Advances in Understanding of the Role of Immune Cell Phenotypes in Hypertension and Associated Vascular Disease
Bibliographic record
Abstract
Many studies in the past 20 years have identified a contribution of inflammation and immune mechanisms to the pathophysiology of hypertension. Innate and adaptive immunity participate in this process. Among innate immune cells, macrophages and monocytes as well as dendritic cells, myeloid-derived suppressor cells, and neutrophils directly or via formation of neutrophil extracellular traps, play roles in the modulation of the inflammatory response in hypertension. Among adaptive immune cells, T and B cells have been implicated to varying degrees, particularly interleukin (IL)-17- and interferon γ-producing T lymphocytes, antagonized by T regulatory lymphocytes that are anti-inflammatory via production of IL-10. Among T cells that produce abundant IL-17, γδ T cells are unconventional T lymphocytes that are infrequent in the circulation in contrast to the much more abundant circulating αβ T lymphocytes, but are found mostly in tissues, and appear to play a role in triggering and sustaining inflammation in hypertension leading to vascular and renal injury. This review will provide an overview of these different immune cell phenotypes involved in the immune pathophysiology of hypertension and associated vascular disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".