The Contribution of Small Vessel Disease to Neurodegeneration: Focus on Alzheimer’s Disease,Parkinson’s Disease and Multiple Sclerosis

Brain small vessel disease (SVD) refers to a variety of structural and functional changes affecting small arteries and micro vessels, and manifesting as white matter changes, microbleeds and lacunar infarcts. Growing evidence indicates that SVD might play a significant role in the neurobiology of central nervous system (CNS) neurodegenerative disorders, namely Alzheimer’s disease (AD) and Parkinson’s disease (PD), and neuroinflammatory diseases, such as multiple sclerosis (MS). These disorders share different pathophysiological pathways and molecular mechanisms (i.e., protein misfolding, derangement of cellular clearance systems, mitochondrial impairment and immune system activation) having neurodegeneration as biological outcome. In these diseases, the actual contribution of SVD to the clinical picture, and its impact on response to pharmacological treatments, is not known yet. Due to the high frequency of SVD in adult-aged patients, it is important to address this issue. In this review, we report preclinical and clinical data on the impact of SVD in AD, PD and MS, with the main aim of clarifying the predictability of SVD on clinical manifestations and treatment response.     

Age-Related Changes in the Matrisome of the Mouse Skeletal Muscle

Aging is characterized by a progressive decline of skeletal muscle (SM) mass and strength which may lead to sarcopenia in older persons. To date, a limited number of studies have been performed in the old SM looking at the whole, complex network of the extracellular matrix (i.e., matrisome) and its aging-associated changes. In this study, skeletal muscle proteins were isolated from whole gastrocnemius muscles of adult (12 mo.) and old (24 mo.) mice using three sequential extractions, each one analyzed by liquid chromatography with tandem mass spectrometry. Muscle sections were investigated using fluorescence- and transmission electron microscopy. This study provided the first characterization of the matrisome in the old SM demonstrating several statistically significantly increased matrisome proteins in the old vs. adult SM. Several proteomic findings were confirmed and expanded by morphological data. The current findings shed new light on the mutually cooperative interplay between cells and the extracellular environment in the aging SM. These data open the door for a better understanding of the mechanisms modulating myocellular behavior in aging (e.g., by altering mechano-sensing stimuli as well as signaling pathways) and their contribution to age-dependent muscle dysfunction.     

Saliva Proteomics as Fluid Signature of Inflammatory and Immune-Mediated Skin Diseases

Saliva is easy to access, non-invasive and a useful source of information useful for the diagnosis of serval inflammatory and immune-mediated diseases. Following the advent of genomic technologies and -omic research, studies based on saliva testing have rapidly increased and human salivary proteome has been partially characterized. As a proteomic protocol to analyze the whole saliva proteome is not currently available, the most common aim of the proteomic analysis is to discriminate between physiological and pathological conditions. The salivary proteome has been initially investigated in several diseases: oral squamous cell carcinoma and oral leukoplakia, chronic graft-versus-host disease, and Sjögren’s syndrome. Otherwise, salivary proteomics studies in the dermatological field are still in the initial phase, thus the aim of this review is to collect the best research evidence on the role of saliva proteomics analysis in immune-mediated skin diseases to understand the direction of research in this field. The results of PRISMA analysis reported herein suggest that human saliva analysis could provide significant data for the diagnosis and prognosis of several immune-mediated and inflammatory skin diseases in the next future.     

Development and characterisation of a Y2Ti2O7-based glass-ceramic as a potential oxidation protective coating for titanium suboxide (TiOx)

A silica-based glass-ceramic, with Y₂Ti₂O₇ as the major crystalline phase, is designed, characterised and tested as an oxidation-protective coating for a titanium suboxide (TiO_x) thermoelectric material at temperatures of up to 600 °C. The optimised sinter-crystallisation treatment temperatures are found to be 1300 °C and 855 °C for a duration of 30 min, and this treatment leads to a glass-ceramic with cubic Y₂Ti₂O₇ and CaAl₂Si₂O₈ as crystalline phases. An increase of ~270 °C in the dilatometric softening temperature is observed after devitrification of the parent glass, thus further extending its working temperature range.Excellent adhesion of the glass-ceramic coating to the thermoelectric material is maintained after exposure to a temperature of 600 °C for 120 h under oxidising conditions, thus confirming the effectiveness of the T1 glass-ceramic in protecting the TiO_x material.     

