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Record W2800258289 · doi:10.7555/jbr.31.20160124

Re-evaluating the role of epithelial-mesenchymal-transition incancer progression

2017· article· en· W2800258289 on OpenAlexaff
Andrew Sulaiman, Lisheng Wang

Bibliographic record

VenueJournal of Biomedical Research · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersChina Scholarship Council
KeywordsEpithelial–mesenchymal transitionMesenchymal stem cellMotilityMetastasisCancer researchTumor progressionPopulationBiologyCancerMedicineCell biologyGenetics

Abstract

fetched live from OpenAlex

Epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) are essential for embryonic development and also important in cancer progression. In a conventional model, epithelial-like cancer cells transit to mesenchymal-like tumor cells with great motility via EMT transcription factors; these mesenchymal-like cells migrate through the circulation system, relocate to a suitable site and then convert back to an epithelial-like phenotype to regenerate the tumor. However, recent findings challenge this conventional model and support the existence of a stable hybrid epithelial/mesenchymal (E/M) tumor population. Hybrid E/M tumor cells exhibit both epithelial and mesenchymal properties, possess great metastatic and tumorigenic capacity and are associated with poorer patient prognosis. The hybrid E/M model and associated regulatory networks represent a conceptual change regarding tumor metastasis and organ colonization. It may lead to the development of novel treatment strategies to ultimately stop cancer progression and improve disease-free survival.

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)0.008
metaresearch head score (Gemma)0.003
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 score0.685
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.137
GPT teacher head0.518
Teacher spread0.381 · 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

Citations38
Published2017
Admission routes1
Has abstractyes

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