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Record W2024225603 · doi:10.3390/ijms141122409

Ionizing Radiation-Induced Responses in Human Cells with Differing TP53 Status

2013· review· en· W2024225603 on OpenAlexaff
Razmik Mirzayans, Bonnie Andrais, April Scott, Ying Wang, David Murray

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2013
Typereview
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClonogenic assayBiologyDNA damageAutophagyCancer researchApoptosisCancer cellProgrammed cell deathSenescenceMitosisMitotic catastropheCell biologyCrosstalkCancerGeneticsDNA

Abstract

fetched live from OpenAlex

Ionizing radiation triggers diverse responses in human cells encompassing apoptosis, necrosis, stress-induced premature senescence (SIPS), autophagy, and endopolyploidy (e.g., multinucleation). Most of these responses result in loss of colony-forming ability in the clonogenic survival assay. However, not all modes of so-called clonogenic cell "death" are necessarily advantageous for therapeutic outcome in cancer radiotherapy. For example, the crosstalk between SIPS and autophagy is considered to influence the capacity of the tumor cells to maintain a prolonged state of growth inhibition that unfortunately can be succeeded by tumor regrowth and disease recurrence. Likewise, endopolyploid giant cells are able to segregate into near diploid descendants that continue mitotic activities. Herein we review the current knowledge on the roles that the p53 and p21(WAF1) tumor suppressors play in determining the fate of human fibroblasts (normal and Li-Fraumeni syndrome) and solid tumor-derived cells after exposure to ionizing radiation. In addition, we discuss the important role of WIP1, a p53-regulated oncogene, in the temporal regulation of the DNA damage response and its contribution to p53 dynamics post-irradiation. This article highlights the complexity of the DNA damage response and provides an impetus for rethinking the nature of cancer cell resistance to therapeutic agents.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.948
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.055
GPT teacher head0.365
Teacher spread0.310 · 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 designOther design
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

Citations122
Published2013
Admission routes1
Has abstractyes

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