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Cyclin-Dependent Kinases and P53 Pathways Are Activated Independently and Mediate Bax Activation in Neurons after DNA Damage

2001· article· en· W1908458283 on OpenAlexafffund
Erick J. Morris, Elizabeth Keramaris, Hardy J. Rideout, Ruth S. Slack, Nicholas J. Dyson, Leonidas Stefanis, David S. Park

Bibliographic record

VenueJournal of Neuroscience · 2001
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsUniversity of Ottawa
FundersMedical Research CouncilMedical Research Council CanadaWellcome TrustHeart and Stroke Foundation of Canada
KeywordsCyclin-dependent kinaseCell biologyDNA damageRetinoblastoma proteinBiologyKinaseApoptosisProgrammed cell deathCell cycleCancer researchDNABiochemistry

Abstract

fetched live from OpenAlex

DNA damage has been implicated as one important initiator of cell death in neuropathological conditions such as stroke. Accordingly, it is important to understand the signaling processes that control neuronal death induced by this stimulus. Previous evidence has shown that the death of embryonic cortical neurons treated with the DNA-damaging agent camptothecin is dependent on the tumor suppressor p53 and cyclin-dependent kinase (CDK) activity and that the inhibition of either pathway alone leads to enhanced and prolonged survival. We presently show that p53 and CDKs are activated independently on parallel pathways. An increase in p53 protein levels, nuclear localization, and DNA binding that result from DNA damage are not affected by the inhibition of CDK activity. Conversely, no decrease in retinoblastoma protein (pRb) phosphorylation was observed in p53-deficient neurons that were treated with camptothecin. However, either p53 deficiency or the inhibition of CDK activity alone inhibited Bax translocation, cytochrome c release, and caspase-3-like activation. Taken together, our results indicate that p53 and CDK are activated independently and then act in concert to control Bax-mediated 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.259
Teacher spread0.237 · 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

Citations100
Published2001
Admission routes2
Has abstractyes

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