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The Transcription Factor E2F1 Modulates Apoptosis of Neurons

2000· article· en· W2034748234 on OpenAlexaff
Sheng‐Tao Hou, Debbie Callaghan, Marie-Christine Fournier, Irene E. Hill, Liping Kang, B. Massie, Paul Morley, Christine L. Murray, Ingrid Rasquinha, Ruth S. Slack, John P. MacManus

Bibliographic record

VenueJournal of Neurochemistry · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsNational Research Council CanadaInstitute for Biological SciencesUniversity of Ottawa
Fundersnot available
KeywordsE2F1ApoptosisStaurosporineBiologyDNA fragmentationTranscription factorCell biologyProgrammed cell deathFragmentation (computing)Molecular biologyCell cycleSignal transductionGeneticsProtein kinase CGene

Abstract

fetched live from OpenAlex

The transcription factor E2F1 is known to mediate apoptosis in isolated quiescent and postmitotic cardiac myocytes, and its absence decreases the size of brain infarction following cerebral ischemia. To demonstrate directly that E2F1 modulates neuronal apoptosis, we used cultured cortical neurons to show a temporal association of the transcription and expression of E2F1 in neurons with increased neuronal apoptosis. Cortical neurons lacking E2F1 expression (derived from E2F1 -/- mice) were resistant to staurosporine-induced apoptosis as evidenced by the significantly lower caspase 3-like activity and a lesser number of cells with apoptotic morphology in comparison with cortical cultures derived from wild-type mice. Furthermore, overexpressing E2F1 alone using replication-deficient recombinant adenovirus was sufficient to cause neuronal cell death by apoptosis, as evidenced by the appearance of hallmarks of apoptosis, such as the threefold increase in caspase 3-like activity and increased laddered DNA fragmentation, in situ endlabeled DNA fragmentation, and numbers of neuronal cells with punctate nuclei. Taken together, we conclude that E2F1 plays a key role in modulating neuronal apoptosis.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.019
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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 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

Citations115
Published2000
Admission routes1
Has abstractyes

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