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Record W2277434785 · doi:10.1126/science.aab3103

Neurons diversify astrocytes in the adult brain through sonic hedgehog signaling

2016· article· en· W2277434785 on OpenAlexafffund
W. Todd Farmer, Therése Abrahamsson, Sabrina Chierzi, Christopher Lui, Cristián Zaelzer, Emma V. Jones, Blandine Ponroy Bally, Gary G. Chen, Jean‐François Théroux, Jimmy Peng, Charles W. Bourque, Frédéric Charron, Carl Ernst, P. Jesper Sjöström, Keith K. Murai

Bibliographic record

VenueScience · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsDouglas CollegeMcGill UniversityDouglas Mental Health University InstituteMontreal Clinical Research InstituteMcGill University Health Centre
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsBiotechnology and Biological Sciences Research CouncilCanada Foundation for Innovation
KeywordsSonic hedgehogNeuroscienceAstrocyteBiologyMorphogenCell typeHedgehogHedgehog signaling pathwaySignallingCellCell biologySignal transductionCentral nervous systemGeneGenetics

Abstract

fetched live from OpenAlex

Astrocytes are specialized and heterogeneous cells that contribute to central nervous system function and homeostasis. However, the mechanisms that create and maintain differences among astrocytes and allow them to fulfill particular physiological roles remain poorly defined. We reveal that neurons actively determine the features of astrocytes in the healthy adult brain and define a role for neuron-derived sonic hedgehog (Shh) in regulating the molecular and functional profile of astrocytes. Thus, the molecular and physiological program of astrocytes is not hardwired during development but, rather, depends on cues from neurons that drive and sustain their specialized properties.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.045
GPT teacher head0.275
Teacher spread0.231 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations277
Published2016
Admission routes2
Has abstractyes

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