Medicine and Health Sciences Commons^™

Effects Of Cannabinoids On Ligand-Gated Ion Channels, Murat Oz, Keun-Hang Susan Yang, Mohamed Omer Mahgoub

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Phytocannabinoids such as Δ⁹-tetrahydrocannabinol and cannabidiol, endocannabinoids such as N-arachidonoylethanolamine (anandamide) and 2-arachidonoylglycerol, and synthetic cannabinoids such as CP47,497 and JWH-018 constitute major groups of structurally diverse cannabinoids. Along with these cannabinoids, CB1 and CB2 cannabinoid receptors and enzymes involved in synthesis and degradation of endocannabinoids comprise the major components of the cannabinoid system. Although, cannabinoid receptors are known to be involved in anti-convulsant, anti-nociceptive, anti-psychotic, anti-emetic, and anti-oxidant effects of cannabinoids, in recent years, an increasing number of studies suggest that, at pharmacologically relevant concentrations, these compounds interact with several molecular targets including G-protein coupled receptors, ion …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Artificial Intelligence And The Situational Rationality Of Diagnosis: Human Problem-Solving And The Artifacts Of Health And Medicine, Michael W. Raphael

Publications and Research

What is the problem-solving capacity of artificial intelligence (AI) for health and medicine? This paper draws out the cognitive sociological context of diagnostic problem-solving for medical sociology regarding the limits of automation for decision-based medical tasks. Specifically, it presents a practical way of evaluating the artificiality of symptoms and signs in medical encounters, with an emphasis on the visualization of the problem-solving process in doctor-patient relationships. In doing so, the paper details the logical differences underlying diagnostic task performance between man and machine problem-solving: its principle of rationality, the priorities of its means of adaptation to abstraction, and the effects …

Linking Historical Redlining Maps To Present‐Day Environmental Hazards In St. Louis, Margaret Keller, Kenneth Brown

Annual Student Research Poster Session

Many of America’s cities were mapped by the Home Owners Loan Corporation (HOLC). This practice led to structurally racist housing policies and practices, namely redlining. The practice of redlining directed both public and private capital to native‐born, white families and away from Black, Latinx, and immigrant families that lived in these areas. While redlining is illegal today due to the Fair Housing Act of 1968, its impact is still evident in the structure of many U.S. cities including St. Louis (Hillier 2015). Today, redlined areas are particularly susceptible to environmental injustices because laws, regulations, governmental programs, and policies inadequately protect …

Examining The Association Between A Modified Quan Charlson Comorbidity Index (Qcci) And Viral Suppression: A Cross Sectional Analysis Of Dc Cohort Participants, Hasmin C. Ramirez, Lauren O’Connor, Morgan Byrne, Anne Monroe

Epidemiology Faculty Posters and Presentations

No abstract provided.

Quality Of Life In Older And Younger People With Hiv And Diabetes, Lauren F. O’Connor, La’Marcus Wingate, Sam Simmens, Amanda D. Castel, Anne K. Monroe

Epidemiology Faculty Posters and Presentations

No abstract provided.

Overview Of The Clpsych 2022 Shared Task: Capturing Moments Of Change In Longitudinal User Posts, Adam Tsakalidis, Jenny Chim, Iman Munire Bilal, Ayah Zirikly, Dana Atzil-Slonim, Federico Nanni, Philip Resnik, Manas Gaur, Kaushik Roy, Becky Inkster, Jeff Leintz, Maria Liakata

Publications

We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of Moments of Change in longitudinal posts by individuals on social media and its connection with information regarding mental health . This year's task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sensitive evaluation metrics. The Shared Task consisted of two subtasks: (a) the main task of capturing changes in an individual's mood (drastic changes-`Switches'- and gradual changes -`Escalations'- on the basis of textual content shared online; and subsequently (b) the sub-task …

Cancer Incidence And Stage At Diagnosis Among People With Psychotic Disorders: Systematic Review And Meta-Analysis., Jared C Wootten, Joshua C Wiener, Phillip S Blanchette, Kelly K. Anderson

Epidemiology and Biostatistics Publications

Research regarding the incidence of cancer among people with psychotic disorders relative to the general population is equivocal, although the evidence suggests that they have more advanced stage cancer at diagnosis. We conducted a systematic review and meta-analysis to examine the incidence and stage at diagnosis of cancer among people with, relative to those without, psychotic disorders. We searched the MEDLINE, EMBASE, PsycINFO, and CINAHL databases. Articles were included if they reported the incidence and/or stage at diagnosis of cancer in people with psychotic disorders. Random effects meta-analyses were used to determine risk of cancer and odds of advanced stage …

Predicting The Level Of Respiratory Support In Covid-19 Patients Using Machine Learning, Hisham Abdeltawab, Fahmi Khalifa, Yaser Elnakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz

