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Record W2467789036 · doi:10.3389/fcell.2016.00072

Microglia Ontology and Signaling

2016· review· en· W2467789036 on OpenAlex
Ayman ElAli, Serge Rivest

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2016
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health ResearchMultiple Sclerosis Scientific Research Foundation
KeywordsMicrogliaPhenotypeNeuroscienceBiologyInnate immune systemImmune systemEmbryonic stem cellNeuroinflammationPathogenesisSignal transductionInflammationImmunologyCell biologyGeneGenetics

Abstract

fetched live from OpenAlex

Microglia constitute the powerhouse of the innate immune system in the brain. It is now widely accepted that they are monocytic-derived cells that infiltrate the developing brain at the early embryonic stages, and acquire a resting phenotype characterized by the presence of dense branching processes, called ramifications. Microglia use these dynamic ramifications as sentinels to sense and detect any occurring alteration in brain homeostasis. Once a danger signal is detected, such as molecular factors associated to brain damage or infection, they get activated by acquiring a less ramified phenotype, and mount adequate responses that range from phagocyting cell debris to secreting inflammatory and trophic factors. Here, we review the origin of microglia and we summarize the main molecular signals involved in controlling their function under physiological conditions. In addition, their implication in the pathogenesis of multiple sclerosis and stress is 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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.277
Teacher spread0.238 · 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