Vascular biology of ageing—Implications in hypertension
Bibliographic record
Abstract
Ageing is associated with functional, structural and mechanical changes in arteries that closely resemble the vascular alterations in hypertension. Characteristic features of large and small arteries that occur with ageing and during the development of hypertension include endothelial dysfunction, vascular remodelling, inflammation, calcification and increased stiffness. Arterial changes in young hypertensive patients mimic those in old normotensive individuals. Hypertension accelerates and augments age-related vascular remodelling and dysfunction, and ageing may impact on the severity of vascular damage in hypertension, indicating close interactions between biological ageing and blood pressure elevation. Molecular and cellular mechanisms underlying vascular alterations in ageing and hypertension are common and include aberrant signal transduction, oxidative stress and activation of pro-inflammatory and pro-fibrotic transcription factors. Strategies to suppress age-associated vascular changes could ameliorate vascular damage associated with hypertension. An overview on the vascular biology of ageing and hypertension is presented and novel molecular mechanisms contributing to these processes are discussed. The complex interaction between biological ageing and blood pressure elevation on the vasculature is highlighted. This article is part of a Special Issue entitled: CV Ageing.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".