Bibliographic record
Abstract
Over the past almost 30 years the roles of inflammation and later of specific immune cells in the mechanisms of hypertension have increasingly been demonstrated. Initially it was shown that immune cells infiltrated perivascular tissue in the heart and kidney. Later, the demonstration that macrophages and shortly after T lymphocytes were critical for angiotensin II and DOCA-salt-induced hypertension consolidated the idea that immune mechanisms played a role in cardiovascular and renal injury in hypertension. As well, the anti-inflammatory role of T regulatory lymphocytes, the participation of dendritic cells, of gamma/delta T lymphocytes, of neutrophils through the formation of neutrophil extracellular traps (NETs), has become evident. The role of antigen presentation and intracellular mechanisms leading to activation of innate and adaptive immunity in hypertension have offered additional opportunities for eventually targeting the immune system to control inflammation-mediated damage in hypertension. Interferon-gamma and interleukin-17 appear to be important pro-inflammatory cytokines, whereas interleukin-10 plays an anti-inflammatory role in hypertension. Recent application of single cell RNA sequencing has opened novel vistas of the landscape of immune cells participating in cardiovascular and renal injury in hypertension, which can lead to new therapeutic options to control blood pressure and target organ damage in hypertensive patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".