Applications of Nanotechnology in the Field of Cardiology.

Cardiovascular diseases (CVDs) are a leading cause of death globally, demanding innovative therapeutic strategies. Nanoformulations, including nanoparticles, address challenges in drug delivery, stem cell therapy, imaging, and gene delivery. Nanoparticles enhance drug solubility, bioavailability, and targeted delivery, with gas microbubbles, liposomal preparations, and paramagnetic nanoparticles showing potential in treating atherosclerosis and reducing systemic side effects. In stem cell therapy, nanoparticles improve cell culture, utilizing three-dimensional nanofiber scaffolds and enhancing cardiomyocyte growth. Gold nanoparticles and poly(lactic-co-glycolic acid) (PLGA)-derived microparticles promote stem cell survival. Stem cell imaging utilizes direct labeling with nanoparticles for magnetic resonance imaging (MRI), while optical tracking employs dye-conjugated nanoparticles. In gene delivery, polymeric nanoparticles like polyethylenimine (PEI) and dendrimers, graphene-based carriers, and chitosan nanoparticles offer alternatives to virus-mediated gene transfer. The potential of magnetic nanoparticles in gene therapy is explored, particularly in hepatocellular carcinoma. Overall, nanoparticles have transformative potential in cardiovascular disease management, with ongoing research poised to enhance clinical outcomes.

The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders.

Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.