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Record W1976608900 · doi:10.4161/cc.27396

Rapamycin induces pluripotent genes associated with avoidance of replicative senescence

2013· article· en· W1976608900 on OpenAlexfundno aff
Tatyana V. Pospelova, Tatiana V. Bykova, С. Г. Зубова, Nataliia V. Katolikova, Natalia M Yartzeva, Valery A. Pospelov

Bibliographic record

VenueCell Cycle · 2013
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsnot available
FundersRussian Academy of SciencesSaint Petersburg State UniversityUniversity of Toronto
KeywordsBiologyTelomereCell biologyTelomeraseSenescenceEmbryonic stem cellInduced pluripotent stem cellCell cyclemTORC1Homeobox protein NANOGCell cycle checkpointGeneMolecular biologyGeneticsPI3K/AKT/mTOR pathwaySignal transduction

Abstract

fetched live from OpenAlex

Primary rodent cells undergo replicative senescence, independent from telomere shortening. We have recently shown that treatment with rapamycin during passages 3-7 suppressed replicative senescence in rat embryonic fibroblasts (REFs), which otherwise occurred by 10-14 passages. Here, we further investigated rapamycin-primed cells for an extended number of passages. Rapamycin-primed cells continued to proliferate without accumulation of senescent markers. Importantly, these cells retained the ability to undergo serum starvation- and etoposide-induced cell cycle arrest. The p53/p21 pathway was functional. This indicates that rapamycin did not cause either transformation or loss of cell cycle checkpoints. We found that rapamycin activated transcription of pluripotent genes, oct-4, sox-2, nanog, as well as further upregulated telomerase (tert) gene. The rapamycin-derived cells have mostly non-rearranged, near-normal karyotype. Still, when cultivated for a higher number of passages, these cells acquired a chromosomal marker within the chromosome 3. We conclude that suppression mTORC1 activity may prevent replicative senescence without transformation of rodent cells.

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 score0.254
Threshold uncertainty score0.623

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 head0.011
GPT teacher head0.217
Teacher spread0.207 · 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

Citations25
Published2013
Admission routes1
Has abstractyes

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