Restenosis is the process of luminal narrowing in an atherosclerotic artery after an intra-arterial intervention such as balloon angioplasty and stenting. It is believed that this process is mainly characterized by migration and proliferation of smooth muscle cells and extracellular matrix accumulation. However, there is now increasing evidence for a role of inflammation in the development of restenosis. The underlying molecular mechanisms of restenosis are, in fact, most probably regulated by inflammatory mediators, such as cytokines. Understanding the molecular mechanisms in restenosis is crucial for the development of a suitable therapy for this disease. Recently, the use of immunosuppressives in drug-eluting stents has provided very promising results in the treatment of restenosis. In this review, we will describe the molecular mechanisms involved in restenosis with a focus on the role of inflammation and the use of immunosuppressive therapy.
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http://dx.doi.org/10.1080/07853890310014876 | DOI Listing |
Aliment Pharmacol Ther
January 2025
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading chronic liver disease worldwide, with alarming prevalence reaching epidemic proportions.
Aims And Methods: The objective of this study is to provide a comprehensive review of the latest blood proteomics studies on MASLD and metabolic dysfunction-associated steatohepatitis (MASH), with emphasis on fibrosis. Furthermore, our objective is to conduct an analysis of protein pathways and interactions by integrating proteomics data using functional enrichment analysis of the deregulated proteins.
Aging Cell
January 2025
Wuxi School of Medicine, Jiangnan University, Wuxi, China.
Endothelial dysfunction, characterized by a decline in endothelial physiological functions, is a significant aspect of cardiovascular aging, contributing notably to arterial stiffness, atherosclerosis, and hypertension. Transient receptor potential channel V4 (TRPV4), a key member of Ca-permeable channels, plays a crucial role in maintaining vascular functions. However, the role and mechanisms of TRPV4 in aging-related endothelial dysfunction remain incompletely understood.
View Article and Find Full Text PDFMol Omics
January 2025
Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India.
Lung cancer remains the leading cause of cancer-related deaths worldwide due to its poor prognosis. Despite significant advancements in the understanding of cancer development, improvements in diagnostic methods, and multimodal therapeutic regimens, the prognosis of lung cancer has still not improved. Therefore, it is reasonable to look for newer and alternative medicines for treatment.
View Article and Find Full Text PDFSAR QSAR Environ Res
January 2025
Research and Scientific Studies Unit, College of Nursing and Health Sciences, Jazan University, Jazan, Saudi Arabia.
Ras is identified as a human oncogene which is frequently mutated in human cancers. Among its three isoforms (K, N, and H), KRas is the most frequently mutated. Mutant Ras exhibits reduced GTPase activity, leading to the prolonged activation of its conformation.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues to pose a significant challenge. This challenge arises primarily from the lack of appropriate descriptors capable of retaining crucial molecular information for accurate prediction while also ensuring computational efficiency. This study presents a successful application of ML for predicting the performance of Ir-catalyzed allylic substitution reactions.
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