All Works

In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the …

Two Singapore Public Healthcare Ai Applications For National Screening Programs And Other Examples, Andy Wee An Ta, Han Leong Goh, Christine Ang, Lian Yeow Koh, Ken Poon, Steven M. Miller

Research Collection School Of Computing and Information Systems

This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation-wide screening programs. The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases, targeting the rapidly increasing number of adults in the country with diabetes. In the second example, the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the …

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of `depression', our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …

Seasonal Habitat Selection By American White Pelicans, Frederick L. Cunningham, Guiming Wang, D. Tommy King

USDA Wildlife Services: Staff Publications

Resource utilization strategies of avian migrants are a major concern for conservation and management. Understanding seasonal habitat selection by migratory birds helps us explain the ongoing continental declines of migratory bird populations. Our objective was to compare the secondorder and third-order habitat selection by the American White Pelican (Pelecanus erythrorhynchos; hereafter pelican) between the breeding and non-breeding grounds. We tested the Lack hypothesis that habitat selection by migratory birds is stronger on the breeding grounds than on the nonbreeding grounds. We used random-effect Dirichlet-multinomial models to estimate the second-order habitat selection between the seasons with the GPS locations …

Role Of Imaging And Ai In The Evaluation Of Covid-19 Infection: A Comprehensive Survey, Mayada Elgendy, Hossam Magdy Balaha, Mohamed Shehata, Ahmed Alksas, Mahitab Ghoneim, Fatma Sherif, Ali Mahmoud, Ahmed Elgarayhi, Fatma Taher, Mohammed Sallah, Mohammed Ghazal, Ayman El-Baz

All Works

Coronavirus disease 2019 (COVID-19) is a respiratory illness that started and rapidly became the pandemic of the century, as the number of people infected with it globally exceeded 253.4 million. Since the beginning of the pandemic of COVID-19, over two years have passed. During this hard period, several defies have been coped by the scientific society to know this novel disease, evaluate it, and treat affected patients. All these efforts are done to push back the spread of the virus. This article provides a comprehensive review to learn about the COVID-19 virus and its entry mechanism, its main repercussions on …

Detecting High-Risk Factors And Early Diagnosis Of Diabetes Using Machine Learning Methods, Zahid Ullah, Farrukh Saleem, Mona Jamjoom, Bahjat Fakieh, Faris Kateb, Abdullah Marish Ali, Babar Shah

All Works

Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can …

Probing Mechanisms Of Binding And Allostery In The Sars-Cov-2 Spike Omicron Variant Complexes With The Host Receptor: Revealing Functional Roles Of The Binding Hotspots In Mediating Epistatic Effects And Communication With Allosteric Pockets, Gennady M. Verkhivker, Steve Agajanian, Ryan Kassab, Keerthi Krishnan

Mathematics, Physics, and Computer Science Faculty Articles and Research

In this study, we performed all-atom MD simulations of RBD–ACE2 complexes for BA.1, BA.1.1, BA.2, and BA.3 Omicron subvariants, conducted a systematic mutational scanning of the RBD–ACE2 binding interfaces and analysis of electrostatic effects. The binding free energy computations of the Omicron RBD–ACE2 complexes and comprehensive examination of the electrostatic interactions quantify the driving forces of binding and provide new insights into energetic mechanisms underlying evolutionary differences between Omicron variants. A systematic mutational scanning of the RBD residues determines the protein stability centers and binding energy hotpots in the Omicron RBD–ACE2 complexes. By employing the ensemble-based global network analysis, we …

Effects Of Early‑Life Experience On Innovation And Problem‑Solving In Captive Coyotes, Andrew C. Garcia, Mitchell A. Parsons, Julie K. Young

USDA Wildlife Services: Staff Publications

Early-life experience often shapes behaviors like innovation and exploration. These behaviors are important to animals encountering novel food resources in diverse habitats, such as mesocarnivores in urban areas. To understand if early-life experiences impact later-life behavior, we examined how coyotes (Canis latrans) responded to a multi-access puzzle box at two life stages: pup (~ 7 weeks) and dispersal (~ 10 months). We first exposed pups, still living with their parents and littermates, to a baited puzzle box. At dispersal age, we again tested both these pups and an age-matched control group that was not exposed to the puzzle …

Interpretable Machine Learning Models For Molecular Design Of Tyrosine Kinase Inhibitors Using Variational Autoencoders And Perturbation-Based Approach Of Chemical Space Exploration, Keerthi Krishnan, Ryan Kassab, Steve Agajanian, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

In the current study, we introduce an integrative machine learning strategy for the autonomous molecular design of protein kinase inhibitors using variational autoencoders and a novel cluster-based perturbation approach for exploration of the chemical latent space. The proposed strategy combines autoencoder-based embedding of small molecules with a cluster-based perturbation approach for efficient navigation of the latent space and a feature-based kinase inhibition likelihood classifier that guides optimization of the molecular properties and targeted molecular design. In the proposed generative approach, molecules sharing similar structures tend to cluster in the latent space, and interpolating between two molecules in the latent space …

