Medicine and Health Sciences Commons^™

Evaluation Of Tumor Necrosis Factor Alpha In Sleep-Deprived Menopausal- Induced Rats And The Impact On Bone Health, Nicole Ellsworth, Dwight Curry Iii, Cj Deleon, Frank Frisch

Student Scholar Symposium Abstracts and Posters

Post-menopausal osteoporosis as a consequence of estrogen depletion is a growing concern for women in the United States. As more women take on executive positions and experience sleep deprivation, there is the potential for up regulation of pro-inflammatory cytokines, such as tumor necrosis factor alpha. It follows that the homeostatic imbalance of osteoclastic and osteoblastic activity leads to a greater risk of disease. Bisphosphonates generally, and Zolendronate specifically works by decreasing the number of osteoclasts. This current study investigated the impact of Zolendronate on the concentrations of tumor necrosis factor alpha-type (TNFɑ) in 32 ovariectomized Wistar rats. Throughout a five …

Improving Medication Information Presentation Through Interactive Visualization In Mobile Apps: Human Factors Design, Don Roosan, Yan Li, Anandi Law, Huy Truong, Mazharul Karim, Jay Chok, Moom Roosan

Pharmacy Faculty Articles and Research

Background: Despite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications.

Objective: The …

Establishing Computational Approaches Towards Identifying Malarial Allosteric Modulators: A Case Study Of Plasmodium Falciparum Hsp70s, Arnold Amusengeri, Lindy Astl, Kevin Lobb, Gennady M. Verkhivker, Özlem Tastan Bishop

Mathematics, Physics, and Computer Science Faculty Articles and Research

Combating malaria is almost a never-ending battle, as Plasmodium parasites develop resistance to the drugs used against them, as observed recently in artemisinin-based combination therapies. The main concern now is if the resistant parasite strains spread from Southeast Asia to Africa, the continent hosting most malaria cases. To prevent catastrophic results, we need to find non-conventional approaches. Allosteric drug targeting sites and modulators might be a new hope for malarial treatments. Heat shock proteins (HSPs) are potential malarial drug targets and have complex allosteric control mechanisms. Yet, studies on designing allosteric modulators against them are limited. Here, we identified allosteric …

Coccidioidomycosis: Medical And Spatio-Temporal Perspectives, Nikias Sarafoglou, Rafael Laniado-Laborin, Menas Kafatos

Mathematics, Physics, and Computer Science Faculty Articles and Research

Coccidioidomycosis (CM) is a disease of major public health importance due to the challenges in its diagnosis and treatment. To understand CM requires the attributes of a multidisciplinary network analysis to appreciate the complexity of the medical, the environmental and the social issues involved: public health, public policy, geology, atmospheric science, agronomy, social sciences and finally humanities, all which provide insight into this population transformation.

In section 1 of this paper, we describe the CM-epidemiology, the clinical features, the diagnosis and finally the treatment.

In section 2, we highlight the most important contributions and controversies in the history of the …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to …

Using Green Emitting Ph-Responsive Nanogels To Report Environmental Changes Within Hydrogels: A Nanoprobe For Versatile Sensing, Mingning Zhu, Dongdong Lu, Shanglin Wu, Qing Lian, Wenkai Wang, L. Andrew Lyon, Weiguang Wang, Paulo Bártolo, Brian R. Saunders

Engineering Faculty Articles and Research

Remotely reporting the local environment within hydrogels using inexpensive laboratory techniques has excellent potential to improve our understanding of the nanometer-scale changes that cause macroscopic swelling or deswelling. Whilst photoluminescence (PL) spectroscopy is a popular method for such studies this approach commonly requires bespoke and time-consuming synthesis to attach fluorophores which may leave toxic residues. A promising and more versatile alternative is to use a pre-formed nanogel probe that contains a donor/acceptor pair and then “dope” that into the gel during gel assembly. Here, we introduce green-emitting methacrylic acid-based nanogel probe particles and use them to report the local environment …

A Virtual Reality System For Practicing Conversation Skills For Children With Autism, Natalia Stewart Rosenfield, Kathleen Lamkin, Jennifer Re, Kendra Day, Louanne E. Boyd, Erik J. Linstead

Engineering Faculty Articles and Research

We describe a virtual reality environment, Bob’s Fish Shop, which provides a system where users diagnosed with Autism Spectrum Disorder (ASD) can practice social interactions in a safe and controlled environment. A case study is presented which suggests such an environment can provide the opportunity for users to build the skills necessary to carry out a conversation without the fear of negative social consequences present in the physical world. Through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding.

