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Record W3105987290 · doi:10.1002/oby.23039

Sex Affects Regional Variations in Subcutaneous Adipose Tissue T Cells but not Macrophages in Adults with Obesity

2020· article· en· W3105987290 on OpenAlexafffund
Jessica Murphy, Kerri Z. Delaney, Vi Dam, Bjorn T. Tam, Natalie Khor, Michael A. Tsoukas, José A. Morais, Sylvia Santosa

Bibliographic record

VenueObesity · 2020
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsMontreal General HospitalMcGill UniversityRoyal Victoria HospitalConcordia UniversityHôpital du Sacré-Cœur de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaHeart and Stroke Foundation of Canada
KeywordsAdipose tissueSubcutaneous adipose tissueObesitySubcutaneous fatMedicineInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVE: The inflammatory environment in lower-body subcutaneous adipose tissue (SAT) has been largely unexplored. This study aimed to examine the effects of region (upper body vs. lower body) and sex on SAT immune cell profiles in young adults with obesity. METHODS: ) with obesity via needle aspiration. Flow cytometry was used to quantify macrophage (CD68+) and T-cell (CD3+) subpopulations in the stromovascular fraction of each SAT region. RESULTS: Females had a greater proportion of most T-cell types (CD3+CD4+CD45RA+, CD3+CD4+CD45RA-, and CD3+CD8+CD45RA+) in FEM compared with AB SAT, while males had similar proportions in both regions. Regardless of sex, the M1-like macrophage population (CD68+CD206-) was proportionally higher in AB SAT than in FEM SAT. CONCLUSIONS: Results showed that T-cell populations vary by SAT region in females but not males. Both sexes, however, have proportionately more proinflammatory macrophages in upper-body than in lower-body SAT. It remains to be seen how these unique immune cell profiles in males and females with obesity contribute to adipose tissue inflammation and metabolic disease risk.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2020
Admission routes2
Has abstractyes

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