Toward Informatics-Enabled Preparedness For Natural Hazards To Minimize Health Impacts Of Climate Change, Jimmy Phuong, Naomi O. Riches, Luca Calzoni, Gora Datta, Deborah Duran, Asiyah Yu Lin, Ramesh P. Singh, Anthony E. Solomonides, Noreen Y. Whysel, Ramakanth Kavuluru

Publications and Research

Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants …

Quantifying The Relationship Between Sub-Population Wastewater Samples And Community-Wide Sars-Cov-2 Seroprevalence, Ted Smith, Rochelle H. Holm, Rachel J. Keith, Alok R. Amraotkar, Chance R. Alvarado, Krzysztof Banecki, Boseung Choi, Ian Santisteban, Adrienne M. Bushau-Sprinkle, Kathleen T. Kitterman, Joshua Fuqua, Krystal T. Hamorsky, Kenneth E. Palmer, J. Michael Brick, Aruni Bhatnagar, Grzegorz A. Rempala

Faculty Scholarship

Robust epidemiological models relating wastewater to community disease prevalence are lacking. Assessments of SARS-CoV-2 infection rates have relied primarily on convenience sampling, which does not provide reliable estimates of community disease prevalence due to inherent biases. This study conducted serial stratified randomized samplings to estimate the prevalence of SARS-CoV-2 antibodies in 3717 participants and obtained weekly samples of community wastewater for SARS-CoV-2 concentrations in Jefferson County, KY (USA) from August 2020 to February 2021. Using an expanded Susceptible-Infected-Recovered model, the longitudinal estimates of the disease prevalence were obtained and compared with the wastewater concentrations using regression analysis. The model analysis …

Cancer Incidence And Stage At Diagnosis Among People With Recent-Onset Psychotic Disorders: A Retrospective Cohort Study Using Health Administrative Data From Ontario, Canada., Jared C Wootten, Lucie Richard, Phillip S Blanchette, Joshua C. Wiener, Kelly K. Anderson

Epidemiology and Biostatistics Publications

OBJECTIVE: Prior evidence on the relative risk of cancer among people with psychotic disorders is equivocal. The objective of this study was to compare incidence and stage at diagnosis of cancer for people with psychotic disorders relative to the general population.

METHOD: We constructed a retrospective cohort of people with a first diagnosis of non-affective psychotic disorder and a comparison group from the general population using linked health administrative databases in Ontario, Canada. The cohort was followed for incident diagnoses of cancer over a 25-year period. We used Poisson and logistic regression models to compare cancer incidence and stage at …

Analyzing The Impact Of Covid-19 Control Policies On Campus Occupancy And Mobility Via Wifi Sensing, Camellia Zakaria, Amee Trivedi, Emmanuel Cecchet, Michael Chee, Prashant Shenoy, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Mobile sensing has played a key role in providing digital solutions to aid with COVID-19 containment policies, primarily to automate contact tracing and social distancing measures. As more and more countries reopen from lockdowns, there remains a pressing need to minimize crowd movements and interactions, particularly in enclosed spaces. Many COVID-19 technology solutions leverage positioning systems, generally using Bluetooth and GPS, and can theoretically be adapted to monitor safety compliance within dedicated environments. However, they may not be the ideal modalities for indoor positioning. This article conjectures that analyzing user occupancy and mobility via deployed WiFi infrastructure can help institutions …

Tetrahydrocurcumin Improves Lipopolysaccharide-Induced Myocardial Dysfunction By Inhibiting Oxidative Stress And Inflammation Via Jnk/Erk Signaling Pathway Regulation, Hanzhao Zhu, Liyun Zhang, Hao Jia, Lu Xu, Yu Cao, Mengen Zhai, Kaifeng Li, Lin Xia, Liqing Jiang, Xiang Li, Yenong Zhou, Jincheng Liu, Shiqiang Yu, Weixun Duan

Chemistry Undergraduate Publications

Background

Acute myocardial dysfunction in patients with sepsis is attributed to oxidative stress, inflammation, and cardiomyocyte loss; however, specific drugs for its prevention are still lacking. Tetrahydrocurcumin (THC) has been proven to contribute to the prevention of various cardiovascular diseases by decreasing oxidative stress and inflammation. This study was performed to investigate the functions and mechanism of action of THC in septic cardiomyopathy.

