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Record W2906031711 · doi:10.3389/fncel.2018.00499

New Therapeutic Avenues of mCSF for Brain Diseases and Injuries

2018· article· en· W2906031711 on OpenAlexafffund
Vincent Pons, Serge Rivest

Bibliographic record

VenueFrontiers in Cellular Neuroscience · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité Laval
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsMicrogliaChemokineInflammationCell biologyImmune systemProinflammatory cytokineMacrophage colony-stimulating factorCCL2MacrophageNeuroscienceMyelinImmunologyBiologyChemistryCentral nervous systemIn vitro

Abstract

fetched live from OpenAlex

Macrophage colony-stimulating factor (mCSF) is a cytokine known to promote the recruitment of macrophages inducing the release of CCL2, a chemokine mobilizing monocytes to sites of inflammation. Additionally, it induces microglia/macrophage proliferation and the polarization of these cells towards a M2-like phenotype, impairing their ability to release pro-inflammatory factors and toxic mediators, while favoring the release of mediators promoting tissue repair. Another important player is the mCSF receptor CSFR1, which is highly expressed in monocytes, macrophages and microglia. Here, we discuss the new interesting therapeutic avenue of the mCSF/CSFR1 axis on brain diseases. More specifically, mCSF cascade might stimulate the survival/proliferation of oligodendrocytes, enhance the immune response as well as modulate the release of growth factors and the phagocytic activity of immune cells to remove myelin debris and toxic proteins from the brain.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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 designTheoretical or conceptual
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

Citations35
Published2018
Admission routes2
Has abstractyes

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