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Record W3025517603 · doi:10.3389/fendo.2020.00267

The Immune Landscape of Visceral Adipose Tissue During Obesity and Aging

2020· review· en· W3025517603 on OpenAlexafffund
Saad Khan, Yi Tao Chan, Xavier S. Revelo, Daniel A. Winer

Bibliographic record

VenueFrontiers in Endocrinology · 2020
Typereview
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsToronto General HospitalUniversity Health NetworkUniversity of Toronto
FundersCanadian Liver FoundationCanadian Institutes of Health ResearchNational Institute of Diabetes and Digestive and Kidney DiseasesBanting and Best Diabetes Centre, University of TorontoNovo NordiskNIH Clinical CenterNational Institutes of Health
KeywordsObesityAdipose tissueImmune systemInsulin resistanceMedicineInflammationDiseaseDiabetes mellitusDysfunctional familyPhenotypeBioinformaticsImmunologyInternal medicineEndocrinologyBiologyGeneGenetics

Abstract

fetched live from OpenAlex

Obesity and aging represent major health burdens to the global adult population. Both conditions promote the development of associated metabolic diseases such as insulin resistance. The visceral adipose tissue (VAT) is a site that becomes dysfunctional during obesity and aging, and plays a significant role during their pathophysiology. The changes in obese and aging VAT are now recognized to be partly driven by a chronic local inflammatory state, characterized by immune cells that typically adopt an inflammatory phenotype during metabolic disease. Here, we summarize the current knowledge on the immune cell landscape of the VAT during lean, obese, and aged conditions, highlighting their similarities and differences. We also briefly discuss possible linked mechanisms that fuel obesity- and age-associated VAT dysfunction.

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 score0.988
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.279
Teacher spread0.267 · 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

Citations118
Published2020
Admission routes2
Has abstractyes

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