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Record W2990053667 · doi:10.3389/fendo.2019.00773

Metabolic Regulation of Epithelial to Mesenchymal Transition: Implications for Endocrine Cancer

2019· review· en· W2990053667 on OpenAlexafffund
Debasmita Bhattacharya, Anthony Scimè

Bibliographic record

VenueFrontiers in Endocrinology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEpithelial–mesenchymal transitionMetastasisBiologyCancerCancer cellEndocrine systemReprogrammingMesenchymal stem cellStem cellCancer researchCancer stem cellBioinformaticsCell biologyCellEndocrinologyHormoneGenetics

Abstract

fetched live from OpenAlex

The last few decades have witnessed an outstanding advancement in our understanding of the hallmarks of endocrine cancers. This includes the epithelial to mesenchymal transition (EMT), a process that alters the morphology and functional characteristics of carcinoma cells. The mesenchymal stem cell like phenotype produced by EMT allows the dislocation of cancer cells from the primary tumor site with inheritance of motility, metastatic and invasive properties. A fundamental driver thought to initiate and propagate EMT is metabolic reprogramming that occur during these transitions. Though there remains a paucity of data regarding the alterations that occur during EMT in endocrine cancers, the contribution of deregulated metabolism is a prominent feature. This mini review focuses on metabolic reprogramming events that occur in cancer cells and in particular those of endocrine origin. It highlights the main metabolic reprogramming outcomes of EMT, encompassing glycolysis, mitochondria oxidative phosphorylation and function, glutamine and lipid metabolism. Comprehending the metabolic changes that occur during EMT will help formulate potential bioenergetic targets as therapies for endocrine cancer metastasis.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.031
GPT teacher head0.332
Teacher spread0.301 · 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.

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

Citations38
Published2019
Admission routes2
Has abstractyes

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