Artifact quantification of venous stents in the MRI environment: Differences between braided and laser-cut designs

PurposeTo quantify B₀- and B₁-induced imaging artifacts of braided venous stents and to compare the artifacts to a set of laser-cut stents used in venous interventions.MethodsThree prototypes of braided venous stents with different geometries were tested in vitro. B₀ field distortion maps were measured via the frequency shift $\mathrm{\Delta }f$ using multi-echo imaging. B₁ distortions were quantified using the double angle method. The relative amplitudes $B_1^{\mathit{rel}}$ were calculated to compare the intraluminal alteration of B₁. Measurements were repeated with the stents in three different orientations: parallel, diagonal and orthogonal to B₀.ResultsAt 1.5 T, the braided stents induced a maximum frequency shift of $\left|\mathrm{\Delta }f\left(\mathit{x}\right)\right|<100\mathrm{H}\mathrm{z}$. Signal voids were limited to a distance of 2 mm to the stent walls at an echo time of 3 ms. No substantial difference in the B₀ field distortions was seen between laser-cut and braided venous stents. $B_1^{\mathit{rel}}$ maps showed strongly varying distortion patterns in the braided stents with the mean intraluminal $B_1^{\mathit{rel}}$ ranging from $\left(63\pm 18\right)\%$ in prototype 1 to $\left(98\pm 38\right)\%$ in prototype 2. Compared to laser-cut stents the braided stents showed a 5 to 9 times higher coefficient of variation of the intraluminal $B_1^{\mathit{rel}}$.ConclusionBraided venous stent prototypes allow for MR imaging of the intraluminal area without substantial signal voids due to B₀-induced artifacts. Whereas B₁ is attenuated homogeneously in laser-cut stents, the B₁ distortion in braided stents is more inhomogeneous and shows areas with enhanced amplitude. This could potentially be used in braided stent designs for intraluminal signal amplification.     

A microdosimetric analysis of the interactions of mono-energetic neutrons with human tissue

Nuclear reactions induced during high-energy radiotherapy produce secondary neutrons that, due to their carcinogenic potential, constitute an important risk for the development of iatrogenic cancer. Experimental and epidemiological findings indicate a marked energy dependence of neutron relative biological effectiveness (RBE) for carcinogenesis, but little is reported on its physical basis. While the exact mechanism of radiation carcinogenesis is yet to be fully elucidated, numerical microdosimetry can be used to predict the biological consequences of a given irradiation based on its microscopic pattern of energy depositions. Building on recent studies, this work investigated the physics underlying neutron RBE by using the microdosimetric quantity dose-mean lineal energy $\left({\bar{y}}_D\right)$ as a proxy. A simulation pipeline was constructed to explicitly calculate the ${\bar{y}}_D$ of radiation fields that consisted of (i) the open source Monte Carlo toolkit Geant4, (ii) its radiobiological extension Geant4-DNA, and (iii) a weighted track-sampling algorithm. This approach was used to study mono-energetic neutrons with initial kinetic energies between 1 eV and 10 MeV at multiple depths in a tissue-equivalent phantom. Spherical sampling volumes with diameters between 2 nm and 1 $\mathrm{\mu }$m were considered. To obtain a measure of RBE, the neutron ${\bar{y}}_D$ values were divided by those of 250 keV X-rays that were calculated in the same way. Qualitative agreement was found with published radiation protection factors and simulation data, allowing for the dependencies of neutron RBE on depth and energy to be discussed in the context of the neutron interaction cross sections and secondary particle distributions in human tissue.     

Protein Interaction Network-based Deep Learning Framework for Identifying Disease-Associated Human Proteins

Infectious diseases in humans appear to be one of the most primary public health issues. Identification of novel disease-associated proteins will furnish an efficient recognition of the novel therapeutic targets. Here, we develop a Graph Convolutional Network (GCN)-based model called PINDeL to identify the disease-associated host proteins by integrating the human Protein Locality Graph and its corresponding topological features. Because of the amalgamation of GCN with the protein interaction network, PINDeL achieves the highest accuracy of $83.45\%$ while AUROC and AUPRC values are $0.90$ and $0.88$, respectively. With high accuracy, recall, F1-score, specificity, AUROC, and AUPRC, PINDeL outperforms other existing machine-learning and deep-learning techniques for disease gene/protein identification in humans. Application of PINDeL on an independent dataset of 24320 proteins, which are not used for training, validation, or testing purposes, predicts 6448 new disease-protein associations of which we verify 3196 disease-proteins through experimental evidence like disease ontology, Gene Ontology, and KEGG pathway enrichment analyses. Our investigation informs that experimentally-verified 748 proteins are indeed responsible for pathogen-host protein interactions of which 22 disease-proteins share their association with multiple diseases such as cancer, aging, chem-dependency, pharmacogenomics, normal variation, infection, and immune-related diseases. This unique Graph Convolution Network-based prediction model is of utmost use in large-scale disease-protein association prediction and hence, will provide crucial insights on disease pathogenesis and will further aid in developing novel therapeutics.     

Crowding-induced protein destabilization in the absence of soft attractions

It is generally assumed that volume exclusion by macromolecular crowders universally stabilizes the native states of proteins and destabilization suggests soft attractions between crowders and protein. Here we show that proteins can be destabilized even by crowders that are purely repulsive. With a coarse-grained sequence-based model, we study the folding thermodynamics of two sequences with different native folds, a helical hairpin and a β-barrel, in a range of crowder volume fractions, ${\phi }_{\text{c}}$. We find that the native state, N, remains structurally unchanged under crowded conditions, while the size of the unfolded state, U, decreases monotonically with ${\phi }_{\text{c}}$. Hence, for all ${\phi }_{\text{c}}>0$, U is entropically disfavored relative to N. This entropy-centric view holds for the helical hairpin protein, which is stabilized under all crowded conditions as quantified by changes in either the folding midpoint temperature, $T_{\text{m}}$, or the free energy of folding. We find, however, that the β-barrel protein is destabilized under low-T, low-${\phi }_{\text{c}}$ conditions. This destabilization can be understood from two characteristics of its folding: 1) a relatively compact U at $T<T_{\text{m}}$, such that U is only weakly disfavored entropically by the crowders; and 2) a transient, compact, and relatively low-energy nonnative state that has a maximum population of only a few percent at ${\phi }_{\text{c}}=0$, but increasing monotonically with ${\phi }_{\text{c}}$. Overall, protein destabilization driven by hard-core effects appears possible when a compaction of U leads to even a modest population of compact nonnative states that are energetically competitive with N.