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Record W4317233792 · doi:10.1002/2211-5463.13559

The breast cancer microenvironment and lipoprotein lipase: Another negative notch for a beneficial enzyme?

2023· review· en· W4317233792 on OpenAlexafffund
Makayla M. Bavis, Allison M. Nicholas, Alexandria J. Tobin, Sherri L. Christian, Robert Brown

Bibliographic record

VenueFEBS Open Bio · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaMemorial University of NewfoundlandUniversity of WashingtonBeatrice Hunter Cancer Research InstituteCancer Research Institute
KeywordsLipoprotein lipaseLipaseBreast cancerTumor microenvironmentEnzymeLipid metabolismCancer researchLipoproteinBiochemistryCancerChemistryBiologyInternal medicineCholesterolMedicineTumor cells

Abstract

fetched live from OpenAlex

The energy demand of breast cancers is in part met through the β-oxidation of exogenous fatty acids. Fatty acids may also be used to aid in cell signaling and toward the construction of new membranes for rapidly proliferating tumor cells. A significant quantity of fatty acids comes from the hydrolysis of lipoprotein triacylglycerols and phospholipids by lipoprotein lipase (LPL). The lipid obtained via LPL in the breast tumor microenvironment may thus promote breast tumor growth and development. In this hypothesis article, we introduce LPL, provide a meta-analysis of RNAseq data showing that LPL is associated with poor prognosis, and explain how LPL might play a role in breast cancer prognosis over time.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.034
GPT teacher head0.326
Teacher spread0.292 · 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 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

Citations12
Published2023
Admission routes2
Has abstractyes

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