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Record W2794347175 · doi:10.1002/dmrr.2993

Micro‐environment and intracellular metabolism modulation of adipose tissue macrophage polarization in relation to chronic inflammatory diseases

2018· review· en· W2794347175 on OpenAlexaff
Xiao Zhu, Yixuan Tu, Hainan Chen, Ampadu O. Jackson, Vaibhav B. Patel, Kai Yin

Bibliographic record

VenueDiabetes/Metabolism Research and Reviews · 2018
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersConstruct Program of the Key Discipline in Hunan Province
KeywordsAdipose tissueMacrophage polarizationInflammationIntracellularInsulin resistanceAdipose tissue macrophagesLipid metabolismImmune systemMetabolismMacrophageEndocrinologyWhite adipose tissueChemistryInternal medicineBiologyMedicineCell biologyObesityImmunologyBiochemistryIn vitro

Abstract

fetched live from OpenAlex

The accumulation and pro-inflammatory polarization of immune cells, mainly macrophages, in adipose tissue (AT) are considered crucial factors for obesity-induced chronic inflammatory diseases. In this review, we highlighted the role of adipose tissue macrophage (ATM) polarization on AT function in the obese state and the effect of the micro-environment and intracellular metabolism on the dynamic switch of ATMs into their pro-inflammatory or anti-inflammatory phenotypes, which may have distinct influences on obesity-related chronic inflammatory diseases. Obesity-associated metabolic dysfunctions, including those of glucose, fatty acid, cholesterol, and other nutrient substrates such as vitamin D and iron in AT, promote the pro-inflammatory polarization of ATMs and AT inflammation via regulating the interaction between ATMs and adipocytes and intracellular metabolic pathways, including glycolysis, fatty acid oxidation, and reverse cholesterol transportation. Focusing on the regulation of ATM metabolism will provide a novel target for the treatment of obesity-related chronic inflammatory diseases, including insulin resistance, cardiovascular diseases, and cancers.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.327
Teacher spread0.295 · 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 designOther design
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

Citations32
Published2018
Admission routes1
Has abstractyes

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