A fast Total Variation-based iterative algorithm for digital breast tomosynthesis image reconstruction:

In this work, we propose a fast iterative algorithm for the reconstruction of digital breast tomosynthesis images. The algorithm solves a regularization problem, expressed as the minimization of the sum of a least-squares term and a weighted smoothed version of the Total Variation regularization function. We use a Fixed Point method for the solution of the minimization problem, requiring the solution of a linear system at each iteration, whose coefficient matrix is a positive definite approximation of the Hessian of the objective function. We propose an efficient implementation of the algorithm, where the linear system is solved by a truncated Conjugate Gradient method. We compare the Fixed Point implementation with a fast first order method such as the Scaled Gradient Projection method, that does not require any linear system solution. Numerical experiments on a breast phantom widely used in tomographic simulations show that both the methods recover microcalcifications very fast while the Fixed Point is more efficient in detecting masses, when more time is available for the algorithm execution

A green prospective for learned post-processing in sparse-view tomographic reconstruction

Deep Learning is developing interesting tools that are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green AI literature, we propose a shallow neural network to perform efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results show that the proposed inexpensive network computes images of comparable (or even higher) quality in about one-fourth of time and is more robust than the widely used and very deep ResUNet for tomographic reconstructions from sparse-view protocols

Platelet Redox Imbalance in Hypercholesterolemia: A Big Problem for a Small Cell

The imbalance between reactive oxygen species (ROS) synthesis and their scavenging by anti-oxidant defences is the common soil of many disorders, including hypercholesterolemia. Platelets, the smallest blood cells, are deeply involved in the pathophysiology of occlusive arterial thrombi associated with myocardial infarction and stroke. A great deal of evidence shows that both increased intraplatelet ROS synthesis and impaired ROS neutralization are implicated in the thrombotic process. Hypercholesterolemia is recognized as cause of atherosclerosis, cerebro- and cardiovascular disease, and, closely related to this, is the widespread acceptance that it strongly contributes to platelet hyperreactivity via direct oxidized LDL (oxLDL)-platelet membrane interaction via scavenger receptors such as CD36 and signaling pathways including Src family kinases (SFK), mitogen-activated protein kinases (MAPK), and nicotinamide adenine dinucleotide phosphate (NADPH) oxidase. In turn, activated platelets contribute to oxLDL generation, which ends up propagating platelet activation and thrombus formation through a mechanism mediated by oxidative stress. When evaluating the effect of lipid-lowering therapies on thrombogenesis, a large body of evidence shows that the effects of statins and proprotein convertase subtilisin/kexin type 9 inhibitors are not limited to the reduction of LDL-C but also to the down-regulation of platelet reactivity mainly by mechanisms sensitive to intracellular redox balance. In this review, we will focus on the role of oxidative stress-related mechanisms as a cause of platelet hyperreactivity and the pathophysiological link of the pleiotropism of lipid-lowering agents to the beneficial effects on platelet function

GPU acceleration of a model-based iterative method for Digital Breast Tomosynthesis

Digital Breast Tomosynthesis (DBT) is a modern 3D Computed Tomography X-ray technique for the early detection of breast tumors, which is receiving growing interest in the medical and scientific community. Since DBT performs incomplete sampling of data, the image reconstruction approaches based on iterative methods are preferable to the classical analytic techniques, such as the Filtered Back Projection algorithm, providing fewer artifacts. In this work, we consider a Model-Based Iterative Reconstruction (MBIR) method well suited to describe the DBT data acquisition process and to include prior information on the reconstructed image. We propose a gradient-based solver named Scaled Gradient Projection (SGP) for the solution of the constrained optimization problem arising in the considered MBIR method. Even if the SGP algorithm exhibits fast convergence, the time required on a serial computer for the reconstruction of a real DBT data set is too long for the clinical needs. In this paper we propose a parallel SGP version designed to perform the most expensive computations of each iteration on Graphics Processing Unit (GPU). We apply the proposed parallel approach on three different GPU boards, with computational performance comparable with that of the boards usually installed in commercial DBT systems. The numerical results show that the proposed GPU-based MBIR method provides accurate reconstructions in a time suitable for clinical trials

A neuronal network of mitochondrial dynamics regulates metastasis.

