Cardiovascular Diseases and their Novel Therapeutic Interventions: A Literature Review
Bibliographic record
Abstract
Introduction: Cardiovascular diseases (CVD) are responsible for millions of deaths worldwide every year and remain one of the main causes of death in low- middle-income countries. Current methods of treating CVD involve the use of lipid-lowering drugs although these patients continue to suffer from atherosclerotic disease risk. Novel RNA therapeutic strategies are being brought to light with the advancement in our understanding of cellular mechanisms and communication, however, these need to be evaluated critically before their clinical use. Methods: Electronic literature databases such as PubMed and Google Scholar were used to access review papers and research studies done in the past 25 years. Studies most relevant to RNA cardiovascular therapeutics were used to study therapeutic interventions and their limitations. Results: MicroRNAs (miRNAs), a subset of non-coding RNAs play an important function in cell-cell communication and microenvironment remodeling due to their role in cellular processes such as differentiation, proliferation, and apoptosis. Dysregulation of miRNA synthesis has been shown to drive disease pathology. Administration of the miRNAs downregulated during disease or silencing the activity of pathogenic miRNA can be used to establish the genetic composition of a healthy individual. Exosomes are cell-derived bilipid layer extracellular vesicles, 40-150 nm in size, which conduct paracrine signaling by carrying a cargo of mRNAs, non-coding RNAs, and proteins. They could be used as an efficient delivery method for miRNAs. Gene silencing therapies targeting the ApoCIII gene have emerged as novel therapeutic interventions to treating CVD with genome-wide association studies demonstrating enhanced cardioprotective function with ApoCIII deficiency. Gene silencing through miRNA delivery and antisense oligonucleotides reveals new avenues of CVD treatment. Discussion: Novel therapeutics addressing miRNA dysregulation and gene expression regulation come with caveats that need to be addressed before they are prescribed. This review describes the role of the gene silencing interventions and the implementation barriers that delay their approval for use in treating heart disease. Conclusion: The treatments and limitations addressed in this review suggest more studies are needed to determine the pharmacokinetic aspects of RNA drugs prior to establishing the use of RNA therapeutics along with conventional cholesterol-lowering drugs to ameliorate CVD risk.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".