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Record W3035852971 · doi:10.1002/glia.23877

Regionally encoded functional heterogeneity of astrocytes in health and disease: A perspective

2020· review· en· W3035852971 on OpenAlexfundno aff
Benjamin Clarke, Doaa M. Taha, Giulia E. Tyzack, Rickie Patani

Bibliographic record

VenueGlia · 2020
Typereview
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsnot available
FundersMedical Research CouncilMotor Neurone Disease AssociationWellcome TrustFrancis Crick InstituteMedical Research Council CanadaCancer Research UK
KeywordsNeuroscienceBiologyAstrocyteNeuroprotectionDiseasePerspective (graphical)PhenotypeFunctional diversityCentral nervous systemGeneticsMedicineGenePathologyEcologyComputer science

Abstract

fetched live from OpenAlex

Increasing evidence has suggested that astrocytes demonstrate striking regionally allocated functional heterogeneity. Here, we discuss how this spatiotemporally encoded diversity determines the astrocytic phenotype along a finely grained spectrum from neuroprotective to deleterious states. With increasing recognition of their diverse and evolving roles in the central neuraxis, astrocytes now represent a tractable cellular target for therapies aiming to restore neural circuit integrity in a broad range of neurodegenerative disorders. Understanding the determinants of astrocyte physiology along with the true extent of heterogeneity in their regional and subregional functions will ultimately inform therapeutic strategy 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 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)0.001
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 score0.985
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.109
GPT teacher head0.342
Teacher spread0.233 · 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.

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

Citations78
Published2020
Admission routes1
Has abstractyes

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