The role of mitochondria in cancer is controversial. Using a genome-wide shRNA screen, we now show that tumours reprogram a network of mitochondrial dynamics operative in neurons, including syntaphilin (SNPH), kinesin KIF5B and GTPase Miro1/2 to localize mitochondria to the cortical cytoskeleton and power the membrane machinery of cell movements. When expressed in tumours, SNPH inhibits the speed and distance travelled by individual mitochondria, suppresses organelle dynamics, and blocks chemotaxis and metastasis, in vivo. Tumour progression in humans is associated with downregulation or loss of SNPH, which correlates with shortened patient survival, increased mitochondrial trafficking to the cortical cytoskeleton, greater membrane dynamics and heightened cell invasion. Therefore, a SNPH network regulates metastatic competence and may provide a therapeutic target in cancer

A 2-year point-prevalence surveillance of healthcare-associated infections and antimicrobial use in Ferrara University Hospital, Italy

Background: Healthcare-Associated Infections (HAIs) represent one of the leading issues to patient safety as well as a significant economic burden. Similarly, Antimicrobial Use (AMU) and Resistance (AMR) represent a growing threat to global public health and the sustainability of healthcare services. Methods: A Point Prevalence Survey (PPS) following the 2016 ECDC protocol for HAI prevalence and AMU was conducted at Ferrara University Hospital (FUH). Data were collected by a team of trained independent surveyors in 2016 and 2018. Risk factors independently associated with HAI were assessed by a multivariate logistic regression model. Results: Of the 1102 patients surveyed, 115 (10.4%) had an active HAI and 487 (44.2%) were on at least 1 systemic antimicrobial agent. Factors independently associated with increased HAI risk were a "Rapidly Fatal" McCabe score (expected fatal outcome within 1 year), presence of medical devices (PVC, CVC, indwelling urinary catheter or mechanically assisted ventilation) and a length of hospital stay of at least 1 week. The most frequent types of HAI were pneumonia, bloodstream infections, and urinary tract infections. Antimicrobial resistance to third-generation cephalosporins was observed in about 60% of Enterobacteriaceae. Conclusions: The survey reports a high prevalence of HAI and AMU in FUH. Repeated PPSs are useful to control HAIs and AMU in large acute-care hospitals, highlighting the main problematic factors and allowing planning for improvement actions

A computational model of induced pluripotent stem-cell derived cardiomyocytes incorporating experimental variability from multiple data sources

KEY POINTS: Induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) capture patient-specific genotype-phenotype relationships, as well as cell-to-cell variability of cardiac electrical activity Computational modelling and simulation provide a high throughput approach to reconcile multiple datasets describing physiological variability, and also identify vulnerable parameter regimes We have developed a whole-cell model of iPSC-CMs, composed of single exponential voltage-dependent gating variable rate constants, parameterized to fit experimental iPSC-CM outputs We have utilized experimental data across multiple laboratories to model experimental variability and investigate subcellular phenotypic mechanisms in iPSC-CMs This framework links molecular mechanisms to cellular-level outputs by revealing unique subsets of model parameters linked to known iPSC-CM phenotypes ABSTRACT: There is a profound need to develop a strategy for predicting patient-to-patient vulnerability in the emergence of cardiac arrhythmia. A promising in vitro method to address patient-specific proclivity to cardiac disease utilizes induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). A major strength of this approach is that iPSC-CMs contain donor genetic information and therefore capture patient-specific genotype-phenotype relationships. A cited detriment of iPSC-CMs is the cell-to-cell variability observed in electrical activity. We postulated, however, that cell-to-cell variability may constitute a strength when appropriately utilized in a computational framework to build cell populations that can be employed to identify phenotypic mechanisms and pinpoint key sensitive parameters. Thus, we have exploited variation in experimental data across multiple laboratories to develop a computational framework for investigating subcellular phenotypic mechanisms. We have developed a whole-cell model of iPSC-CMs composed of simple model components comprising ion channel models with single exponential voltage-dependent gating variable rate constants, parameterized to fit experimental iPSC-CM data for all major ionic currents. By optimizing ionic current model parameters to multiple experimental datasets, we incorporate experimentally-observed variability in the ionic currents. The resulting population of cellular models predicts robust inter-subject variability in iPSC-CMs. This approach links molecular mechanisms to known cellular-level iPSC-CM phenotypes, as shown by comparing immature and mature subpopulations of models to analyse the contributing factors underlying each phenotype. In the future, the presented models can be readily expanded to include genetic mutations and pharmacological interventions for studying the mechanisms of rare events, such as arrhythmia triggers.S