Assessing brain involvement in Fabry disease with deep learning and the brain-age paradigm.

While neurological manifestations are core features of Fabry disease (FD), quantitative neuroimaging biomarkers allowing to measure brain involvement are lacking. We used deep learning and the brain-age paradigm to assess whether FD patients' brains appear older than normal and to validate brain-predicted age difference (brain-PAD) as a possible disease severity biomarker. MRI scans of FD patients and healthy controls (HCs) from a single Institution were, retrospectively, studied. The Fabry stabilization index (FASTEX) was recorded as a measure of disease severity. Using minimally preprocessed 3D T1-weighted brain scans of healthy subjects from eight publicly available sources (N = 2160; mean age = 33 years [range 4-86]), we trained a model predicting chronological age based on a DenseNet architecture and used it to generate brain-age predictions in the internal cohort. Within a linear modeling framework, brain-PAD was tested for age/sex-adjusted associations with diagnostic group (FD vs. HC), FASTEX score, and both global and voxel-level neuroimaging measures. We studied 52 FD patients (40.6 ± 12.6 years; 28F) and 58 HC (38.4 ± 13.4 years; 28F). The brain-age model achieved accurate out-of-sample performance (mean absolute error = 4.01 years, R2 = .90). FD patients had significantly higher brain-PAD than HC (estimated marginal means: 3.1 vs. -0.1, p = .01). Brain-PAD was associated with FASTEX score (B = 0.10, p = .02), brain parenchymal fraction (B = -153.50, p = .001), white matter hyperintensities load (B = 0.85, p = .01), and tissue volume reduction throughout the brain. We demonstrated that FD patients' brains appear older than normal. Brain-PAD correlates with FD-related multi-organ damage and is influenced by both global brain volume and white matter hyperintensities, offering a comprehensive biomarker of (neurological) disease severity.

Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach.

OBJECTIVES: To stratify patients with multiple sclerosis (pwMS) based on brain MRI-derived volumetric features using unsupervised machine learning. METHODS: The 3-T brain MRIs of relapsing-remitting pwMS including 3D-T1w and FLAIR-T2w sequences were retrospectively collected, along with Expanded Disability Status Scale (EDSS) scores and long-term (10 ± 2 years) clinical outcomes (EDSS, cognition, and progressive course). From the MRIs, volumes of demyelinating lesions and 116 atlas-defined gray matter regions were automatically segmented and expressed as z-scores referenced to external populations. Following feature selection, baseline MRI-derived biomarkers entered the Subtype and Stage Inference (SuStaIn) algorithm, which estimates subgroups characterized by distinct patterns of biomarker evolution and stages within subgroups. The trained model was then applied to longitudinal MRIs. Stability of subtypes and stage change over time were assessed via Krippendorf's α and multilevel linear regression models, respectively. The prognostic relevance of SuStaIn classification was assessed with ordinal/logistic regression analyses. RESULTS: We selected 425 pwMS (35.9 ± 9.9 years; F/M: 301/124), corresponding to 1129 MRI scans, along with healthy controls (N = 148; 35.9 ± 13.0 years; F/M: 77/71) and external pwMS (N = 80; 40.4 ± 11.9 years; F/M: 56/24) as reference populations. Based on 11 biomarkers surviving feature selection, two subtypes were identified, designated as "deep gray matter (DGM)-first" subtype (N = 238) and "cortex-first" subtype (N = 187) according to the atrophy pattern. Subtypes were consistent over time (α = 0.806), with significant annual stage increase (b = 0.20; p < 0.001). EDSS was associated with stage and DGM-first subtype (p ≤ 0.02). Baseline stage predicted long-term disability, transition to progressive course, and cognitive impairment (p ≤ 0.03), with the latter also associated with DGM-first subtype (p = 0.005). CONCLUSIONS: Unsupervised learning modelling of brain MRI-derived volumetric features provides a biologically reliable and prognostically meaningful stratification of pwMS. KEY POINTS: • The unsupervised modelling of brain MRI-derived volumetric features can provide a single-visit stratification of multiple sclerosis patients. • The so-obtained classification tends to be consistent over time and captures disease-related brain damage progression, supporting the biological reliability of the model. • Baseline stratification predicts long-term clinical disability, cognition, and transition to secondary progressive course.

Mild Skin Heating Evokes Warmth Hyperknesis Selectively for Histaminergic and Serotoninergic Itch in Humans.

Chronic itch can severely affect quality of life. Patients report that their chronic itch can be exacerbated by exposure to warm conditions ("warmth hyperknesis"). The aim of this mechanistic study was to investigate the effect of mild heating of the skin in humans on various experimental models of itch. A total of 18 healthy subjects were recruited to the study. Itch was provoked by histamine, serotonin, or cowhage in 3 different sessions. The provoked area was heated with an infrared lamp, and the skin temperature was either not altered, or was increased by 4°C or 7°C. Subsequent to induction of itch, the itch intensity was recorded for 10 min while the skin was heated continuously throughout the entire period of itch induction. Heating the skin resulted in a significant increase in itch intensity when provoked by histamine or serotonin. It is possible that thermoception and pruriception interact and selectively produce a higher itch intensity in histaminergic and serotoninergic itch.

