Dissecting the role of cannabinoids in vascular health and disease
Bibliographic record
Abstract
Cannabis, often recognized as the most widely used illegal psychoactive substance globally, has seen a shift in its legal status in several countries and regions for both recreational and medicinal uses. This change has brought to light new evidence linking cannabis consumption to various vascular conditions. Specifically, there is an association between cannabis use and atherosclerosis, along with conditions such as arteritis, reversible vasospasm, and incidents of aortic aneurysm or dissection. Recent research has started to reveal the mechanisms connecting cannabinoid compounds to atherosclerosis development. It is well known that the primary biological roles of cannabinoids operate through the activation of cannabinoid receptor types 1 and 2. Manipulation of the endocannabinoid system, either genetically or pharmacologically, is emerging as a promising approach to address metabolic dysfunctions related to obesity. Additionally, numerous studies have demonstrated the vasorelaxant properties and potential atheroprotective benefits of cannabinoids. In preclinical trials, cannabidiol is being explored as a treatment option for monocrotaline-induced pulmonary arterial hypertension. Although existing literature suggests a direct role of cannabinoids in the pathogenesis of atherosclerosis, the correlation between cannabinoids and other vascular diseases was only reported in some case series or observational studies, and its role and precise mechanisms remain unclear. Therefore, it is necessary to summarize and update previously published studies. This review article aims to summarize the latest clinical and experimental research findings on the relationship between cannabis use and vascular diseases. It also seeks to shed light on the potential mechanisms underlying these associations, offering a comprehensive view of current knowledge in this evolving field of study.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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 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".