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Biology subjects

Hultin, L.

Publications and source records attributed to Hultin, L..

2 recordsLinked to original sources

Targeted delivery of mRNA to the heart via extracellular vesicles or lipid nanoparticles

Targeted mRNA transport plays a crucial role in enhancing the therapeutic efficacy of the molecule, reducing its side effects, and minimizing off-target effects. Systemic administration of mRNA through lipid nanoparticles (LNPs) or extracellular vesicles (EVs) predominantly results in mRNA accumulation in the liver. We hypothesized that cardiac-specific EVs could more effectively target the transport of mRNA to the heart, in comparison to non-cardiac-specific EVs or LNPs. In mice, after intravenous administration, EVs from cardiac progenitor cells (CPC-EVs) were the most efficient to transport the modified mRNA, encoding vascular endothelial growth factor A (VEGF-A), to mouse heart, with minimal liver accumulation compared to non-cardiac-specific EVs or LNPs. Additionally, intracardiac injections of CPC-EVs not only demonstrate that they are the most adapted vehicle for interacting with heart tissue, delivering the mRNA to cells, and inducing maximal VEGF-A protein production, but RNA-seq analyses also revealed their minimal impact on overall gene expression, compared to LNPs or non-cardiac-specific EVs. Furthermore, immunofluorescence staining of CD31 and -SMA, markers of microvascular density, showed increased vessel density in mouse aortic rings following the delivery of VEGF-A mRNA via CPC-EVs. These findings suggest that CPC-EVs are superior in mRNA targeting to heart, communication with cardiac cells, and causing minimal transcriptomic changes during VEGF-A mRNA delivery. Therefore, CPC-EVs could be promising vectors for heart-targeted mRNA delivery, potentially reducing liver accumulation.

molecular biology↗

Convolutional neural networks for automated CMR image segmentation in rats with myocardial infarcts

In translational cardiovascular research, delineation of left ventricle (LV) in magnetic resonance images is a crucial step in assessing hearts function. Performed manually, this task is time-consuming and prone to inter- and intra-reader variability. Here we report first AI-based tool for segmentation of rat cardiovascular MRI. The method is an ensemble of fully convolutional networks and can quantify clinically relevant measures: end-diastolic volume (EDV), end-systolic volume (ESV), and ejection fraction (EF) automatically. Overall, our method reaches Dice score of 0.93 on the independent test set. The mean absolute difference of segmented volumes between automated and manual segmentation is 22.5L for EDV, 13.6L for ESV, and for EF 2.9%. Our work demonstrates the value of AI in development of tools that will significantly reduce time spent on repetitive work and result in increased efficiency of reporting data to project teams.

bioengineering↗