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Record W3087744353 · doi:10.3390/ijms21186875

Role of Microglia in Modulating Adult Neurogenesis in Health and Neurodegeneration

2020· review· en· W3087744353 on OpenAlexaff
Mohammed Al‐Onaizi, Alaa Al-Khalifah, Dalal Qasem, Ayman ElAli

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2020
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMicrogliaNeurogenesisNeurodegenerationNeuroscienceBiologyImmune systemPhagocytosisInnate immune systemNeuroimmunologyNeuroinflammationPhenotypeInflammationCell biologyCentral nervous systemImmunologyMedicineDiseasePathology

Abstract

fetched live from OpenAlex

Microglia are the resident immune cells of the brain, constituting the powerhouse of brain innate immunity. They originate from hematopoietic precursors that infiltrate the developing brain during different stages of embryogenesis, acquiring a phenotype characterized by the presence of dense ramifications. Microglial cells play key roles in maintaining brain homeostasis and regulating brain immune responses. They continuously scan and sense the brain environment to detect any occurring changes. Upon detection of a signal related to physiological or pathological processes, the cells are activated and transform to an amoeboid-like phenotype, mounting adequate responses that range from phagocytosis to secretion of inflammatory and trophic factors. The overwhelming evidence suggests that microglia are crucially implicated in influencing neuronal proliferation and differentiation, as well as synaptic connections, and thereby cognitive and behavioral functions. Here, we review the role of microglia in adult neurogenesis under physiological conditions, and how this role is affected in 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.333
Teacher spread0.290 · 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 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

Citations48
Published2020
Admission routes1
Has abstractyes

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