Exosomes in Cardiovascular Diseases: Pathological Potential of Nano-Messenger
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it