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Record W2909833689 · doi:10.1016/j.neuron.2018.12.020

Oligodendrocyte Progenitor Cells Become Regionally Diverse and Heterogeneous with Age

2019· article· en· W2909833689 on OpenAlexfundno aff
Sonia Spitzer, Sergey Sitnikov, Yasmine Kamen, Kimberley Anne Evans, Deborah Kronenberg‐Versteeg, Sabine Dietmann, Omar de Faria, Sylvia Agathou, Ragnhildur Thóra Káradóttir

Bibliographic record

VenueNeuron · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsnot available
FundersWellcome - MRC Cambridge Stem Cell Institute, University of CambridgeBiotechnology and Biological Sciences Research CouncilEuropean Research CouncilFonds de Recherche du Québec - SantéGates Cambridge TrustMedical Research CouncilDirectorate for Biological SciencesWellcome TrustCambridge TrustEuropean CommissionLister Institute of Preventive MedicineMultiple Sclerosis SocietyPaul G. Allen Frontiers GroupPaul G. Allen Family Foundation
KeywordsNeuroscienceHomogeneousProgenitor cellBiologyElectrophysiologyOligodendrocytePopulationProgenitorStem cellMyelinCentral nervous systemCell biologyMedicine

Abstract

fetched live from OpenAlex

Oligodendrocyte progenitor cells (OPCs), which differentiate into myelinating oligodendrocytes during CNS development, are the main proliferative cells in the adult brain. OPCs are conventionally considered a homogeneous population, particularly with respect to their electrophysiological properties, but this has been debated. We show, by using single-cell electrophysiological recordings, that OPCs start out as a homogeneous population but become functionally heterogeneous, varying both within and between brain regions and with age. These electrophysiological changes in OPCs correlate with the differentiation potential of OPCs; thus, they may underlie the differentiational differences in OPCs between regions and, likewise, differentiation failure with age.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.217
Teacher spread0.196 · 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

Citations375
Published2019
Admission routes1
Has abstractyes

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