A Matrix-Based Approach to the Image Moment Problem

An image can be seen as an element of a vector space and hence it can be expressed in as a linear combination of the elements of any non necessarily orthogonal basis of this space. After giving a matrix formulation of this well-known fact, this paper presents a reconstruction method of an image from its moments that sheds new light on this inverse problem. Two main contributions are presented: (a) the results using the standard approach based on the least squares approximation of the result using orthogonal polynomials can also be obtained using matrix pseudoinverses, which implies higher control on the numerical stability of the problem; and (b) it is possible to use basis functions in the reconstruction different from orthogonal polynomials, such as Fourier or Haar basis, allowing to introduce constraints relative to the bandwidth or the spatial resolution on the image to be reconstructed. Judit Martònez received the B.Sc. degree in 1993 and the PhD degree (with honors) in 1998, both in telecommunications engineering from the Technical University of Catalonia. She developed her research at the Institut de Robútica i Informütica Industrial of the Spanish High Council for Scientific Research. In 1999 she joined the Computer Vision Center, a R&D center founded by the Autonomous University of Barcelona and the Autonomous Government of Catalonia. She has been principal researcher of several industrial and research projects related to computer vision technologies. Her research interests include industrial applications of machine vision, efficient algorithms for low-level image processing, multiresolution mathematical models, statistical clustering, pattern classification and inverse problems. Josep M. Porta received the Engineer Degree in Computer Science in 1994 and the Ph.D. in Artificial Intelligence in 2001, both from the Technical University of Catalonia (UPC). After that, he joined the IAS group of the University of Amsterdam and currently, he holds a post-doc position at the Institut de Robútica i Informütica Industrial (CSIC-UPC) in Barcelona. He carried research in legged robots, machine learning, vision-based methods for autonomous robot localization, and computational kinematics. Federico Thomas is Research Professor at the Spanish Scientific Research Council (CSIC) and director of the Institut de Robútica i Informütica Industrial (CSIC-UPC), Barcelona, Spain. He received the telecommunications engineering degree in 1984, and the Ph.D. degree (with honors) in computer science in 1988, both from the Technical University of Catalonia (UPC). In 1991, he won a NATO postdoctoral scholarship at the University of Massachusetts with the late Prof. Robin Popplestone. In 1999, he was visiting professor, sponsored by the Autonomous Government of Catalonia, at the Oxford University Computing Laboratory with Prof. Stephen Cameron. He has been project leader of several national projects financed by the Spanish Committee for Science and Technology (CICYT), and by local companies such as ENHER, a power generation company now part of ENDESA. His current research interests are in Geometry and Kinematics with applications to Robotics, Computer Graphics and Computer Vision. Prof. Thomas is an Associate Editor of the IEEE Transactions on Robotics.     

Probabilistic methods for eigenvalue estimates

Some probabilistic techniques are discussed regarding their use in estimating the principal eigenvalue of an elliptic operator. Such estimates are useful when studying models for biochemical reactions governed by lateral diffusion.In this paper we summarize recent results on some mathematical techniques that were developed to study certain biological models.     

Phase retrieval in digital speckle pattern interferometry by use of a smoothed space-frequency distribution

We evaluate the use of a smoothed space-frequency distribution (SSFD) to retrieve optical phase maps in digital speckle pattern interferometry (DSPI). The performance of this method is tested by use of computer-simulated DSPI fringes. Phase gradients are found along a pixel path from a single DSPI image, and the phase map is finally determined by integration. This technique does not need the application of a phase unwrapping algorithm or the introduction of carrier fringes in the interferometer. It is shown that a Wigner-Ville distribution with a smoothing Gaussian kernel gives more-accurate results than methods based on the continuous wavelet transform. We also discuss the influence of filtering on smoothing of the DSPI fringes and some additional limitations that emerge when this technique is applied. The performance of the SSFD method for processing experimental data is then illustrated.