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Record W2996145504 · doi:10.1038/s41598-019-55985-z

Lipid accumulation and oxidation in glioblastoma multiforme

2019· article· en· W2996145504 on OpenAlexaff
Bouchra Taïb, Amine Mohamed Aboussalah, Mohammed Moniruzzaman, Suming Chen, Norman J. Haughey, Sangwon F. Kim, Rexford S. Ahima

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid metabolism and biosynthesis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMonoacylglycerol lipaseLipid dropletOleic acidLipid metabolismTriglycerideFatty acidGlioblastomaPerilipinBeta oxidationBiochemistryCancer researchChemistryLipaseFatty acid synthesisCholesterolBiologyLipolysisEnzymeEndocannabinoid systemAdipose tissue

Abstract

fetched live from OpenAlex

Glioblastoma multiforme (GBM) is the most common and lethal primary malignant brain tumor in adults. Despite the multimodal standard treatments for GBM, the median survival is still about one year. Analysis of brain tissues from GBM patients shows that lipid droplets are highly enriched in tumor tissues while undetectable in normal brain tissues, yet the identity and functions of lipid species in GBM are not well understood. The aims of the present work are to determine how GBM utilizes fatty acids, and assess their roles in GBM proliferation. Treatment of U138 GBM cells with a monounsaturated fatty acid, oleic acid, induces accumulation of perilipin 2-coated lipid droplets containing triglycerides enriched in C18:1 fatty acid, and increases fatty acid oxidation. Interestingly, oleic acid also increases glucose utilization and proliferation of GBM cells. In contrast, pharmacologic inhibition of monoacylglycerol lipase attenuates GBM proliferation. Our findings demonstrate that monounsaturated fatty acids promote GBM proliferation via triglyceride metabolism, suggesting a novel lipid droplet-mediated pathway which may be targeted for GBM treatment.

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)0.001
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 score0.100
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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

Citations149
Published2019
Admission routes1
Has abstractyes

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