Paradoxical Changes: EMMPRIN Tissue and Plasma Levels in Marfan Syndrome-Related Thoracic Aortic Aneurysms.

Background: Thoracic aortic aneurysms (TAAs) associated with Marfan syndrome (MFS) are unique in that extracellular matrix metalloproteinase inducer (EMMPRIN) levels do not behave the way they do in other cardiovascular pathologies. EMMPRIN is shed into the circulation through the secretion of extracellular vesicles. This has been demonstrated to be dependent upon the Membrane Type-1 MMP (MT1-MMP). We investigated this relationship in MFS TAA tissue and plasma to discern why unique profiles may exist. Methods: Protein targets were measured in aortic tissue and plasma from MFS patients with TAAs and were compared to healthy controls. The abundance and location of MT1-MMP was modified in aortic fibroblasts and secreted EMMPRIN was measured in conditioned culture media. Results: EMMPRIN levels were elevated in MFS TAA tissue but reduced in plasma, compared to the controls. Tissue EMMPRIN elevation did not induce MMP-3, MMP-8, or TIMP-1 expression, while MT1-MMP and TIMP-2 were elevated. MMP-2 and MMP-9 were reduced in TAA tissue but increased in plasma. In aortic fibroblasts, EMMPRIN secretion required the internalization of MT1-MMP. Conclusions: In MFS, impaired EMMPRIN secretion likely contributes to higher tissue levels, influenced by MT1-MMP cellular localization. Low EMMPRIN levels, in conjunction with other MMP analytes, distinguished MFS TAAs from controls, suggesting diagnostic potential.

New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning.

This review article presents an appraisal of pioneering technologies poised to revolutionize the diagnosis and management of aortic aneurysm disease, with a primary focus on the thoracic aorta while encompassing insights into abdominal manifestations. Our comprehensive analysis is rooted in an exhaustive survey of contemporary and historical research, delving into the realms of machine learning (ML) and computer-assisted diagnostics. This overview draws heavily upon relevant studies, including Siemens' published field report and many peer-reviewed publications. At the core of our survey lies an in-depth examination of ML-driven diagnostic advancements, dissecting an array of algorithmic suites to unveil the foundational concepts anchoring computer-assisted diagnostics and medical image processing. Our review extends to a discussion of circulating biomarkers, synthesizing insights gleaned from our prior research endeavors alongside contemporary studies gathered from the PubMed Central database. We elucidate the prevalent challenges and envisage the potential fusion of AI-guided aortic measurements and sophisticated ML frameworks with the computational analyses of pertinent biomarkers. By framing current scientific insights, we contemplate the transformative prospect of translating fundamental research into practical diagnostic tools. This narrative not only illuminates present strides, but also forecasts promising trajectories in the clinical evaluation and therapeutic management of aortic aneurysm disease.

A standardized method for plasma extracellular vesicle isolation and size distribution analysis.

The following protocol describes our workflow for isolation and quantification of plasma extracellular vesicles (EVs). It requires limited sample volume so that the scientific value of specimens is maximized. These steps include isolation of vesicles by automated size exclusion chromatography and quantification by tunable resistive pulse sensing. This workflow optimizes reproducibility by minimizing variations in processing, handling, and storage of EVs. EVs have significant diagnostic and therapeutic potential, but clinical application is limited by disparate methods of data collection. This standardized protocol is scalable and ensures efficient recovery of physiologically intact EVs that may be used in a variety of downstream biochemical and functional analyses. Simultaneous measurement quantifies EV concentration and size distribution absolutely. Absolute quantification corrects for variations in EV number and size, offering a novel method of standardization in downstream applications.

Plasma microrna quantification protocol.

MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate translation and are involved in many pathological processes. They have emerged as promising biomarkers for diagnosis of conditions such as aortic aneurysm disease. Quantifying miRNAs in plasma is uniquely challenging because of the lack of standardized reproducible protocols. To facilitate the independent verification of conclusions, it is necessary to provide a thorough disclosure of all pertinent experimental details. In this technical note, we present a comprehensive protocol for quantifying plasma miRNAs using droplet digital PCR. We detail the entire workflow, including blood collection, plasma processing, cryo-storage, miRNA isolation, reverse transcription, droplet generation, PCR amplification, fluorescence reading, and data analysis. We offer comprehensive guidance regarding optimization, assay conditions, expected results, and insight into the troubleshooting of common issues. The stepwise normalization and detailed methodological guide enhance reproducibility. Moreover, multiple portions of this protocol may be automated. The data provided in this technical note is demonstrative of the values typically obtained when following its steps. To facilitate standardization in data reporting, we include a table of expected aortic aneurysm-related miRNA levels in healthy human plasma. This versatile protocol can be easily adapted to quantify most circulating miRNAs in plasma, making it a valuable resource for diagnostic development.