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Record W2084955511 · doi:10.1097/ppo.0000000000000106

Drivers of the Warburg Phenotype

2015· review· en· W2084955511 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Cancer Journal · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsWarburg effectPhenotypeOxidative phosphorylationCitric acid cycleGlycolysisCell biologyMitochondrionBiologyTumor microenvironmentMetabolic pathwayMetabolismChemistryBiochemistryCancer researchTumor cellsGene

Abstract

fetched live from OpenAlex

The Warburg effect was first described by Otto Warburg in the 1920s and describes the preferential conversion of glucose to lactate as opposed to its metabolism through the citric acid cycle to fuel oxidative phosphorylation in the mitochondria, even in the presence of oxygen. This phenotype is a common feature of malignant cells and is also observed in some highly proliferative normal tissues. The selective advantage provided by this phenotype is not entirely clear. Adopting this metabolic state may allow tumor cells to balance their need for ATP, biosynthetic precursor molecules, and reducing power in order to respond to growth and proliferation signals and may provide a selective advantage in the hypoxic and acidic microenvironments that are often a feature of solid tumors. Oncogenic signaling pathways and responses to the local microenvironment combine to produce this metabolic phenotype via a number of molecular mechanisms. A better understanding of these mechanisms in both tumor and normal tissues and a more complete understanding of how the Warburg effect interacts with the rest of the tumor metabolic network should provide opportunities for novel clinical intervention.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.334
Teacher spread0.293 · 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