The Role of Pro-Inflammatory Cytokines in the Pathogenesis of Cardiovascular Disease
Bibliographic record
Abstract
With cardiovascular disease (CVD) being a primary source of global morbidity and mortality, it is crucial that we understand the molecular pathophysiological mechanisms at play. Recently, numerous pro-inflammatory cytokines have been linked to several different CVDs, which are now often considered an adversely pro-inflammatory state. These cytokines most notably include interleukin-6 (IL-6),tumor necrosis factor (TNF)α, and the interleukin-1 (IL-1) family, amongst others. Not only does inflammation have intricate and complex interactions with pathophysiological processes such as oxidative stress and calcium mishandling, but it also plays a role in the balance between tissue repair and destruction. In this regard, pre-clinical and clinical evidence has clearly demonstrated the involvement and dynamic nature of pro-inflammatory cytokines in many heart conditions; however, the clinical utility of the findings so far remains unclear. Whether these cytokines can serve as markers or risk predictors of disease states or act as potential therapeutic targets, further extensive research is needed to fully understand the complex network of interactions that these molecules encompass in the context of heart disease. This review will highlight the significant advances in our understanding of the contributions of pro-inflammatory cytokines in CVDs, including ischemic heart disease (atherosclerosis, thrombosis, acute myocardial infarction, and ischemia-reperfusion injury), cardiac remodeling (hypertension, cardiac hypertrophy, cardiac fibrosis, cardiac apoptosis, and heart failure), different cardiomyopathies as well as ventricular arrhythmias and atrial fibrillation. In addition, this article is focused on discussing the shortcomings in both pathological and therapeutic aspects of pro-inflammatory cytokines in CVD that still need to be addressed by future studies.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".