Time-Domain and Monostatic-like Frequency-Domain Methods for Bistatic SAR Simulation.

In recent years, an increasing interest has been devoted to bistatic SAR configurations, which can be effectively used to improve system performance and/or to increase the amount of physical information retrievable from the observed scene. Within this context, the availability of simulation tools is of paramount importance, for both mission planning and processing algorithm verification and testing. In this paper, a time domain simulator useful to obtain the point-spread function and the raw signal for the generic bistatic SAR configuration is presented. Moreover, we focus on the case of two bistatic configurations, which are of considerable interest in actual SAR applications, i.e., the translational invariant SAR and the one-stationary SAR acquisition geometries, for which we obtain meaningful expressions of the Transfer Functions. In particular, these expressions are formally equal to those obtained for the monostatic SAR configuration, so that the already available monostatic simulator can be easily adapted to these bistatic cases. The point-target raw signals obtained using the (exact) time domain simulator and the (approximated) frequency domain one are compared, with special attention to acquisition geometries that may be of practical interest in Formation-Flying SAR applications. Results show that the phase difference between raw signals simulated with the two approaches is, in all cases, smaller (and often much smaller) than about 10 degrees, except that at the very edge of the raw signals, where however, it does not exceed about 50 degrees.

Software Tool for Soil Surface Parameters Retrieval from Fully Polarimetric Remotely Sensed SAR Data.

The retrieval of soil surface parameters, in particular soil moisture and roughness, based on Synthetic Aperture Radar (SAR) data, has been the subject of a large number of studies, of which results are available in the scientific literature. However, although refined methods based on theoretical/analytical scattering models have been proposed and successfully applied in experimental studies, at the operative level very simple, empirical models with a number of adjustable parameters are usually employed. One of the reasons for this situation is that retrieval methods based on analytical scattering models are not easy to implement and to be employed by non-expert users. Related to this, commercially and freely available software tools for the processing of SAR data, although including routines for basic manipulation of polarimetric SAR data (e.g., coherency and covariance matrix calculation, Pauli decomposition, etc.), do not implement easy-to-use methods for surface parameter retrieval. In order to try to fill this gap, in this paper we present a user-friendly computer program for the retrieval of soil surface parameters from Polarimetric Synthetic Aperture Radar (PolSAR) imageries. The program evaluates soil permittivity, soil moisture and soil roughness based on the theoretical predictions of the electromagnetic scattering provided by the Polarimetric Two-Scale Model (PTSM) and the Polarimetric Two-Scale Two-Component Model (PTSTCM). In particular, nine different retrieval methodologies, whose applicability depends on both the used polarimetric data (dual- or full-pol) and the characteristics of the observed scene (e.g., on its topography and on its vegetation cover), as well as their implementation in the Interactive Data Language (IDL) platform, are discussed. One specific example from Germany's Demmin test-site is presented in detail, in order to provide a first guide to the use of the tool. Obtained retrieval results are in agreement with what was expected according to the available literature.

Integration of Sentinel-1 and Sentinel-2 Data for Land Cover Mapping Using W-Net.

In this paper, we present a new approach to the fusion of Sentinel 1 (S1) and Sentinel 2 (S2) data for land cover mapping. The proposed solution aims at improving methods based on Sentinel 2 data, that are unusable in case of cloud cover. This goal is achieved by using S1 data to generate S2-like segmentation maps to be used to integrate S2 acquisitions forbidden by cloud cover. In particular, we propose for the first time in remote sensing a multi-temporal W-Net approach for the segmentation of Interferometric Wide swath mode (IW) Sentinel-1 data collected along ascending/descending orbit to discriminate rice, water, and bare soil. The quantitative assessment of segmentation accuracy shows an improvement of 0.18 and 0.25 in terms of accuracy and F1-score by applying the proposed multi-temporal procedure with respect to the previous single-date approach. Advantages and disadvantages of the proposed W-Net based solution have been tested in the National Park of Albufera, Valencia, and we show a performance gain in terms of the classical metrics used in segmentation tasks and the computational time.

Perturbation Theory for Scattering from Multilayers with Randomly Rough Fractal Interfaces: Remote Sensing Applications.

