CD28-expressing δ T cells are increased in perivascular adipose tissue of hypertensive mice and in subcutaneous adipose tissue of obese humans
Bibliographic record
Abstract
OBJECTIVES: γδ T-lymphocytes play a role in angiotensin II (AngII)-induced hypertension, vascular injury and T-cell infiltration in perivascular adipose tissue (PVAT) in mice. Mesenteric arteries of hypertensive mice and subcutaneous arteries from obese humans present similar remodeling. We hypothesized that γδ T-cell subtypes in mesenteric vessels with PVAT (MV/PVAT) from hypertensive mice and subcutaneous adipose tissue (SAT) from obese humans, who are prone to develop hypertension, would be similar. METHODS: Mice were infused with AngII for 14 days. MV/PVAT T-cells were used for single-cell RNA-sequencing (scRNA-seq). scRNA-seq data (GSE155960) of SAT CD45 + cells from three lean and three obese women were downloaded from the Gene Expression Omnibus database. RESULTS: δ T-cell subclustering identified six δ T-cell subtypes. AngII increased T-cell receptor δ variable 4 ( Trdv4 ) + γδ T-effector memory cells and Cd28high δ T EM -cells, changes confirmed by flow cytometry. δ T-cell subclustering identified nine δ T-cell subtypes in human SAT. CD28 expressing δ T-cell subclustering demonstrated similar δ T-cell subpopulations in murine MV/PVAT and human SAT. Cd28+ γδ NKT EM and Cd28high δ T EM -cells increased in MV/PVAT from hypertensive mice and CD28high δ T EM -cells in SAT from obese women compared to the lean women. CONCLUSION: Similar CD28 + δ T-cells were identified in murine MV/PVAT and human SAT. CD28 high δ T EM -cells increased in MV/PVAT in hypertensive mice and in SAT from humans with obesity, a prehypertensive condition. CD28 + δ T-lymphocytes could have a pathogenic role in human hypertension associated with obesity, and could be a potential target for therapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".