Hepatocellular carcinoma in adult thalassemia patients: an expert opinion based on current evidence

Beta-thalassemia represents a heterogeneous group of haemoglobin inherited disorders, among the most common genetic diseases in the world, frequent in the Mediterranean basin. As beta-thalassemia patients' survival has increased over time, previously unknown complications are observed with increasing frequency. Among them, an increased risk of hepatocellular carcinoma (HCC) has been registered. Our aim is to reduce inequalities in diagnosis and treatment and to offer patients univocal recommendations in any institution. The members of the panel - gastroenterologists, radiologists, surgeons and oncologists -were selected on the basis of their publication records and expertise. Thirteen clinical questions, derived from clinical needs, and an integration of all the committee members' suggestions, were formulated. Modified Delphi approach involving a detailed literature review and the collective judgement of experts, was applied to this work. Thirteen statements were derived from expert opinions' based on the current literature, on recently developed reviews and on technological advancements. Each statement is discussed in a short paragraph reporting the current key evidence. As this is an emerging issue, the number of papers on HCC in beta-thalassemia patients is limited and based on anecdotal cases rather than on randomized controlled studies. Therefore, the panel has discussed, step by step, the possible differences between beta-thalassemia and non beta-thalassemia patients. Despite the paucity of the literature, practical and concise statements were generated. This paper offers a practical guide organized by statements describing how to manage HCC in patients with beta-thalassemia

NHERF1/EBP50 in Breast Cancer: Clinical Perspectives

Na(+)/H(+) exchanger regulatory factor 1 (NHERF1) is a postsynaptic density 95/disc-large/zona occludens (PDZ) domain-containing protein that recruits membrane receptors/transporters and cytoplasmic signaling proteins into functional complexes. NHERF1 expression has been demonstrated to be altered in breast cancer, but its role in mammary cancerogenesis and progression remains still undefined. In this paper, we review what is known on the pathological role and the potential clinical application of NHERF1 protein in breast cancer. Recent evidence shows that an increased cytoplasmic expression of NHERF1 suggests a key role of its localization/compartmentalization in defining cancerogenesis, progression, and invasion. NHERF1 overexpression is associated with increasing tumor cytohistological grade, aggressive clinical behavior, unfavorable prognosis, and increased tumor hypoxia. Moreover, NHERF1 co-localizes with the oncogenic receptor HER2/neu in HER2/neu-overexpressing carcinoma and in distant metastases. These data make NHERF1 also a potential candidate of clinical relevance for anti-HER2/neu therapy

Modelling local winds over the Salento peninsula

A three-day mesoscale numerical simulation has been performed over the narrow Salento peninsula (south-eastern Italy) during summer conditions characterised by weak synoptic forcing. These atmospheric conditions favour the development of complex sea-breeze systems and convergence zones on the peninsula. The aim of this work is to investigate the ability of an atmospheric mesoscale model to reproduce the surface fields of meteorological variables in the presence of local-scale forcing and breeze circulations, which are fundamental in applications such as air pollution modelling and nowcasting. The modelled fields have been compared with available surface measurements and sodar data. Results indicate that the model can simulate the general mean wind field in a realistic way. The diurnal evolution of the wind is well reproduced and the maximum deviations mostly occur during the night, being associated with calm conditions. Statistical analysis indicates that the typical mean bias is found to be about 1 m s−1 for hourly averaged wind speed, less than 20° for wind direction and about 1°C for temperature. The root mean square error (rmse) varies from 1 to 3 m s−1 for wind speed, from 50° to 70° for wind direction, and is about 2.4°C for temperature. All the values of the numerical indexes are within ranges which are characteristic of those found for other state-of-the-art models applied to similar cases studies. Despite a good overall agreement between predictions and observations, some discrepancies were found in the individual profiles due both to the limited spatial representation of the local details and to the complex wind field which makes the space–time matching between the model and the observations quite critical. The structures of the thermal mixed layer and the breeze convergence zone are similar to numerical studies relative to more idealised conditions. Copyright © 2004 Royal Meteorological Societ

