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 New Compartmental Epidemiological Model for COVID-19 with a Case Study of Portugal

We propose a compartmental mathematical model for the spread of the COVID-19 disease, showing its usefulness with respect to the pandemic in Portugal, from the first recorded case in the country till the end of the three states of emergency. New results include the compartmental model, described by a system of seven ordinary differential equations; proof of positivity and boundedness of solutions; investigation of equilibrium points and their stability analysis; computation of the basic reproduction number; and numerical simulations with official real data from the Portuguese health authorities. Besides completely new, the proposed model allows to describe quite well the spread of COVID-19 in Portugal, fitting simultaneously not only the number of active infected individuals but also the number of hospitalized individuals, respectively with a $L^2$ error of $9.2152e-04$ and $1.6136e-04$ with respect to the initial population. Such results are very important, from a practical point of view, and far from trivial from a mathematical perspective. Moreover, the obtained value for the basic reproduction number is in agreement with the one given by the Portuguese authorities at the end of the three emergency states.     

A deep learning approach for magnetic resonance fingerprinting: Scaling capabilities and good training practices investigated by simulations.

MR fingerprinting (MRF) is an innovative approach to quantitative MRI. A typical disadvantage of dictionary-based MRF is the explosive growth of the dictionary as a function of the number of reconstructed parameters, an instance of the curse of dimensionality, which determines an explosion of resource requirements. In this work, we describe a deep learning approach for MRF parameter map reconstruction using a fully connected architecture. Employing simulations, we have investigated how the performance of the Neural Networks (NN) approach scales with the number of parameters to be retrieved, compared to the standard dictionary approach. We have also studied optimal training procedures by comparing different strategies for noise addition and parameter space sampling, to achieve better accuracy and robustness to noise. Four MRF sequences were considered: IR-FISP, bSSFP, IR-FISP-$B_1$, and IR-bSSFP-$B_1$. A comparison between NN and the dictionary approaches in reconstructing parameter maps as a function of the number of parameters to be retrieved was performed using a numerical brain phantom. Results demonstrated that training with random sampling and different levels of noise variance yielded the best performance. NN performance was at least as good as the dictionary-based approach in reconstructing parameter maps using Gaussian noise as a source of artifacts: the difference in performance increased with the number of estimated parameters because the dictionary method suffers from the coarse resolution of the parameter space sampling. The NN proved to be more efficient in memory usage and computational burden, and has great potential for solving large-scale MRF problems.     

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.     

Significant compaction of H4 histone tail upon charge neutralization by acetylation and its mimics,possible effects on chromatin structure

The intrinsically disordered, positively charged H4 histone tail is important for chromatin structure and function. We have explored conformational ensembles of human H4 tail in solution, with varying levels of charge neutralization via acetylation or amino-acid substitutions such as $K\to Q$. We have employed an explicit water model shown recently to be well suited for simulations of intrinsically disordered proteins.Upon progressive neutralization of the H4, its radius of gyration decreases linearly with the tail charge q, the trend is explained using a simple polymer model. While the wild type state ($q=+8$) is essentially a random coil, hyper-acetylated H4 ($q=+3$) is virtually as compact and stable as a globular protein of the same number of amino-acids.Conformational ensembles of acetylated H4 match the corresponding $K\to X$ substitutions only approximately: based on the ensemble similarity, we propose $K\to M$ as a possible alternative to the commonly used $K\to Q$.Possible effects of the H4 tail compaction on chromatin structure are discussed within a qualitative model in which the chromatin is highly heterogeneous, easily inter-converting between various structural forms. We predict that upon progressive charge neutralization of the H4 tail, the least compact sub-states of chromatin de-condense first, followed by de-condensation of more compact structures, e.g. those that harbor a high fraction of stacked di-nucleosomes. The predicted hierarchy of DNA accessibility increase upon progressive acetylation of H4 might be utilized by the cell for selective DNA accessibility control.