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Record W2913214687 · doi:10.1126/scitranslmed.aau5758

Suppressing fatty acid uptake has therapeutic effects in preclinical models of prostate cancer

2019· article· en· W2913214687 on OpenAlexaff
Matthew J. Watt, Ashlee K. Clark, Luke A. Selth, Vanessa Haynes, Natalie L. Lister, Richard J. Rebello, Laura H. Porter, Birunthi Niranjan, Sarah Whitby, Jennifer Lo, Cheng Huang, Ralf B. Schittenhelm, Kimberley E. Anderson, Luc Furic, Poornima R. Wijayaratne, Maria Matzaris, Magdalene K. Montgomery, Melissa Papargiris, Sam Norden, Maria Febbraio, Gail P. Risbridger, Mark Frydenberg, Daniel K. Nomura, Renea A. Taylor

Bibliographic record

VenueScience Translational Medicine · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsUniversity of Alberta
FundersNational Health and Medical Research CouncilDiabetes Australia Research TrustProstate Cancer Foundation of AustraliaVictorian Cancer Agency
KeywordsProstate cancerLipidomeProstateCancerCancer researchFatty acidCarcinogenesisMedicinePharmacologyChemistryInternal medicineBiochemistryLipid metabolism

Abstract

fetched live from OpenAlex

mice reduced fatty acid uptake and the abundance of oncogenic signaling lipids and slowed cancer progression. Moreover, CD36 antibody therapy reduced cancer severity in patient-derived xenografts. We further demonstrated cross-talk between fatty acid uptake and de novo lipogenesis and found that dual targeting of these pathways more potently inhibited proliferation of human cancer-derived organoids compared to the single treatments. These findings identify a critical role for CD36-mediated fatty acid uptake in prostate cancer and suggest that targeting fatty acid uptake might be an effective strategy for treating prostate cancer.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.340
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), 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

Citations314
Published2019
Admission routes1
Has abstractyes

Explore more

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