Subspace Energy Monitoring for Anomaly Detection @Sensor or @Edge

The amount of data generated by distributed monitoring systems that can be exploited for anomaly detection, along with real time, bandwidth, and scalability requirements leads to the abandonment of centralized approaches in favor of processing closer to where data are generated. This increases the interest in algorithms coping with the limited computational resources of gateways or sensor nodes. We here propose two dual and lightweight methods for anomaly detection based on generalized spectral analysis. We monitor the signal energy laying along with the principal and anti-principal signal subspaces, and call for an anomaly when such energy changes significantly with respect to normal conditions. A streaming approach for the online estimation of the needed subspaces is also proposed. The methods are tested by applying them to synthetic data and real-world sensor readings. The synthetic setting is used for design space exploration and highlights the tradeoff between accuracy and computational cost. The real-world example deals with structural health monitoring and shows how, despite the extremely low computations costs, our methods are able to detect permanent and transient anomalies that would classically be detected by full spectral analysis

A Deep Learning Method for Optimal Undersampling Patterns and Image Recovery for MRI Exploiting Losses and Projections

Compressed Sensing was recently proposed to reduce the long acquisition time of Magnetic Resonance Imaging by undersampling the signal frequency content and then algorithmically reconstructing the original image. We propose a way to significantly improve the above method by exploiting a deep neural network to tackle both problems of frequency sub-sampling and image reconstruction simultaneously, thanks to the introduction of a new loss function to drive the training and the addition of a post-processing non-neural stage. Furthermore, we highlight how some of the quantities along the processing chain can be used as a proxy of the quality of the recovered image, thus allowing a self-assessment of the whole technique. All improvements hinge on the possibility of identifying constraints to which the final image must obey and suitably enforce them. The effectiveness of our approach is tested on real-world MRI acquisitions from the fastMRI public database and achieves an appreciable improvement in Peak Signal-to-Noise Ratio with respect to the original CS-based proposal with speed-up factors 4 and 8

Inertial Sensors in Swimming: Detection of Stroke Phases through 3D Wrist Trajectory.

Monitoring the upper arm propulsion is a crucial task for swimmer performance. The swimmer indeed can produce displacement of the body by modulating the upper limb kinematics. The present study proposes an approach for automatically recognize all stroke phases through three-dimensional (3D) wrist\u2019s trajectory estimated using inertial devices. Inertial data of 14 national-level male swimmer were collected while they performed 25 m front-crawl trial at intensity range from 75% to 100% of their 25 m maximal velocity. The 3D coordinates of the wrist were computed using the inertial sensors orientation and considering the kinematic chain of the upper arm biomechanical model. An algorithm that automatically estimates the duration of entry, pull, push, and recovery phases result from the 3D wrist\u2019s trajectory was tested using the bi-dimensional (2D) video-based systems as temporal reference system. A very large correlation (r = 0.87), low bias (0.8%), and reasonable Root Mean Square error (2.9%) for the stroke phases duration were observed using inertial devices versus 2D video-based system methods. The 95% limits of agreement (LoA) for each stroke phase duration were always lower than 7.7% of cycle duration. The mean values of entry, pull, push and recovery phases duration in percentage of the complete cycle detected using 3D wrist\u2019s trajectory using inertial devices were 34.7 (\ub1 6.8)%, 22.4 (\ub1 5.8)%, 14.2 (\ub1 4.4)%, 28.4 (\ub1 4.5)%. The swimmer\u2019s velocity and arm coordination model do not affect the performance of the algorithm in stroke phases detection. The 3D wrist trajectory can be used for an accurate and complete identification of the stroke phases in front crawl using inertial sensors. Results indicated the inertial sensor device technology as a viable option for swimming arm-stroke phase assessment

