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The Retinoblastoma Protein Is Essential for Survival of Postmitotic Neurons

2012· article· en· W1976838074 on OpenAlexafffund
Matthew G. Andrusiak, Renaud Vandenbosch, David S. Park, Ruth S. Slack

Bibliographic record

VenueJournal of Neuroscience · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health ResearchDeutsches KrebsforschungszentrumUniversity of OttawaAlzheimer SocietyHeart and Stroke Foundation of Canada
KeywordsRetinoblastoma proteinE2FRetinoblastomaBiologyCell cyclePsychological repressionCell Cycle ProteinProgrammed cell deathEctopic expressionTranscription factorCell biologyPumaCell cycle checkpointCellular differentiationApoptosisCancer researchNeuroscienceGeneGeneticsGene expression

Abstract

fetched live from OpenAlex

The retinoblastoma protein (Rb) family members are essential regulators of cell cycle progression, principally through regulation of the E2f transcription factors. Growing evidence indicates that abnormal cell cycle signals can participate in neuronal death. In this regard, the role of Rb (p105) itself has been controversial. Germline Rb deletion leads to massive neuronal loss, but initial reports argue that death is non-cell autonomous. To more definitively resolve this question, we generated acute murine knock-out models of Rb in terminally differentiated neurons in vitro and in vivo. Surprisingly, we report that acute inactivation of Rb in postmitotic neurons results in ectopic cell cycle protein expression and neuronal loss without concurrent induction of classical E2f-mediated apoptotic genes, such as Apaf1 or Puma. These results suggest that terminally differentiated neurons require Rb for continuous cell cycle repression and survival.

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 score0.002

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)0.0010.001

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 head0.023
GPT teacher head0.290
Teacher spread0.267 · 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

Citations46
Published2012
Admission routes2
Has abstractyes

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