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Increasing Progenitor Cell Proliferation in the Sub-Ventricular Zone: A Therapeutic Treatment for Progressive Multiple Sclerosis?Introduction: The purpose of this study was to determine if pharmacological treatmentcould increase progenitor cell proliferation in the Sub-ventricular Zone of aged rats. Previous workhad shown that increasing progenitor cell proliferation in this region correlated well (R2=0.78; p=0.0007) with functional recovery in a damaged corpus callosum (white matter tract), suggesting thatprogenitor cell proliferation results in oligodendrocytes in this region. Methods: 10 month old male and female Sprague Dawley rats were fed the drugs for 30 days in cookiedough, then immunocytochemistry was performed on coronal brain sections, using Ki67 labeling todetermine progenitor cell proliferation. Results: Female rats showed low endogenous (control) progenitor cell proliferation, significantly differentfrom male rats (P<0.0001), at this age. Ascorbic Acid (20 mg/kg, daily for 30 days) increasedprogenitor cell proliferation overall, but maintained the innate gender difference in stem cell proliferation(P=0.001). Prozac (5 mg/kg, daily for 30 days) increased progenitor cell proliferation for femalesbut decreased stem cell proliferation for males, again showing a gender difference (P<0.0001).Simvastatin (1 mg/kg for 30 days) also increased progenitor cell proliferation in females and decreasedprogenitor cell proliferation in males, leading to a significant gender difference. Discussion: The three drug combinations (fluoxetine, simvastatin, and ascorbic acid, patent #9,254,281) led to ~ 4 fold increasemore »
Using Machine Learning to Train a Wearable Device for Measuring Students’ Cognitive Load during Problem-Solving Activities Based on Electrodermal Activity, Body Temperature, and Heart Rate: Development of a Cognitive Load Tracker for Both Personal and Classroom UseAutomated tracking of physical fitness has sparked a health revolution by allowing individuals to track their own physical activity and health in real time. This concept is beginning to be applied to tracking of cognitive load. It is well known that activity in the brain can be measured through changes in the body’s physiology, but current real-time measures tend to be unimodal and invasive. We therefore propose the concept of a wearable educational fitness (EduFit) tracker. We use machine learning with physiological data to understand how to develop a wearable device that tracks cognitive load accurately in real time. In an initial study, we found that body temperature, skin conductance, and heart rate were able to distinguish between (i) a problem solving activity (high cognitive load), (ii) a leisure activity (moderate cognitive load), and (iii) daydreaming (low cognitive load) with high accuracy in the test dataset. In a second study, we found that these physiological features can be used to predict accurately user-reported mental focus in the test dataset, even when relatively small numbers of training data were used. We explain how these findings inform the development and implementation of a wearable device for temporal tracking and logging a user’smore »