RESUMO
This narrative review provides an overview of the current advances, challenges, and opportunities in nanoparticle drug delivery for central nervous system (CNS) disorders. The treatment of central nervous system disorders is challenging due to the blood-brain barrier (BBB), which limits the delivery of therapeutic agents to the brain. Promising approaches to address these issues and improve the efficacy of CNS disease therapies are provided by nanoparticle-based drug delivery systems. Nanoparticles, such as liposomes, polymeric nanoparticles, dendrimers, and solid lipid nanoparticles, can be modified to enhance targeting, stability, and drug-release patterns. They allow for the encapsulation of a variety of therapeutic compounds and can be functionalized with ligands or antibodies for active targeting, minimizing off-target effects. Additionally, nanoparticles can circumvent drug resistance processes and provide versatile platforms for applications that combine therapeutic and diagnostic functions. Although the delivery of CNS medications using nanoparticles has advanced significantly, there are still challenges to be resolved. These include understanding the BBB interactions, doing long-term safety studies, and scaling up the production. However, improvements in nanotechnology and a deeper comprehension of CNS disorders provide opportunities to enhance treatment results and address unmet medical requirements. Future research and ongoing clinical trials are required to further explore the potential of nanoparticle drug delivery for CNS disorders.
RESUMO
This narrative review delves into the potential of artificial intelligence (AI) in predicting, stratifying risk, and personalizing treatment planning for congenital heart disease (CHD). CHD is a complex condition that affects individuals across various age groups. The review highlights the challenges in predicting risks, planning treatments, and prognosticating long-term outcomes due to CHD's multifaceted nature, limited data, ethical concerns, and individual variabilities. AI, with its ability to analyze extensive data sets, presents a promising solution. The review emphasizes the need for larger, diverse datasets, the integration of various data sources, and the analysis of longitudinal data. Prospective validation in real-world clinical settings, interpretability, and the importance of human clinical expertise are also underscored. The ethical considerations surrounding privacy, consent, bias, monitoring, and human oversight are examined. AI's implications include improved patient outcomes, cost-effectiveness, and real-time decision support. The review aims to provide a comprehensive understanding of AI's potential for revolutionizing CHD management and highlights the significance of collaboration and transparency to address challenges and limitations.