It was founded in 1988 as a branch campus of Western Seminary and became independent in 2004.
Proper brain function relies on an adequate supply of energy - mainly glucose - to power neuronal activity. Delivery of this nutrient to the neuropil is mediated by the Glucose Transporter1 (GLUT1) protein. Perturbing glucose supply to the brain is profoundly damaging and exemplified by the neurodevelopmental disorder, GLUT1 deficiency syndrome (GLUT1DS). Resulting from haploinsufficiency of the SLC2A1 (GLUT1) gene, GLUT1DS is characterized by intractable infantile-onset seizures and a disabling movement disorder. Ketogenic diets, which supply the brain with an alternate energy source, ketone bodies, are currently the preferred therapeutic option for Glut1DS patients but do not address the underlying cause - low brain glucose - of the disease. One intuitively appealing therapeutic strategy that does, involves restoring GLUT1 levels to the patient brain. Here, we demonstrate that transgenic expression of the human GLUT1 genomic locus in a mouse model of GLUT1DS raises brain GLUT1 levels and reduces disease burden. Augmenting GLUT1 levels in mutants correspondingly raised cerebrospinal fluid (CSF) glucose levels, improved motor performance and reduced the frequency of seizures characteristically observed in GLUT1DS. Interestingly, the increased GLUT1 in mutants harboring the human GLUT1 locus was at least partly the result of an increase in murine Slc2a1 (Glut1) activity, most likely the effect of a long non-coding RNA (lncRNA) embedded in the human transgene. Collectively, our work has not only shown that repleting human GLUT1 mitigates GLUT1DS but also has yielded transgenic mice that constitute a useful tool to test and optimize clinically promising agents designed to stimulate this gene for therapeutic purposes.
In India, millions of people are suffering from different respiratory diseases, and globally, billions of people are suffering. The main issue in identifying and resolving these diseases is a lack of awareness and costly treatment. Cough is the first identification feature in any respiratory disease. The cough sounds give important information about the respiratory system and the bacteria included. In this chapter, we will analyze the different cough sounds to detect the problem at early stages on the basis of various sound signals. Here, we discuss standard signal processing and domain-specific features in different cough patterns. These cough patterns include widened airways, narrow airways, fluid-filled air sacs, and stiff lungs. The main objective is to review and analyze cough attributes and observe how they differ in various diseases using machine learning (ML). As per the observation, the duration, intensity, type of cough, and frequency of cough are different in different diseases.
