Sparse classification with MRI based markers for neuromuscular disease categorization

International audienceIn this paper, we present a novel method for disease classification between two patient populations based on features extracted from Magnetic Resonance Imaging (MRI) data. Anatomically meaningful features are extracted from structural data (T1- and T2-weighted MR images) and Diffusion Tensor Imaging (DTI) data, and used to train a new machine learning algorithm, the k-support SVM (ksup-SVM). The k-support regularized SVM has an inherent feature selection property, and thus it eliminates the requirement for a separate feature selection step. Our dataset consists of patients that suffer from facioscapulohumeral muscular dystrophy (FSH) and Myotonic muscular dystrophy type 1 (DM1) and our proposed method achieves a high performance. More specifically, it achieves a mean Area Under the Curve (AUC) of 0.7141 and mean accuracy 77% ± 0.013. Moreover, we provide a sparsity visualization of the features in order to indentify their discriminative value. The results suggest the potential of the combined use of MR markers to diagnose myopathies, and the general utility of the ksup-SVM. Source code is also available at https://gitorious.org/ksup-svm

Profile of genetic disorders prevalent in northeast region of Cairo, Egypt

As clinical geneticists, we recently reviewed our 43 years experience in an attempt to represent the frequency of genetic disorders in the Division of Genetics at Pediatric Hospital, Faculty of Medicine, Ain-Shams University, Cairo, Egypt, during the period from 1966 to 2009. All patients (from birth up to 18 years) suspected of having a genetic disorder were referred to the Genetics Clinic in the same hospital. 28,689 Patients were proved to have genetic disorders after full investigations among 660,280 children attending the Pediatrics Hospital which constituted 4.35% or 43.5/1000. Neurologic disorders were the most common (31.38%) followed by hematologic disorders (18.48%), chromosomal abnormalities (11.51%), fetal, neonatal and infant deaths (6.56%), special senses (5.82%), inborn errors of metabolism (4.24%), endocrine disorders (3.87%), skeletal disorders (3.17%), genito-gonadal anomalies (3.10%), neuromuscular disorders (2.86%), syndromes (2.08%), genodermatoses (1.92%), cardiac disorders (1.47%), gastrointestinal tract anomalies (1.37%), renal anomalies (0.26%), connective tissue disorders (0.26%), respiratory defects (0.22%), vascular anomalies (0.21%), and immunologic disorders were the least common (0.19%).Keywords: Genetic disorders; Congenital malformations; Inbreeding profile; Northeast region; Cairo; Egyp

Central and Peripheral Nervous System Complications of Vasculitis Syndromes From Pathology to Bedside: Part 1—Central Nervous System

Purpose of Review: The aim of this review is to provide a comprehensive update on the clinical assessment, diagnosis, complications, and treatment of primary central nervous system vasculitis (PCNSV). Recent Findings: The developments in neuroimaging, molecular testing, and cerebral biopsy have enhanced clinical assessment and decision making, providing novel insights to prevent misdiagnosis increasing diagnostic certainty. Advances in imaging techniques visualizing the wall of intracranial vessels have improved the possibility to distinguish inflammatory from non-inflammatory vascular lesions. Large recent studies have revealed a more varied histopathological pictures and disclosed an association with amyloid angiopathy. Unfortunately, therapy remains largely empiric. Summary: PCNSV is a heterogeneous group of disorders encompassing different clinical subsets that may differ in terms of prognosis and therapy. Recent evidence has described a more benign course, with good response to therapy. New diagnostic techniques will play soon a pivotal role in the appropriate diagnosis and prompt management of PCNSV

Characterization and modelling of complex motion patterns

Movement analysis is the principle of any interaction with the world and the survival of living beings completely depends on the effciency of such analysis. Visual systems have remarkably developed eficient mechanisms that analyze motion at different levels, allowing to recognize objects in dynamical and cluttered environments. In artificial vision, there exist a wide spectrum of applications for which the study of complex movements is crucial to recover salient information. Yet each domain may be different in terms of scenarios, complexity and relationships, a common denominator is that all of them require a dynamic understanding that captures the relevant information. Overall, current strategies are highly dependent on the appearance characterization and usually they are restricted to controlled scenarios. This thesis proposes a computational framework that is inspired in known motion perception mechanisms and structured as a set of modules. Each module is in due turn composed of a set of computational strategies that provide qualitative and quantitative descriptions of the dynamic associated to a particular movement. Diverse applications were herein considered and an extensive validation was performed for each of them. Each of the proposed strategies has shown to be reliable at capturing the dynamic patterns of different tasks, identifying, recognizing, tracking and even segmenting objects in sequences of video.Resumen. El análisis del movimiento es el principio de cualquier interacción con el mundo y la supervivencia de los seres vivos depende completamente de la eficiencia de este tipo de análisis. Los sistemas visuales notablemente han desarrollado mecanismos eficientes que analizan el movimiento en diferentes niveles, lo cual permite reconocer objetos en entornos dinámicos y saturados. En visión artificial existe un amplio espectro de aplicaciones para las cuales el estudio de los movimientos complejos es crucial para recuperar información saliente. A pesar de que cada dominio puede ser diferente en términos de los escenarios, la complejidad y las relaciones de los objetos en movimiento, un común denominador es que todos ellos requieren una comprensión dinámica para capturar información relevante. En general, las estrategias actuales son altamente dependientes de la caracterización de la apariencia y por lo general están restringidos a escenarios controlados. Esta tesis propone un marco computacional que se inspira en los mecanismos de percepción de movimiento conocidas y esta estructurado como un conjunto de módulos. Cada módulo esta a su vez compuesto por un conjunto de estrategias computacionales que proporcionan descripciones cualitativas y cuantitativas de la dinámica asociada a un movimiento particular. Diversas aplicaciones fueron consideradas en este trabajo y una extensa validación se llevó a cabo para cada uno de ellas. Cada una de las estrategias propuestas ha demostrado ser fiable en la captura de los patrones dinámicos de diferentes tareas identificando, reconociendo, siguiendo e incluso segmentando objetos en secuencias de video.Doctorad

