Exosomes in Cardiovascular Diseases: Pathological Potential of Nano-Messenger
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) represent a major global health problem, due to their continued high incidences and mortality. The last few decades have witnessed new advances in clinical research which led to increased survival and recovery in CVD patients. Nevertheless, elusive and multifactorial pathophysiological mechanisms of CVD development perplexed researchers in identifying efficacious therapeutic interventions. Search for novel and effective strategies for diagnosis, prevention, and intervention for CVD has shifted research focus on extracellular vesicles (EVs) in recent years. By transporting molecular cargo from donor to recipient cells, EVs modulate gene expression and influence the phenotype of recipient cells, thus EVs prove to be an imperative component of intercellular signaling. Elucidation of the role of EVs in intercellular communications under physiological conditions implied the enormous potential of EVs in monitoring and treatment of CVD. The EVs secreted from the myriad of cells in the cardiovascular system such as cardiomyocytes, cardiac fibroblasts, cardiac progenitor cells, endothelial cells, inflammatory cells may facilitate the communication in physiological and pathological conditions. Understanding EVs-mediated cellular communication may delineate the mechanism of origin and progression of cardiovascular diseases. The current review summarizes exosome-mediated paracrine signaling leading to cardiovascular disease. The mechanistic role of exosomes in cardiovascular disease will provide novel avenues in designing diagnosis and therapeutic interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".