MétaCan
Menu
Back to cohort
Record W2074985708 · doi:10.2174/156720511795256035

The Role of Microglial Cell Subsets in Alzheimers Disease

2011· review· en· W2074985708 on OpenAlexafffund
Gaëlle Naert, Serge Rivest

Bibliographic record

VenueCurrent Alzheimer Research · 2011
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversité Laval
FundersCanadian Institutes of Health ResearchCanada Research ChairsAlzheimer Society
KeywordsMicrogliaCX3CR1Alzheimer's diseaseNeuroscienceSenile plaquesNeuroinflammationInflammationCognitive declineBiologyImmunologyChemokineNeurodegenerationPhenotypeCCR2DiseaseMedicineDementiaPathologyChemokine receptorGene

Abstract

fetched live from OpenAlex

Alzheimer disease (AD) is characterized by a progressive cognitive decline and accumulation of β-amyloid (Aβ forming senile plaques that are associated with inflammatory molecules and cells. Resident microglia and newly differentiated cells that are derived from the bone marrow are found in the vicinity of Aβ plaques. Although these two types of microglia are not distinguishable by specific markers in the brain, they seem to possess different phenotype and functions. In mouse models of AD, bone marrow-derived microglia (BMDM) have been shown to delay or stop the progression of AD and preventing their recruitment exacerbates the pathology. Transplantation of competent hematopoietic stem cells or their genetic modifications ameliorate cognitive functions, reduce Aβ accumulation and prevent synaptic dysfunctions. Improving the recruitment of genetically-modified BMDM may be considered as a powerful new therapeutic strategy to counteract AD. Here we review the role of microglia subsets in AD and how these cells have a great potential to fight against Aβ accumulation and cognitive impairment.

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)0.001
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.250
GPT teacher head0.472
Teacher spread0.222 · 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

Citations49
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueCurrent Alzheimer ResearchSame topicAlzheimer's disease research and treatmentsFrench-language works237,207