Bibliographic record
Abstract
Low-grade inflammation plays a role in cardiovascular disease. The innate and the adaptive immune responses participate in mechanisms that contribute to inflammatory responses. It has been increasingly appreciated that different subsets of lymphocytes and the cytokines they produce modulate the vascular remodelling that occurs in cardiovascular disease. Effector T cells such as T-helper (Th) 1 (interferon-γ-producing) and Th2 lymphocytes (that produce interleukin-4), as well as Th17 (that produce interleukin-17), and T suppressor lymphocytes including regulatory T cells (Treg), which express the transcription factor forkhead box P3 (Foxp3), are involved in the remodelling of small arteries that occurs under the action of angiotensin II, deoxycorticosterone-salt and aldosterone-salt, as well as in models of hypertension such as the Dahl-salt-sensitive rat. The mechanism whereby the immune system is activated is unclear, but it has been suggested that neo-antigens may be generated by the elevation of blood pressure or other stimuli, leading to the activation of the immune response. Activated Th1 may contribute to vascular remodelling directly on blood vessels via effects of the cytokines produced or indirectly by actions on the kidney. The protective effect of Treg may be mediated similarly directly or via renal effects. These data offer promise for the discovery of new therapeutic targets to ameliorate vascular remodelling, which could lead to improved outcome in 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".