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Analyzing Tumor Metabolism In Vivo

2016· article· en· W2406803433 on OpenAlexfundno aff
Brandon Faubert, Ralph J. DeBerardinis

Bibliographic record

VenueAnnual Review of Cancer Biology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsIn vivoMetabolomicsPositron emission tomographyCancerPhenotypeMetabolismCancer researchBiologyMetabolic pathwayIn vivo magnetic resonance spectroscopyCancer cellMetabolomeDiseaseBioinformaticsMagnetic resonance imagingMechanism (biology)NeurosciencePathologyMedicineBiochemistryGeneticsGeneRadiology

Abstract

fetched live from OpenAlex

Altered metabolism is a clinically actionable hallmark of cancer. Some reprogrammed activities in cancer cells have predictive value and others are associated with therapeutic liabilities. Recent years have brought increased exploration of metabolism in intact tumors, complementing a large literature on cancer cell lines. We review the expanding tool kit available for studying the metabolic features of tumors in vivo. These techniques include metabolomics, positron emission tomography, magnetic resonance spectroscopy, and multiparametric magnetic resonance imaging, and they vary according to their invasiveness and breadth of metabolic assessment. Special attention is given to the emerging role of intraoperative infusions of stable, isotope-labeled nutrients, which have provided the first view of true metabolic flux in human tumors. These studies also demonstrate markedly different metabolic phenotypes from those observed in culture, indicating the potential for this approach to provide a disease-relevant view of cancer metabolism and to nominate new therapeutic targets from reprogrammed pathways.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.309
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations43
Published2016
Admission routes1
Has abstractyes

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