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Record W2005368067 · doi:10.1002/jcb.22221

Runx transcription factors: Lineage‐specific regulators of neuronal precursor cell proliferation and post‐mitotic neuron subtype development

2009· review· en· W2005368067 on OpenAlexafffund
Chrissandra J. Zagami, Morena Zusso, Stefano Stifani

Bibliographic record

VenueJournal of Cellular Biochemistry · 2009
Typereview
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsTranscription factorCell biologyMitosisLineage (genetic)Cell growthNeuronCell lineageBiologyCellular differentiationGeneNeuroscienceGenetics

Abstract

fetched live from OpenAlex

Runt-related (RUNX) genes encode evolutionarily conserved transcription factors that play essential roles during development and adult tissue homeostasis. RUNX proteins regulate the transition from proliferation to differentiation in a variety of cell lineages. Moreover, they control the diversification of distinct cellular phenotypes in numerous tissues. Alterations of RUNX functions are associated with several cancers and other human pathologies, underscoring the vital roles of these transcription factors in adult organs. Insights into the functions and regulations of mammalian RUNX proteins have been provided mostly by studies of RUNX involvement in mechanisms of hematopoietic and skeletal development and disease. A growing number of recent investigations are revealing new functions for RUNX family members during the development of the mammalian nervous system. This review will discuss recent progress in the study of RUNX protein involvement in mammalian neural development, with emphasis on the differentiation of olfactory, sensory, and motor neuron lineages.

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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.004

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 head0.028
GPT teacher head0.279
Teacher spread0.251 · 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

Citations31
Published2009
Admission routes2
Has abstractyes

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