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Record W4378980382 · doi:10.18632/oncoscience.577

Cancer cell repopulation after therapy: which is the mechanism?

2023· article· en· W4378980382 on OpenAlexafffund
Rewati Prakash, Carlos M. Telleria

Bibliographic record

VenueOncoscience · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMcGill University Health CentreMcGill University
FundersOvarian Cancer Canada
KeywordsRepopulationCancerCancer researchCellCancer cellCancer stem cellBiologyCell therapyDiseaseMechanism (biology)MedicineStem cellImmunologyOncologyInternal medicineGeneticsHaematopoiesis

Abstract

fetched live from OpenAlex

Cancer cell repopulation after therapy is a phenomenon that leads to therapeutic failure with the consequent relapse of the disease. The process is understudied and mechanisms need to be uncovered. Here we discuss the issue of cancer cell repopulation after chemo- and radio-therapies. We compile evidence alleging that the repopulation of cancer cells can be originated from either cancer stem cells resistant to therapy, cancer cells that in response to therapy become polyploid and thereafter germinate into near-diploid rapid proliferating cells, and/or cells that respond to treatment undergoing senescence as a transient mechanism to survive, followed by the reinitiation of the cell cycle. Approaches targeted to prevent this post-therapy cancer cell repopulation should be uncovered to prevent tumor relapse and thus increase overall survival from this devastating disease.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.898

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.326
Teacher spread0.287 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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