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Record W4285742736 · doi:10.3389/fimmu.2022.908749

The Roles of Adipose Tissue Macrophages in Human Disease

2022· review· en· W4285742736 on OpenAlexaff
Weizheng Liang, Yanxu Qi, Hongyang Yi, Chenyu Mao, Qingxue Meng, Hao Wang, Chunfu Zheng

Bibliographic record

VenueFrontiers in Immunology · 2022
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of Calgary
FundersDepartment of Education of Guangdong ProvinceNatural Science Foundation of Shandong Province
KeywordsAdipose tissueAdipose tissue macrophagesInflammationImmune systemMacrophageImmunologyPopulationMacrophage polarizationBiologySecretionMacrophage-activating factorCell biologyMedicineCancer researchWhite adipose tissueEndocrinologyIn vitroLymphokine

Abstract

fetched live from OpenAlex

Macrophages are a population of immune cells functioning in antigen presentation and inflammatory response. Research has demonstrated that macrophages belong to a cell lineage with strong plasticity and heterogeneity and can be polarized into different phenotypes under different microenvironments or stimuli. Many macrophages can be recruited by various cytokines secreted by adipose tissue. The recruited macrophages further secrete various inflammatory factors to act on adipocytes, and the interaction between the two leads to chronic inflammation. Previous studies have indicated that adipose tissue macrophages (ATMs) are closely related to metabolic diseases like obesity and diabetes. Here, we will not only conclude the current progress of factors affecting the polarization of adipose tissue macrophages but also elucidate the relationship between ATMs and human diseases. Furthermore, we will highlight its potential in preventing and treating metabolic diseases as immunotherapy targets.

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 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 score0.992
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.017
GPT teacher head0.302
Teacher spread0.285 · 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 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

Citations80
Published2022
Admission routes1
Has abstractyes

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