Vascular inflammation in hypertension and diabetes: molecular mechanisms and therapeutic interventions
Bibliographic record
Abstract
More than 80% of patients with type 2 diabetes mellitus develop hypertension, and approx. 20% of patients with hypertension develop diabetes. This combination of cardiovascular risk factors will account for a large proportion of cardiovascular morbidity and mortality. Lowering elevated blood pressure in diabetic hypertensive individuals decreases cardiovascular events. In patients with hypertension and diabetes, the pathophysiology of cardiovascular disease is multifactorial, but recent evidence points toward the presence of an important component dependent on a low-grade inflammatory process. Angiotensin II may be to a large degree responsible for triggering vascular inflammation by inducing oxidative stress, resulting in up-regulation of pro-inflammatory transcription factors such as NF-kappaB (nuclear factor kappaB). These, in turn, regulate the generation of inflammatory mediators that lead to endothelial dysfunction and vascular injury. Inflammatory markers (e.g. C-reactive protein, chemokines and adhesion molecules) are increased in patients with hypertension and metabolic disorders, and predict the development of cardiovascular disease. Lifestyle modification and pharmacological approaches (such as drugs that target the renin-angiotensin system) may reduce blood pressure and inflammation in patients with hypertension and metabolic disorders, which will reduce cardiovascular risk, development of diabetes and cardiovascular morbidity and mortality.
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 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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".