Medicine and Health Sciences Commons^™

Reliability Of Accelerometer-Based Reaction Time Tests, Jacob Hepp, Warner Rhodes, Jordan Walton, Rahul Soangra, Brent Harper

Student Scholar Symposium Abstracts and Posters

Concussions are traumatic brain injuries that affect the function of the brain. One of the primary symptoms of a concussion is a lack of reaction time. The people that are most susceptible to concussions are athletes; Laker’s (2011) study found that 135,000 patients that suffer concussions from playing sports are expected to be hospitalized each year, with football making up 75% of concussions at high school and college levels. Honda et al. (2018) suggested reaction time as an important biomarker of concussion. Laboratory camera-based motion capture data, while reliable, is not a realistic tool to use outside of a laboratory …

Macrodosing To Microdosing With Psychedelics: Clinical, Social, And Cultural Perspectives, Ayse Ceren Kaypak, Amir Raz

Psychology Faculty Articles and Research

To date, the clinical and scientific literature has best documented the effects of classical psychedelics, such as lysergic acid diethylamide (LSD), psilocybin, and dimethyltryptamine (DMT), in typical quantities most often associated with macrodosing. More recently, however, microdosing with psychedelics has emerged as a social trend and nascent therapeutic intervention. This variation in psychedelic practice refers to repeat, intermittent ingestion of less-than-macrodose amounts that do not cause the effects associated with full-blown “trips”. Microdosing paves the road to incorporating psychedelic drugs into a daily routine while maintaining, or even improving, cognitive and mental function. Unlike macrodosing with psychedelics, the influence of …

Assessing The Reidentification Risks Posed By Deep Learning Algorithms Applied To Ecg Data, Arin Ghazarian, Jianwei Zheng, Daniele Struppa, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

ECG (Electrocardiogram) data analysis is one of the most widely used and important tools in cardiology diagnostics. In recent years the development of advanced deep learning techniques and GPU hardware have made it possible to train neural network models that attain exceptionally high levels of accuracy in complex tasks such as heart disease diagnoses and treatments. We investigate the use of ECGs as biometrics in human identification systems by implementing state-of-the-art deep learning models. We train convolutional neural network models on approximately 81k patients from the US, Germany and China. Currently, this is the largest research project on ECG identification. …

Cytotoxic Activity Of Non-Specific Lipid Transfer Protein (Nsltp1) From Ajwain (Trachyspermum Ammi) Seeds, Saud O. Alshammari, Taibah Aldakhil, Qamar A. Alshammari, David Salehi, Aftab Ahmed

Pharmacy Faculty Articles and Research

Background

Trachyspermum ammi, commonly known as Ajwain, is a member of the Apiaceae family. It is a therapeutic herbal spice with diverse pharmacological properties, used in traditional medicine for various ailments. However, all previous studies were conducted using small molecule extracts, leaving the protein’s bioactivity undiscovered.

Aim

The current study aimed to demonstrate the cytotoxic activity of Ajwain non-specific lipid transfer protein (nsLTP1) in normal breast (MCF10A), breast cancer (MCF-7), and pancreatic cancer (AsPC-1) cell lines. Also, to evaluate its structural stability in human serum as well as at high temperature conditions.

Methods

The cytotoxic activity of Ajwain nsLTP1 was …

A High Precision Machine Learning-Enabled System For Predicting Idiopathic Ventricular Arrhythmia Origins, Jianwei Zheng, Guohua Fu, Daniele Struppa, Islam Abudayyeh, Tahmeed Contractor, Kyle Anderson, Huimin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Background: Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy.

Method: A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, …

Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead

Engineering Faculty Articles and Research

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of …