Genetics of Cardiomyopathy

Cardiomyopathies (CMs) encompass a heterogeneous group of structural and functional (systolic and diastolic) abnormalities of the myocardium and are either confined to the cardiovascular system or are part of a systemic disorder. CMs represent a leading cause of morbidity and mortality and account for a significant percentage of death and cardiac transplantation. The 2006 American Heart Association (AHA) classification grouped CMs into primary (genetic, mixed, or acquired) or secondary (i.e., infiltrative or autoimmune). In 2008, the European Society of Cardiology classification proposed subgrouping CM into familial or genetic and nonfamilial or nongenetic forms. In 2013, the World Heart Federation recommended the MOGES nosology system, which incorporates a morpho-functional phenotype (M), organ(s) involved (O), the genetic inheritance pattern (G), an etiological annotation (E) including genetic defects or underlying disease/substrates, and the functional status (S) of a particular patient based on heart failure symptoms. Rapid advancements in the biology of cardio-genetics have revealed substantial genetic and phenotypic heterogeneity in myocardial disease. Given the variety of disciplines in the scientific and clinical fields, any desired classification may face challenges to obtaining consensus. Nonetheless, the heritable phenotype-based CM classification offers the possibility of a simple, clinically useful diagnostic scheme. In this chapter, we will describe the genetic basis of dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), arrhythmogenic cardiomyopathy (ACM), LV noncompaction cardiomyopathy (LVNC), and restrictive cardiomyopathy (RCM). Although the descriptive morphologies of these types of CM differ, an overlapping phenotype is frequently encountered within the CM types and arrhythmogenic pathology in clinical practice. CMs appear to originate secondary to disruption of “final common pathways.” These disruptions may have purely genetic causes. For example, single gene mutations result in dysfunctional protein synthesis causing downstream dysfunctional protein interactions at the level of the sarcomere and a CM phenotype. The sarcomere is a complex with multiple protein interactions, including thick myofilament proteins, thin myofilament proteins, and myosin-binding proteins. In addition, other proteins are involved in the surrounding architecture of the sarcomere such as the Z-disk and muscle LIM proteins. One or multiple genes can exhibit tissue-specific function, development, and physiologically regulated patterns of expression for each protein. Alternatively, multiple mutations in the same gene (compound heterozygosity) or in different genes (digenic heterozygosity) may lead to a phenotype that may be classic, more severe, or even overlapping with other disease forms

Quantitative Imaging of Net Axonal Transport in vivo: A Biomarker for Motor Neuron Health and Disease

Amyotrophic lateral sclerosis (ALS) is a lethal, progressive neurodegenerative disorder that selectively affects both upper and lower motor neurons, leading to muscle weakness, paralysis and death. Despite recent advances in the identification of genes associated with ALS, the quest for a sensitive biomarker for rapid and accurate diagnosis, prognosis, and treatment response monitoring has not been fulfilled. In this thesis, I report a method of quantifying the integrity of motor neurons in vivo using imaging to record uptake and retrograde transport of intramuscularly injected tetanus toxin fragment C (TTC) into spinal motor neurons. This method tracks and profiles progression of disease (transgenic SOD1G93A and PFN1 ALS mice) and detects subclinical perturbations in net transport, as analyzed in C9orf72 transgenic mice. It also defines a progressive reduction in net transport with aging. To address whether our technique enables drug development, I evaluated therapeutic benefits of (1) gene editing and (2) mutant gene silencing (with RNAi targeting SOD1) in SOD1G93A transgenic mice by characterizing their net axonal transport profiles. I constructed a computational model to evaluate key molecular processes affected in net axonal transport in ALS mouse model. The model allows prediction of key parameters affected in a C9ORF72 BAC transgenic mouse line. Prior immunization with tetanus toxoid does not preclude use of this assay, and it can be used repetitively in the same subject. This assay of net axonal transport offers broad clinical application as a diagnostic tool for motor neuron diseases and as a biomarker for rapid detection of benefit from therapies for transport dysfunction in a range of motor neuron diseases