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The combination of high-throughput sequencing and in vivo crosslinking approaches leads to the progressive uncovering of the complex interdependence between cellular transcriptome and proteome. Yet, the molecular determinants governing interactions in protein-RNA networks are not well understood. Here we investigated the relationship between the structure of an RNA and its ability to interact with proteins. Analysing in silico, in vitro and in vivo experiments, we find that the amount of double-stranded regions in an RNA correlates with the number of protein contacts. This relationship —which we call structure-driven protein interactivity— allows classification of RNA types, plays a role in gene regulation and could have implications for the formation of phase-separated ribonucleoprotein assemblies. We validate our hypothesis by showing that a highly structured RNA can rearrange the composition of a protein aggregate. We report that the tendency of proteins to phase-separate is reduced by interactions with specific RNAs.
Range variability in cmr feature tracking multilayer strain across different stages of heart failure
(2019)
Heart failure (HF) is associated with progressive ventricular remodeling and impaired contraction that affects distinctly various regions of the myocardium. Our study applied cardiac magnetic resonance (CMR) feature tracking (FT) to assess comparatively myocardial strain at 3 distinct levels: subendocardial (Endo-), mid (Myo-) and subepicardial (Epi-) myocardium across an extended spectrum of patients with HF. 59 patients with HF, divided into 3 subgroups as follows: preserved ejection fraction (HFpEF, N = 18), HF with mid-range ejection fraction (HFmrEF, N = 21), HF with reduced ejection fraction (HFrEF, N = 20) and a group of age- gender- matched volunteers (N = 17) were included. Using CMR FT we assessed systolic longitudinal and circumferential strain and strain-rate at Endo-, Myo- and Epi- levels. Strain values were the highest in the Endo- layer and progressively lower in the Myo- and Epi- layers respectively, this gradient was present in all the patients groups analyzed but decreased progressively in HFmrEF and further on in HFrEF groups. GLS decreased with the severity of the disease in all 3 layers: Normal > HFpEF > HFmrEF > HFrEF (Endo-: −23.0 ± 3.5 > −20.0 ± 3.3 > −16.4 ± 2.2 > −11.0 ± 3.2, p < 0.001, Myo-: −20.7 ± 2.4 > −17.5.0 ± 2.6 > −14.5 ± 2.1 > −9.6 ± 2.7, p < 0.001; Epi-: −15.7 ± 1.9 > −12.2 ± 2.1 > −10.6 ± 2.3 > −7.7 ± 2.3, p < 0.001). In contrast, GCS was not different between the Normal and HFpEF (Endo-: −34.5 ± 6.2 vs −33.9 ± 5.7, p = 0.51; Myo-: −21.9 ± 3.8 vs −21.3 ± 2.2, p = 0.39, Epi-: −11.4 ± 2.0 vs −10.9 ± 2.3, p = 0.54) but was, as well, markedly lower in the systolic heart failure groups: Normal > HFmrEF > HFrEF (Endo-: −34.5 ± 6.2 > −20.0 ± 4.2 > 12.3 ± 4.2, p < 0.001; Myo-: −21.9 ± 3.8 > −13.0 ± 3.4 > −8.0 ± 2.7. p < 0.001; Epi-: −11.4 ± 2.0 > −7.9 ± 2.3 > −4.5 ± 1.9. p < 0.001). CMR feature tracking multilayer strain assessment identifies large range differences between distinct myocardial regions. Our data emphasizes the importance of sub-endocardial myocardium for cardiac contraction and thus, its predilect role in imaging detection of functional impairment. CMR feature tracking offers a convenient, readily available, platform to evaluate myocardial contraction with excellent spatial resolution, rendering further details about discrete areas of the myocardium. Using this technique across distinct groups of patients with heart failure (HF), we demonstrate that subendocardial regions of the myocardium exhibit much higher strain values than mid-myocardium or subepicardial and are more sensitive to detect contractile impairment. We also show comparatively higher values of circumferential strain compared with longitudinal and a higher sensitivity to detect contractile impairment. A newly characterized group of patients, HF with mid-range ejection fraction (EF), shows similar traits of decompensation but has relatively higher strain values as patients with HF with reduced EF.
Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.