Advances in nanobased platforms for cardiovascular diseases: Early diagnosis, imaging, treatment, and tissue engineering

Nano revolution in cardiovascular health: Nanoparticles (NPs) as tiny titans for diagnosis and therapeutics

Cardiovascular diseases (CVDs) are known as life-threatening illnessescaused by severe abnormalities in the cardiovascular system. They are a leading cause of mortality and morbidity worldwide.Nanotechnology integrated substantialinnovations in cardiovascular diagnostic and therapeutic at the nanoscale. This in-depth analysis explores cutting-edge methods for diagnosing CVDs, including nanotechnological interventions and crucial components for identifying risk factors, developing treatment plans, and monitoring patients' progress with chronic CVDs.Intensive research has gone into making nano-carriers that can image and treat patients. To improve the efficiency of treating CVDs, the presentreview sheds light on a decision-tree-based solution by investigating recent and innovative approaches in CVD diagnosis by utilizing nanoparticles (NPs). Treatment choices for chronic diseases like CVD, whose etiology might take decades to manifest, are very condition-specific and disease-stage-based. Moreover, thisreview alsobenchmarks the changing landscape of employing NPs for targeted and better drug administration while examining the limitations of various NPs in CVD diagnosis, including cost, space, time, and complexity. To better understand and treatment of cardiovascular diseases, the conversation moves on to the nano-cardiovascular possibilities for medical research.We also focus on recent developments in nanoparticle applications, the ways they might be helpful, and the medical fields where they may find future use. Finally, this reviewadds to the continuing conversation on improved diagnosis and treatment approaches for cardiovascular disorders by discussing the obstacles and highlighting the revolutionary effects of nanotechnology.

Navigating the landscape of RNA delivery systems in cardiovascular disease therapeutics

Cardiovascular diseases (CVDs) stand as the leading cause of death worldwide, posing a significant global health challenge. Consequently, the development of innovative therapeutic strategies to enhance CVDs treatment is imperative. RNA-based therapies, encompassing non-coding RNAs, mRNA, aptamers, and CRISPR/Cas9 technology, have emerged as promising tools for addressing CVDs. However, inherent challenges associated with RNA, such as poor cellular uptake, susceptibility to RNase degradation, and capture by the reticuloendothelial system, underscore the necessity of combining these therapies with effective drug delivery systems. Various non-viral delivery systems, including extracellular vesicles, lipid-based carriers, polymeric and inorganic nanoparticles, as well as hydrogels, have shown promise in enhancing the efficacy of RNA therapeutics. In this review, we offer an overview of the most relevant RNA-based therapeutic strategies explored for addressing CVDs and emphasize the pivotal role of delivery systems in augmenting their effectiveness. Additionally, we discuss the current status of these therapies and the challenges that hinder their clinical translation.

ROS-Suppression Nanoplatform Combined Activation of STAT3/Bcl-2 Pathway for Preventing Myocardial Infarction in Mice

Myocardial infarction (MI) is the leading cause of death worldwide. The most effective way to treat myocardial infarction is to rescue ischemic cardiomyocytes. After an ischemic event, the overproduction of reactive oxygen species (ROS) is a key driver of myocardial injury. The produced ROS affects mitochondrial function and induces apoptosis in cardiomyocytes. This was accomplished by constructing platelet-membrane-encapsulated ROS-responsive drug-releasing nanoparticles (PMN@NIC-MalNPs) to deliver malonate and niclosamide (NIC). The results revealed that PMN@NIC-MalNPs degraded and released malonate and niclosamide in a high-level ROS microenvironment, effectively reducing the oxidative stress and apoptosis rate. By enhancing basal mitochondrial oxygen consumption rate (OCR), adenosine triphosphate (ATP) production, and spare respiratory capacity (SRC) in vitro, reduced the oxidative stress levels and restored mitochondrial function. In vivo studies revealed that the PMN@NIC-MalNPs improved cardiac dysfunction, inhibited succinate dehydrogenase (SDH) activity, increased ATP production, and reduced the myocardial infarct size in myocardial infarction model mice. Further, transcriptome analysis and Western blot revealed that PMN@NIC-MalNPs prevented apoptosis by activating the expressions of the signal transducer and activator of transcription 3 (STAT3) and Bcl-2, and inhibiting the expression of Bax. Thus, this study provides a novel therapeutic solution for treating myocardial infarction and predicting the viability of an antioxidant and antiapoptotic therapeutic solution in the treatment of myocardial injury.

A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease

Cardiovascular disease (CVD) remains a leading cause of death globally, presenting significant challenges in early detection and treatment. The complexity of CVD arises from its multifaceted nature, influenced by a combination of genetic, environmental, and lifestyle factors. Traditional diagnostic approaches often struggle to effectively integrate and interpret the heterogeneous data associated with CVD. Addressing this challenge, we introduce a novel Attention-Based Cross-Modal (ABCM) transfer learning framework. This framework innovatively merges diverse data types, including clinical records, medical imagery, and genetic information, through an attention-driven mechanism. This mechanism adeptly identifies and focuses on the most pertinent attributes from each data source, thereby enhancing the model's ability to discern intricate interrelationships among various data types. Our extensive testing and validation demonstrate that the ABCM framework significantly surpasses traditional single-source models and other advanced multi-source methods in predicting CVD. Specifically, our approach achieves an accuracy of 93.5%, precision of 92.0%, recall of 94.5%, and an impressive area under the curve (AUC) of 97.2%. These results not only underscore the superior predictive capability of our model but also highlight its potential in offering more accurate and early detection of CVD. The integration of cross-modal data through attention-based mechanisms provides a deeper understanding of the disease, paving the way for more informed clinical decision-making and personalized patient care.