MétaCan
Menu
Back to cohort
Record W4404671961 · doi:10.1097/moh.0000000000000853

Unraveling lipid metabolism for acute myeloid leukemia therapy

2024· review· en· W4404671961 on OpenAlexaff
Cristiana O’Brien, Courtney L. Jones

Bibliographic record

VenueCurrent Opinion in Hematology · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersLeukemia and Lymphoma SocietyRally Foundation
KeywordsLipid metabolismMyeloid leukemiaLeukemiaBiologyStem cellCell biologyBioinformaticsCancer researchBiochemistryImmunology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The aim of this review is to highlight the importance of lipids' intricate and interwoven role in mediating diverse acute myeloid leukemia (AML) processes, as well as potentially novel lipid targeting strategies. This review will focus on new studies of lipid metabolism in human leukemia, particularly highlighting work in leukemic stem cells (LSCs), where lipids were assessed directly as a metabolite. RECENT FINDINGS: Lipid metabolism is essential to support LSC function and AML survival through diverse mechanisms including supporting energy production, membrane composition, signaling pathways, and ferroptosis. Recent work has highlighted the role of lipid rewiring in metabolic plasticity which can underlie therapy response, the impact of cellular and genetic heterogeneity in AML on lipid metabolism, and the discovery of noncanonical roles of lipid related proteins in AML. SUMMARY: Recent findings around lipid metabolism clearly demonstrates their importance to our understanding and therapeutic targeting of AML. We have only begun to unravel the regulation and utilization of lipids in this disease. Further, understanding the layered dynamics of lipid homeostasis could provide novel opportunities to target lipid metabolism in AML and LSCs with the potential of improving outcomes for patients with AML.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.074
GPT teacher head0.405
Teacher spread0.331 · 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.

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

Citations11
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in HematologySame topicCancer, Lipids, and MetabolismFrench-language works237,207