A model for the estimation of standard deviation of air pollution concentration in different stability conditions

We propose to estimate the standard deviations of the air pollution concentration in the horizontal and vertical direction, σy and σz, based on Pasquill’s well-known equation, in terms of the wind variance and the Lagrangian integral time scales, on the basis of an atmospheric turbulence spectra model. The main advantage of the spectral model is its treatment of turbulent kinetic energy spectra as the sum of buoyancy and a shear produced part, modelling each one separately. The formulation represents both shear and buoyant turbulent mechanisms characterizing the various regimes of the Planetary Boundary Layer, and gives continuous values at any elevation and all stability conditions from unstable to stable. As a consequence, both the wind variance and the Lagrangian integral time scales in the dispersion parameters are more general than those found in literature, because they are not derived from diffusion experiments as most parameterizations. Furthermore, they provide a formulation continuous for the whole boundary layer resulting more physically consistent. The σy, σz parameters, included in a Gaussian model have been tested and compared with a dispersion scheme reported in the literature, using experimental data in different emission conditions (low and tall stacks) and in several meteorological conditions ranging from stable to convective. Results show that the dispersion model with the sigmas parameterisation included, produces a good fitting of the measured ground-level concentration data in all the experimental conditions considered, performing slightly better than other state-of-art models

NLRP3 Inflammasome From Bench to Bedside: New Perspectives for Triple Negative Breast Cancer

The tumor microenvironment (TME) is crucial in cancer onset, progression and response to treatment. It is characterized by an intricate interaction of immune cells and cytokines involved in tumor development. Among these, inflammasomes are oligomeric molecular platforms and play a key role in inflammatory response and immunity. Inflammasome activation is initiated upon triggering of pattern recognition receptors (Toll-like receptors, NOD-like receptors, and Absent in melanoma like receptors), on the surface of immune cells with the recruitment of caspase-1 by an adaptor apoptosis-associated speck-like protein. This structure leads to the activation of the pro-inflammatory cytokines interleukin (IL)-1β and IL-18 and participates in different biological processes exerting its effects. To date, the Nod–Like Receptor Protein 3 (NLRP3) inflammasome has been well studied and its involvement has been established in different cancer diseases. In this review, we discuss the structure, biology and mechanisms of inflammasomes with a special focus on the specific role of NLRP3 in breast cancer (BC) and in the sub-group of triple negative BC. The NLRP3 inflammasome and its down-stream pathways could be considered novel potential tumor biomarkers and could open new frontiers in BC treatment

Low-power fixed-point compressed sensing decoder with support oracle

Approaches for reconstructing signals encoded with Compressed Sensing (CS) techniques, and based on Deep Neural Networks (DNNs) are receiving increasing interest in the literature. In a recent work, a new DNN-based method named Trained CS with Support Oracle (TCSSO) is introduced, relying the signal reconstruction on the two separate tasks of support identification and measurements decoding. The aim of this paper is to improve the TCSSO framework by considering actual implementations using a finite-precision hardware. Solutions with low memory footprint and low computation requirements by employing fixed-point notation and by reducing the number of bits employed are considered. Results using synthetic electrocardiogram (ECG) signals as a case study show that this approach, even when used in a constrained-resources scenario, still outperform current state-of-art CS approaches

Training Binary Layers by Self-Shrinking of Sigmoid Slope: Application to Fast MRI Acquisition

Deep Neural Networks (DNN) have become popular and widespread because they combine computational power and flexibility, but they may present critical hyper-parameters that need to be tuned before the model can be trained. Recently, the use of trainable binary masks in the field of Magnetic Resonance Imaging (MRI) acquisition brought new state-of-the-art results, but with the disadvantage of introducing a bulky hyper-parameter, which tuning is usually time-consuming. We present a novel callback-based method that is applied during training and turns the tuning problem into a triviality, also bringing non-negligible performance improvements. We test our method on the fastMRI dataset