Bibliographic record
Abstract
Over the last 20 years it has become recognized that low-grade inflammation plays a role in cardiovascular disease. More recently, participation of the innate and the adaptive immune response in mechanisms that contribute to inflammation in cardiovascular disease has been reported in atherosclerosis and hypertension. Different subsets of lymphocytes and their cytokines are involved in vascular remodelling in hypertension, chronic kidney disease and heart disease. Effector T-cells include Th1 (interferon-γ-producing) and Th2 (interleukin-4 producing) lymphocytes, as well as Th17 (which produce interleukin-17) and T-suppressor lymphocytes such as T(reg)-cells (regulatory T-cells), which express the transcription factor Foxp3 (forkhead box P3) and participate respectively as pro- and anti-inflammatory cells. Pro-inflammatory T-lymphocytes participate in mechanisms of cardiovascular disease in part by mediating the effects of angiotensin II and mineralocorticoids. Involvement of immune mechanisms in cardiac, vascular and renal changes in hypertension has been demonstrated in many experimental models, an example being the Dahl-salt sensitive rat and the spontaneously hypertensive rat. How activation of immunity is triggered remains unknown, but neo-antigens could be generated by elevated blood pressure through damage-associated molecular pattern receptors or other mechanisms. Once activated, Th1 cells may contribute to blood pressure elevation by affecting the kidney, vascular remodelling of blood vessels directly via the effects of the cytokines produced or through their effects on perivascular fat. T(reg)-cells protect from blood pressure elevation by acting upon similar targets. Recent data suggests that participation of these mechanisms that have been demonstrated already in murine models also occurs in humans. These novel findings may open the way for new therapeutic approaches to improve outcomes in hypertension and cardiovascular disease in humans.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".