A general, approximate perturbation method, able to provide closed-form expressions of scattering from a layered structure with an arbitrary number of rough interfaces, has been recently developed. Such a method provides a unique tool for the characterization of radar response patterns of natural rough multilayers. In order to show that, here, for the first time in a journal paper, we describe the application of the developed perturbation theory to fractal interfaces; we then employ the perturbative method solution to analyze the scattering from real-world layered structures of practical interest in remote sensing applications. We focus on the dependence of normalized radar cross section on geometrical and physical properties of the considered scenarios, and we choose two classes of natural stratifications: wet paleosoil covered by a low-loss dry sand layer and a sea-ice layer above water with dry snow cover. Results are in accordance with the experimental evidence available in the literature for the low-loss dry sand layer, and they may provide useful indications about the actual ability of remote sensing instruments to perform sub-surface sensing for different sensor and scene parameters.

Integrated Toolset for WSN Application Planning, Development, Commissioning and Maintenance: The WSN-DPCM ARTEMIS-JU Project.

In this article we present the main results obtained in the ARTEMIS-JU WSN-DPCM project between October 2011 and September 2015. The first objective of the project was the development of an integrated toolset for Wireless sensor networks (WSN) application planning, development, commissioning and maintenance, which aims to support application domain experts, with limited WSN expertise, to efficiently develop WSN applications from planning to lifetime maintenance. The toolset is made of three main tools: one for planning, one for application development and simulation (which can include hardware nodes), and one for network commissioning and lifetime maintenance. The tools are integrated in a single platform which promotes software reuse by automatically selecting suitable library components for application synthesis and the abstraction of the underlying architecture through the use of a middleware layer. The second objective of the project was to test the effectiveness of the toolset for the development of two case studies in different domains, one for detecting the occupancy state of parking lots and one for monitoring air concentration of harmful gasses near an industrial site.

Tackling Chronic Pain and Inflammation through the Purinergic System.

The purinergic system is composed of purine and pyrimidine transmitters, the enzymes that modulate the interconversion of nucleotides and nucleosides, the membrane transporters that control their extracellular concentrations, and the many receptor subtypes that are responsible for their cellular responses. The components of this system are ubiquitously localized in all tissues and organs, and their involvement in several physiological conditions has been clearly demonstrated. Moreover, extracellular purine and pyrimidine concentrations rise several folds under pathological conditions like tissue damage, ischemia, and inflammation, which suggest that this signaling system might contribute both to disease outcome and, possibly, to its tentative resolution. The complexity of this system has greatly impaired the clear identification of the mediators and receptors that are actually involved in a given pathology, also due to the often opposite roles played by the various receptor subtypes. Nevertheless, this knowledge is fundamental for the possible exploitation of these molecular entities as targets for the development of new pharmacological approaches. In this review, we aim at highlighting what is currently known on the role of the purinergic system in various pain conditions and during inflammatory processes. Although some confusion may arise from conflicting results, literature data clearly show that targeting specific purinergic receptors may represent an innovative approach to various pain and inflammatory conditions, and that new purine-based drugs are now very close to reach the market with these indications.

Purple Corn Extract as Anti-allodynic Treatment for Trigeminal Pain: Role of Microglia.

Natural products have attracted interest in the search for new and effective analgesics and coadjuvant approaches to several types of pain. It is in fact well known that many of their active ingredients, such as anthocyanins (ACNs) and polyphenols, can exert potent anti-inflammatory actions. Nevertheless, their potential beneficial effects in orofacial painful syndromes have not been assessed yet. Here, we have evaluated the preventive effect of an ACN-enriched purple corn extract against the development of orofacial allodynia, in comparison with isogenic yellow corn extract containing only polyphenols. Orofacial allodynia developed following induction of temporomandibular joint (TMJ) inflammation in male rats, due to the injection of Complete Freund's Adjuvant (CFA), and was evaluated by von Frey filaments. Animals drank purple or yellow corn extracts or water starting from 11 days before induction of inflammation and up to the end of the experiment 3 days later. To highlight possible additive and/or synergic actions, some animals also received the anti-inflammatory drug acetyl salicylic acid (ASA). In parallel with the evaluation of allodynia, we have focused our attention on the activation of microglia cells in the central nervous system (CNS), as it is well-known that they significantly contribute to neuronal sensitization and pain. Our data demonstrate that purple corn extract is as effective as ASA in preventing the development of orofacial allodynia, and only partial additive effect is observed when the two agents are co-administered. Yellow corn exerted no effect. Multiple mechanisms are possibly involved in the action of purple corn, including reduction of trigeminal macrophage infiltration and the shift of microglia cell polarization to an anti-inflammatory phenotype. In fact, in rats receiving yellow corn or water microglia cells show thick, short cell processes typical of activated cells. Conversely, thinner and longer microglia cell processes are observed in the brainstem of animals drinking purple corn extract; shape changes are accompanied by a reduction in the expression of pro-inflammatory molecules and increased production of anti-inflammatory mediators. Administration of purple corn extracts therefore represents a possible low-cost and easy way to reduce trigeminal-associated pain in various pathological conditions also thanks to the modulation of microglia reactivity.