Ecological Determinants Of Respiratory Health: Examining Associations Between Asthma Emergency Department Visits, Diesel Particulate Matter, And Public Parks And Open Space In Los Angeles, California, Jason A. Douglas, Reginald S. Archer, Serena E. Alexander

Health Sciences and Kinesiology Faculty Articles

Los Angeles County (LAC) low-income communities of color experience uneven asthma rates, evidenced by asthma emergency department visits (AEDV). This has partly been attributed to inequitable exposure to diesel particulate matter (DPM). Promisingly, public parks and open space (PPOS) contribute to DPM mitigation. However, low-income communities of color with limited access to PPOS may be deprived of associated public health benefits. Therefore, this novel study investigates the AEDV, DPM, PPOS nexus to address this public health dilemma and inform public policy in at-risk communities. Optimized Hotspot Analysis was used to examine geographic clustering of AEDVs, DPM, and PPOS at the …

Purification And Characterization Of A Nonspecific Lipid Transfer Protein 1 (Nsltp1) From Ajwain (Trachyspermum Ammi) Seeds, Meshal Nazeer, Humera Waheed, Maria Saeed, Saman Yousuf Ali, M. Iqbal Choudhary, Zaheer Ul-Haq, Aftab Ahmed

Pharmacy Faculty Articles and Research

Ajwain (Trachyspermum ammi) belongs to the family Umbelliferae, is commonly used in traditional, and folk medicine due to its carminative, stimulant, antiseptic, diuretic, antihypertensive, and hepatoprotective activities. Non-specific lipid transfer proteins (nsLTPs) reported from various plants are known to be involved in transferring lipids between membranes and in plants defense response. Here, we describe the complete primary structure of a monomeric non-specific lipid transfer protein 1 (nsLTP1), with molecular weight of 9.66 kDa, from ajwain seeds. The nsLTP1 has been purified by combination of chromatographic techniques, and further characterized by mass spectrometry, and Edman degradation. The ajwain nsLTP1 …

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …

Allosteric Mechanism Of The Circadian Protein Vivid Resolved Through Markov State Model And Machine Learning Analysis, Hongyu Zhou, Zheng Dong, Gennady M. Verkhivker, Brian D. Zoltowski, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The fungal circadian clock photoreceptor Vivid (VVD) contains a photosensitive allosteric light, oxygen, voltage (LOV) domain that undergoes a large N-terminal conformational change. The mechanism by which a blue-light driven covalent bond formation leads to a global conformational change remains unclear, which hinders the further development of VVD as an optogenetic tool. We answered this question through a novel computational platform integrating Markov state models, machine learning methods, and newly developed community analysis algorithms. Applying this new integrative approach, we provided a quantitative evaluation of the contribution from the covalent bond to the protein global conformational change, and proposed an …

Synthesis, Biological Evaluation And Molecular Modeling Studies Of Novel Chromone/Aza-Chromone Fused Α-Aminophosphonates As Src Kinase Inhibitors, S. Bapat, N. Viswanadh, M. Mujahid, Amir Nasrolahi Shirazi, Rakesh Tiwari, Keykavous Parang, M. Karthikeyan, M. Muthukrishnan, Renu Vyas

Pharmacy Faculty Articles and Research

A series of novel chromone/aza-chromone fused α-aminophosphonate derivatives were synthesized in good yields using silica chloride as the catalyst. All the synthesized compounds were tested for their c-Src kinase inhibitory activity. Aza-chromone compound showed Src kinase inhibition with an IC50 value of 15.8 µM. The compounds were subjected to molecular docking and dynamics simulations to study the atomic level interactions with an unphosphorylated proto-oncogenic tyrosine protein kinase Src (PDB code 1Y57) as well as phosphorylated tyrosine protein kinase Src (PDB code 2H8H). Docking and molecular dynamic results revealed phosphorylated Src tyrosine kinase protein better results than unphosphorylated tyrosine Src kinase …