1,2,3,4,6‐Penta‐<i>O</i>‐galloyl‐β‐<scp>d</scp>‐glucose modulates perivascular inflammation and prevents vascular dysfunction in angiotensin II‐induced hypertension
Bibliographic record
Abstract
Abstract Background and Purpose Hypertension is a multifactorial disease, manifested by vascular dysfunction, increased superoxide production, and perivascular inflammation. In this study, we have hypothesized that 1,2,3,4,6‐penta‐ O ‐galloyl‐β‐ d ‐glucose (PGG) would inhibit vascular inflammation and protect from vascular dysfunction in an experimental model of hypertension. Experimental Approach PGG was administered to mice every 2 days at a dose of 10 mg·kg −1 i.p during 14 days of Ang II infusion. It was used at a final concentration of 20 μM for in vitro studies in cultured cells. Key Results Ang II administration increased leukocyte and T‐cell content in perivascular adipose tissue (pVAT), and administration of PGG significantly decreased total leukocyte and T‐cell infiltration in pVAT. This effect was observed in relation to all T‐cell subsets. PGG also decreased the content of T‐cells bearing CD25, CCR5, and CD44 receptors and the expression of both monocyte chemoattractant protein 1 (CCL2) in aorta and RANTES (CCL5) in pVAT. PGG administration decreased the content of TNF + and IFN‐γ + CD8 T‐cells and IL‐17A + CD4 + and CD3 + CD4 − CD8 − cells. Importantly, these effects of PGG were associated with improved vascular function and decreased ROS production in the aortas of Ang II‐infused animals independently of the BP increase. Mechanistically, PGG (20 μM) directly inhibited CD25 and CCR5 expression in cultured T‐cells. It also decreased the content of IFN‐γ + CD8 + and CD3 + CD4 − CD8 − cells and IL‐17A + CD3 + CD4 − CD8 − cells. Conclusion and Implication PGG may constitute an interesting immunomodulating strategy in the regulation of vascular dysfunction and hypertension. Linked Articles This article is part of a themed section on Immune Targets in Hypertension. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.12/issuetoc
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".