Rob Krams

Aims: Hybrid intravascular ultrasound-optical coherence tomography (IVUS-OCT) can enable more accurate plaque characterization than single-modality intravascular imaging, enhancing treatment planning and vulnerable plaque detection. However, image interpretation in IVUS-OCT is challenging and time-consuming. To overcome this limitation, we introduce a novel histology-trained deep learning (DL)-classifier for plaque component classification in IVUS-OCT images and compare its performance against single-modality DL and expert analysts. Methods and results: IVUS-OCT frames and matched histological sections from 10 cadaveric human hearts were included in this analysis. The histological data were used to define fibrotic, calcific, and necrotic core tissue regions of interest (ROIs) in IVUS-OCT and used to train three DL-classifiers for IVUS, OCT, or hybrid IVUS-OCT image analysis (992 frames) and test their performance (264 frames). The test set was additionally annotated by experts from three different core labs, and their estimations and those of the DL-classifiers were compared with histology. The IVUS-OCT DL-classifier had a superior performance to the IVUS-DL, OCT-DL, and the expert analysts in detecting plaque phenotypes (Kappa 0.60 vs. 0.19, 0.35, and 0.53, respectively) and accurately classified 68% of histologically defined fibroatheromas. The hybrid IVUS-OCT DL-classifier also had a better performance than single-modality DL-classifiers and the experts in assessing tissue types in ROIs annotated by histology (overall accuracy 86.7% compared with 73.2% for IVUS-DL, 66.6% for OCT-DL, and 70.6% for the experts). Conclusion: Plaque characterization using a histology-trained hybrid IVUS-OCT DL-classifier is feasible and enables more accurate detection of plaque components and phenotype classification than single-modality DL-classifiers and expert analysts.

Editorial: Computational modelling of cardiovascular hemodynamics and machine learning

Nano-indentation techniques might be better equipped to assess the heterogeneous material properties of plaques than macroscopic methods but there are no bespoke protocols for this kind of material testing for coronary arteries. Therefore, we developed a measurement protocol to extract mechanical properties from healthy and atherosclerotic coronary artery tissue sections. Young's modulus was derived from force-indentation data. Metrics of collagen fibre density were extracted from the same tissue, and the local material properties were co-registered to the local collagen microstructure with a robust framework. The locations of the indentation were retrospectively classified by histological category (healthy, plaque, lipid-rich, fibrous cap) according to Picrosirius Red stain and adjacent Hematoxylin & Eosin and Oil-Red-O stains. Plaque tissue was softer ( p < 0.001) than the healthy coronary wall. Areas rich in collagen within the plaque (fibrous cap) were significantly ( p < 0.001) stiffer than areas poor in collagen/lipid-rich, but less than half as stiff as the healthy coronary media. Young's moduli correlated (Pearson's ρ = 0.53, p < 0.05) with collagen content. Atomic force microscopy (AFM) is capable of detecting tissue stiffness changes related to collagen density in healthy and diseased cardiovascular tissue. Mechanical characterization of atherosclerotic plaques with nano-indentation techniques could refine constitutive models for computational modelling.

Shear stress is known to affect many processes in (patho-) physiology through a complex, multi-molecular mechanism, termed mechanotransduction. The sheer complexity of the process has raised questions how mechanotransduction is regulated. Here, we comprehensively evaluate the literature about the role of small non-coding miRNA in the regulation of mechanotransduction. Regulation of mRNA by miRNA is rather complex, depending not only on the concentration of mRNA to miRNA, but also on the amount of mRNA competing for a single mRNA. The only mechanism to counteract the latter factor is through overarching structures of miRNA. Indeed, two overarching structures are present miRNA families and miRNA clusters, and both will be discussed in details, regarding the latest literature and a previous conducted study focussed on mechanotransduction. Both the literature and our own data support a new hypothesis that miRNA-clusters predominantly regulate mechanotransduction, affecting 65% of signalling pathways. In conclusion, a new and important mode of regulation of mechanotransduction is proposed, based on miRNA clusters. This finding implicates new avenues for treatment of mechanotransduction and atherosclerosis.

Intracranial aneurysms are associated with disturbed velocity patterns, and chronic inflammation, but the relevance for these findings are currently unknown. Here, we show that (disturbed) shear stress induced by vortices is a sufficient condition to activate the endothelial NF-kB pathway, possibly through a mechanism of mechanosensor de-activation. We provide evidence for this statement through in-vitro live cell imaging of NF-kB in HUVECs exposed to different flow conditions, stochastic modelling of flow induced NF-kB activation and induction of disturbed flow in mouse carotid arteries. Finally, CFD and immunofluorescence on human intracranial aneurysms showed a correlation similar to the mouse vessels, suggesting that disturbed shear stress may lead to sustained NF-kB activation thereby offering an explanation for the close association between disturbed flow and intracranial aneurysms.

The emerging role of YAP/TAZ in mechanotransduction