Methods

After the oral administration of THC (120 mg/kg) for 5 consecutive days, a mouse model of sepsis was established via intraperitoneal lipopolysaccharide (LPS, 10 mg/kg) injection. Following this, cardiac function was assessed, pathological section staining was …

Contrastive Transformer-Based Multiple Instance Learning For Weakly Supervised Polyp Frame Detection, Tian Yu, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan Verjans, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images, which i) ignore the importance of temporal information in consecutive video frames, and ii) lack knowledge about the polyps. Consequently, they often have high detection errors, especially on challenging polyp cases (e.g., small, flat, or partially visible polyps). In this work, we formulate polyp detection as a weakly-supervised anomaly detection task that uses video-level labelled training data to detect frame-level polyps. In particular, we propose a novel convolutional transformer-based multiple instance learning method designed to identify abnormal frames (i.e., frames with polyps) from anomalous videos (i.e., …

Flint Michigan Drinking Water Crisis, J. David Aiken

Cornhusker Economics

Briefly covers the Flint, Michigan drinking water crisis including providing some background, a timeline of events, and key takeaways from the perspective of public policy.

This article was originally prepared for distribution to students in Aiken's AECN 357 environmental and natural resources law course.

Divergent Neural And Endocrine Responses In Wild-Caught And Laboratory-Bred Rattus Norvegicus, Joanna Jacob, Sally Watanabe, Jonathan Richardson, Nick Gonzales, Emily Ploppert, Garet Lahvis, Aaron Shiels, Sadie Wenger, Kelly Saverino, Janhavi Bhalerao, Brendan Crockett, Erin Burns, Olivia Harding, Krista Fischer-Stenger, Kelly Lambert

USDA Wildlife Services: Staff Publications

Although rodents have represented the most intensely studied animals in neurobiological investigations for more than a century, few studies have systematically compared neural and endocrine differences between wild rodents in their natural habitats and laboratory strains raised in traditional laboratory environments. In the current study, male and female Rattus norvegicus rats were trapped in an urban setting and compared to weight-and sex-matched conspecifics living in standard laboratory housing conditions. Brains were extracted for neural assessments and fecal boli were collected for endocrine [corticosterone and dehydroepiandrosterone (DHEA)] assays. Additionally, given their role in immune and stress functions, spleen and adrenal weights …

Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi

All Works

COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which …

Did Usage Of Mental Health Apps Change During Covid-19? A Comparative Study Based On An Objective Recording Of Usage Data And Demographics, Maryam Aziz, Aiman Erbad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali

All Works

This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users' sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. …

Statistical Analysis Methods Applied To Early Outpatient Covid-19 Treatment Case Series Data, Eleftherios Gkioulekas, Peter A. Mccullough, Vladimir Zelenko

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

When confronted with a public health emergency, significant innovative treatment protocols can sometimes be discovered by medical doctors at the front lines based on repurposed medications. We propose a statistical framework for analyzing the case series of patients treated with such new protocols, that enables a comparison with our prior knowledge of expected outcomes, in the absence of treatment. The goal of the proposed methodology is not to provide a precise measurement of treatment efficacy, but to establish the existence of treatment efficacy, in order to facilitate the binary decision of whether the treatment protocol should be adopted on an …

Experimental Infection Of Brazilian Free-Tailed Bats (Tadarida Brasiliensis) With Two Strains Of Sars-Cov-2, Angela M. Bosco-Lauth, Stephanie M. Porter, Karen A. Fox, Mary E. Wood, Daniel Neubaum, Marissa Quilici

USDA Wildlife Services: Staff Publications

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is presumed to have originated from wildlife and shares homology with other bat coronaviruses. Determining the susceptibility of North American bat species to SARS-CoV-2 is of utmost importance for making decisions regarding wildlife management, public health, and conservation. In this study, Brazilian free-tailed bats (Tadarida brasiliensis) were experimentally infected with two strains of SARS-CoV-2 (parental WA01 and Delta variant), evaluated for clinical disease, sampled for viral shedding and antibody production, and analyzed for pathology. None of the bats (n = 18) developed clinical disease associated with infection, shed infectious virus, or …

Long-Term Effect Of A Gnrh-Based Immunocontraceptive On Feral Cattle In Hong Kong, Rebecca Pinkham, Ka-Kei Koon, Jason To, Jason Chan, Flavie Vial, Matt Gomm, Douglas C. Eckery, Giovanna Massei

USDA Wildlife Services: Staff Publications

Increasing human-wildlife conflicts worldwide are driving the need for multiple solutions to reducing “problem” wildlife and their impacts. Fertility control is advocated as a non-lethal tool to manage free-living wildlife and in particular to control iconic species. Injectable immunocontraceptives, such as GonaCon, stimulate the immune system to produce antibodies against the gonadotrophin-releasing hormone (GnRH), which in turn affects the release of reproductive hormones in mammals. Feral cattle (Bos indicus or Bos taurus) in Hong Kong are an iconic species whose numbers and impacts on human activities have increased over the last decade. Previous studies have proven that a …