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Record W2009664794 · doi:10.1371/journal.pone.0056551

Probing Early Tumor Response to Radiation Therapy Using Hyperpolarized [1-13C]pyruvate in MDA-MB-231 Xenografts

2013· article· en· W2009664794 on OpenAlexafffund
Albert P. Chen, William Chu, Yiping Gu, Charles H. Cunnhingham

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsIn vivoLactate dehydrogenaseRadiation therapyCancer researchHyperpolarization (physics)MedicineChemistryNuclear medicinePathologyInternal medicineBiologyBiochemistryEnzymeNuclear magnetic resonance spectroscopy

Abstract

fetched live from OpenAlex

Following radiation therapy (RT), tumor morphology may remain unchanged for days and sometimes weeks, rendering anatomical imaging methods inadequate for early detection of therapeutic response. Changes in the hyperpolarized [1-¹³C]lactate signals observed in vivo following injection of pre-polarized [1-¹³C]pyruvate has recently been shown to be a marker for tumor progression or early treatment response. In this study, the feasibility of using ¹³C metabolic imaging with [1-¹³C]pyruvate to detect early radiation treatment response in a breast cancer xenograft model was demonstrated in vivo and in vitro. Significant decreases in hyperpolarized [1-¹³C]lactate relative to [1-¹³C]pyruvate were observed in MDA-MB-231 tumors 96 hrs following a single dose of ionizing radiation. Histopathologic data from the treated tumors showed higher cellular apoptosis and senescence; and changes in the expression of membrane monocarboxylate transporters and lactate dehydrogenase B were also observed. Hyperpolarized ¹³C metabolic imaging may be a promising new tool to develop novel and adaptive therapeutic regimens for patients undergoing RT.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.045
GPT teacher head0.270
Teacher spread0.225 · 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

Citations35
Published2013
Admission routes2
Has abstractyes

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