record W3089027348 · doi:10.3389/fncel.2020.00284

Beneficial Roles of Microglia and Growth Factors in MS, a Brief Review

2020· review· en· W3089027348 on OpenAlexafffund
Vincent Pons, Serge Rivest

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2020
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversité Laval
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsMicrogliaRemyelinationMultiple sclerosisNeuroscienceMyelinBiologyImmune systemMedicineCentral nervous systemImmunologyInflammation

Abstract

fetched live from OpenAlex

Microglia are the brain resident immune cells; they can produce a large variety of growth factors (GFs) to prevent neuronal damages and promote recovery. In neurodegenerative diseases, microglia can play both benefic and deleterious roles, depending on different factors and disease context. In multiple sclerosis, microglia are involved in both demyelination (DM) and remyelination (RM) processes. Recent studies suggest a beneficial role of microglia in regenerative processes. These include the regenerative development of myelin after DM. This review gives an overlook of how microglia and GFs can influence the RM properties.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.275
Teacher spread0.231 · 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.

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

Citations27
Published2020
Admission routes2
Has abstractyes

Explore more

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