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Record W4405108417 · doi:10.3390/cells13232022

Cancer Cells in Sleep Mode: Wake Them to Eliminate or Keep Them Asleep Forever?

2024· review· en· W4405108417 on OpenAlexafffund
Wenjie Liu, Antal H. Kovacs, Jinqiang Hou

Bibliographic record

VenueCells · 2024
Typereview
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsLakehead UniversityThunder Bay Regional Research Institute
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsDormancyCancer cellCancerMetastasisCancer stem cellBiologyPopulationCancer researchMedicineBotanyGenetics

Abstract

fetched live from OpenAlex

Cancer cell dormancy is a critical phase in cancer development, wherein cancer cells exist in a latent state marked by temporary but reversible growth arrest. This dormancy phase contributes to anticancer drug resistance, cancer recurrence, and metastasis. Treatment strategies aimed at cancer dormancy can be categorized into two contradictory approaches: inducing cancer cells into a dormant state or eliminating dormant cells. While the former seeks to establish permanent dormancy, the latter aims at eradicating this small population of dormant cells. In this review, we explore the current advancements in therapeutic methods targeting cancer cell dormancy and discuss future strategies. The concept of cancer cell dormancy has emerged as a promising avenue for novel cancer treatments, holding the potential for breakthroughs in the future.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.005

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 head0.102
GPT teacher head0.380
Teacher spread0.278 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2024
Admission routes2
Has abstractyes

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