About the book
Amyotrophic lateral sclerosis (ALS), often termed Lou Gehrig's disease, is a progressive, paralytic disorder characterized by degeneration of motor neurons in the brain and spinal cord, with the eventual involvement of most muscles, including the diaphragm. Death due to respiratory paralysis occurs within three to five years. Often more than a year ensues prior to accurate diagnosis, with delays in treatment having long-term neurological sequelae. Complicating diagnosis is its presentation with such non-specific symptoms as limb weakness, fasciculations, and fatigue, which is mimicked by other motor neuron diseases like primary lateral sclerosis and spinal and bulbar muscular atrophy. New tools and methods are urgently needed to distinguish ALS from mimicks, to assess disease progression, and to guide therapeutic development. These needs have prompted intense research into the identification of biomarkers that can accurately guide diagnosis and therapy, especially in its early stages. Current candidates relate to the three major categories of pathophysiological processes now known to be affected by ALS, cytoskeletal dynamics, protein processing, trafficking, and homeostasis, and inflammatory mechanisms. Spanning the spectrum from protein panels to epigenetics and risk alleles, and including novel neuroimaging approaches that incorporate machine learning and large data analysis, e.g., deep learning convolutional neural nets, these tools promise to identify ALS earlier and to track disease progression and therapeutic efficacy with greater fidelity for improved medical management.
This project will aim to explore the current state of diagnostic advance for ALS, with an eye toward prognosis, therapeutic intervention and medical management.