UC-36 Using Machine Learning Techniques to Predict RT-PCR Results for COVID-19 Patients.

With the COVID-19 pandemic still a threat, healthcare professionals and medical industries keep searching for better ways to mitigate the spread of COVID-19. While Machine Learning has been applied in many other domains, there is now a high demand for diagnosis systems that utilize Machine Learning techniques in the healthcare domain and in particular combating COVID-19. In this project, we explore the role of Machine Learning models in combating COVID-19, using WEKA as the main tool for analysis.Advisors(s): Dr. Ming Yang - IT 4983 Capstone Professor Dr. Seyedamin Pouriyeh - Project OwnerTopic(s): Data/Data AnalyticsIT 498

Urinary catecholamines and mitral valve prolapse in panic-anxiety patients

Free norepinephrine and epinephrine were measured in two consecutive 12-hour urine collections gathered during normal activity and sleep from 23 panic-anxiety patients and 9 normal subjects. Mitral value prolapse (MVP) was found in 7 of 20 patients who had echocardiograms. Mean nighttime norepinephrine and epinephrine excretion in panic-anxiety patients without MVP was significantly higher than that of control subjects, and was significantly higher than that of anxiety patients with MVP. In the daytime, all groups had higher catecholamine (CA) levels, but the differences between the groups were less pronounced. Medication significantly relieved symptoms and was associated with decreased CA levels. Elevated basal CA levels may characterize the subgroup of panic-anxiety patients who do not have MVP.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25817/1/0000380.pd

Systemic hormonal and physiological abnormalities in anxiety disorders

Among the studies of systemic hormonal and physiological abnormalities associated with anxiety disorders, the most consistent and extensive findings suggest (a) peripheral adrenergic hyperactivity (including increases in norepinephrine but not epinephrine) and functional dysregulation, (b) increased incidence of mitral valve prolapse in panic patients, and (c) normal suppressibility of the hypothalamic-pituitary-adrenal cortical endocrine system with dexamethasone in panic patients. Other less-certain findings include (a) increased circulating concentrations of plasma ACTH and/or cortisol, and prolactin, in panic patients, (b) increased platelet monoamine oxidase activity in generalized anxiety and/or panic patients, (c) decreased gonadal axis activity in some anxious individuals, (d) decreased nighttime melatonin plasma concentrations in panic patients, and (e) peripheral [alpha]2 and [beta]-adrenoreceptor down-regulation, with normal serotonin binding parameters. These findings, taken together, provide tentative support for dysfunction in adrenergic and GABAergic central nervous system mechanisms in people with anxiety disorders. Abnormal anxiety and normal stress both show evidence of adrenergic hyperactivity; however, there appear to be differences in hormonal profiles, especially the apparent lack of increase of epinephrine during panic attacks, as well as differences in the reactivity of the system, and in the "trigger" mechanisms which determine when the response occurs.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27526/1/0000570.pd

Cell-Specific “Competition for Calories� Drives Asymmetric Nutrient-Energy Partitioning, Obesity, and Metabolic Diseases in Human and Non-human Animals

The mammalian body is a complex physiologic “ecosystem� in which cells compete for calories (i.e., nutrient-energy). Axiomatically, cell-types with competitive advantages acquire a greater number of consumed calories, and when possible, increase in size and/or number. Thus, it is logical and parsimonious to posit that obesity is the competitive advantages of fat-cells (adipocytes) driving a disproportionate acquisition and storage of nutrient-energy. Accordingly, we introduce two conceptual frameworks. Asymmetric Nutrient-Energy Partitioning describes the context-dependent, cell-specific competition for calories that determines the partitioning of nutrient-energy to oxidation, anabolism, and/or storage; and Effective Caloric Intake which describes the number of calories available to constrain energy-intake via the inhibition of the sensorimotor appetitive cells in the liver and brain that govern ingestive behaviors. Inherent in these frameworks is the independence and dissociation of the energetic demands of metabolism and the neuro-muscular pathways that initiate ingestive behaviors and energy intake. As we demonstrate, if the sensorimotor cells suffer relative caloric deprivation via asymmetric competition from other cell-types (e.g., skeletal muscle- or fat-cells), energy-intake is increased to compensate for both real and merely apparent deficits in energy-homeostasis (i.e., true and false signals, respectively). Thus, we posit that the chronic positive energy balance (i.e., over-nutrition) that leads to obesity and metabolic diseases is engendered by apparent deficits (i.e., false signals) driven by the asymmetric inter-cellular competition for calories and concomitant differential partitioning of nutrient-energy to storage. These frameworks, in concert with our previous theoretic work, the Maternal Resources Hypothesis, provide a parsimonious and rigorous explanation for the rapid rise in the global prevalence of increased body and fat mass, and associated metabolic dysfunctions in humans and other mammals inclusive of companion, domesticated, laboratory, and feral animals

Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

In the version of this article initially published, the author affiliations incorrectly listed “Candiolo Cancer Institute FPO-IRCCS, Candiolo (TO), Italy” as “Candiolo Cancer Institute, Candiolo, Italy.” The change has been made to the HTML and PDF versions of the article

Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2)

The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Multi-ancestry genome-wide association meta-analysis of Parkinson?s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations