Mechanisms and consequences of large artery rigidity
Bibliographic record
Abstract
In this review paper, the classical and more recently described mechanisms responsible for the structural and functional characteristics of large artery rigidity are described. Mostly important, these characteristics appear to be non-specific to the primary disease process involved in arterial hypertension, diabetes mellitus, dyslipidemia, congestive heart failure, chronic uremia, and perhaps senescence, including vascular dementia. Nonspecific in terms of aetiology, the vasculopathy encountered in these diseases exhibits common structural and functional abnormalities. The identification of such abnormalities could well become the target of potent nonpharmacological and (or) pharmacological interventions capable of preventing or retarding morbidity and mortality. The structural characteristics responsible for large artery rigidity include smooth muscle cell hypertrophy, matrix collagen deposition, and recently described, dysfunction in proteoglycan metabolism. Functional abnormalities, such as bradykinin-dependent hyper-reactivity of smooth muscle cells and vasa vasorum microcirculation network disturbances, also appear to alter aortic wall rigidity. The physiopathology of target organ damage is then revisited, based on endothelial dysfunction, documented in large and resistance arteries, as well as in microcirculation networks, where altered permeability to macromolecules leads to interstitial matrix disorganization and cell damage. The clinical evaluation of large artery rigidity is described, and one of the noninvasive methods, evaluation of pulse-wave velocity, is validated in normal conditions and in disease processes. Finally, non-pharmacological and pharmacological therapeutic measures are presented, and includes physical exercise to reduce insulin resistance, and renin-angiotensin-II-aldosterone modulators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".