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Record W4402021119 · doi:10.1007/s11010-024-05099-6

Macrophage energy metabolism in cardiometabolic disease

2024· review· en· W4402021119 on OpenAlexafffund
Angela Wong, Qiuyu Sun, Ismail Ibrahim Latif, Qutuba G. Karwi

Bibliographic record

VenueMolecular and Cellular Biochemistry · 2024
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsUniversity of AlbertaMemorial University of Newfoundland
FundersJaneway Children's Hospital Foundation
KeywordsMacrophage polarizationMacrophageGlycolysisBeta oxidationBiologyClinical chemistryMetabolic diseaseMetabolismMetabolic pathwayEnergy metabolismFlux (metallurgy)ImmunologyBioinformaticsBiochemistryChemistryEndocrinologyIn vitro

Abstract

fetched live from OpenAlex

In a rapidly expanding body of literature, the major role of energy metabolism in determining the response and polarization status of macrophages has been examined, and it is currently a very active area of research. The metabolic flux through different metabolic pathways in the macrophage is interconnected and complex and could influence the polarization of macrophages. Earlier studies suggested glucose flux through cytosolic glycolysis is a prerequisite to trigger the pro-inflammatory phenotypes of macrophages while proposing that fatty acid oxidation is essential to support anti-inflammatory responses by macrophages. However, recent studies have shown that this understanding is oversimplified and that the metabolic control of macrophage polarization is highly complex and not fully defined yet. In this review, we systematically reviewed and summarized the literature regarding the role of energy metabolism in controlling macrophage activity and how that might be altered in cardiometabolic diseases, namely heart failure, obesity, and diabetes. We critically appraised the experimental studies and methodologies in the published studies. We also highlighted the challenging concepts in macrophage metabolism and identified several research questions yet to be addressed in future investigations.

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 score0.835
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 science0.0000.000
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 head0.007
GPT teacher head0.247
Teacher spread0.240 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

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