VTechWorks Repository :: Browsing by Author "Abedi, Vida"

Browsing by Author "Abedi, Vida"

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Challenges in Personalized Nutrition and Health
Verma, Meghna; Hontecillas, Raquel; Tubau-Juni, Nuria; Abedi, Vida; Bassaganya-Riera, Josep (Frontiers, 2018-11-29)
Characterization of Regulatory Mechanisms in Mucosal Immunity by Systems Immunology
Tubau Juni, Nuria (Virginia Tech, 2020-01-28)
The mucosal immunity of the gastrointestinal (GI) tract is constituted by a complex, highly specialized and dynamic system of immune components that aim to protect the gut from external threats. The sustained exposure of the mucosal immune system of the GI tract to an enormous number of lumen antigens, requires the constant upkeep of a highly regulated balance between initiation of immune responses against harmful agents and the generation of immune tolerance towards innocuous antigens. Therefore, the regulatory component is key to preserve tissue homeostasis and a normal functioning of the system. Indeed, defective regulatory responses lead to the development of pathological conditions, including unresolved infections, and inflammatory diseases. In this study, we aim to elucidate novel mechanisms involved in host-pathogen interactions during Helicobacter pylori and Clostridium difficile infections. Indeed, this work integrates preclinical in vivo and in vitro experimental approaches together with a bioinformatics pipeline to identify and characterize novel regulatory mechanisms and molecular targets of the mucosal immune system during enteric infections. Firstly, we identified a novel regulatory mechanism during H. pylori infection mediated by a specific subset of IL10-producing tissue resident macrophages. Secondly, we employed an ex vivo H. pylori co-culture with bone marrow derived macrophages, that together with a global transcriptomic analysis and a bioinformatics pipeline, lead to the discovery of promising regulatory genes based on expression kinetics. Lastly, we characterized the innate inflammatory responses induced during the course of C. difficile infection and identified IL-1ß, and its subsequent induction of neutrophil recruitment, as a key mediator of C. difficile-induced effectors responses. The characterized regulatory mechanisms in this work show promise to lead the generation of new host-centered therapeutics through the modulation of the immune response as promising alternative treatments for infectious diseases.
Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event
Chaudhary, Durgesh; Abedi, Vida; Li, Jiang; Schirmer, Clemens M.; Griessenauer, Christoph J.; Zand, Ramin (2019-11-12)
Introduction: Recurrent stroke has a higher rate of death and disability. A number of risk scores have been developed to predict short-term and long-term risk of stroke following an initial episode of stroke or transient ischemic attack (TIA) with limited clinical utilities. In this paper, we review different risk score models and discuss their validity and clinical utilities. Methods: The PubMed bibliographic database was searched for original research articles on the various risk scores for risk of stroke following an initial episode of stroke or TIA. The validation of the models was evaluated by examining the internal and external validation process as well as statistical methodology, the study power, as well as the accuracy and metrics such as sensitivity and specificity. Results: Different risk score models have been derived from different study populations. Validation studies for these risk scores have produced conflicting results. Currently, ABCD(2) score with diffusion weighted imaging (DWI) and Recurrence Risk Estimator at 90 days (RRE-90) are the two acceptable models for short-term risk prediction whereas Essen Stroke Risk Score (ESRS) and Stroke Prognosis Instrument-II (SPI-II) can be useful for prediction of long-term risk. Conclusion: The clinical risk scores that currently exist for predicting short-term and long-term risk of recurrent cerebral ischemia are limited in their performance and clinical utilities. There is a need for a better predictive tool which can overcome the limitations of current predictive models. Application of machine learning methods in combination with electronic health records may provide platform for development of new-generation predictive tools.
Empirical study using network of semantically related associations in bridging the knowledge gap
Abedi, Vida; Yeasin, Mohammed; Zand, Ramin (2014-11-27)
Background The data overload has created a new set of challenges in finding meaningful and relevant information with minimal cognitive effort. However designing robust and scalable knowledge discovery systems remains a challenge. Recent innovations in the (biological) literature mining tools have opened new avenues to understand the confluence of various diseases, genes, risk factors as well as biological processes in bridging the gaps between the massive amounts of scientific data and harvesting useful knowledge. Methods In this paper, we highlight some of the findings using a text analytics tool, called ARIANA - Adaptive Robust and Integrative Analysis for finding Novel Associations. Results Empirical study using ARIANA reveals knowledge discovery instances that illustrate the efficacy of such tool. For example, ARIANA can capture the connection between the drug hexamethonium and pulmonary inflammation and fibrosis that caused the tragic death of a healthy volunteer in a 2001 John Hopkins asthma study, even though the abstract of the study was not part of the semantic model. Conclusion An integrated system, such as ARIANA, could assist the human expert in exploratory literature search by bringing forward hidden associations, promoting data reuse and knowledge discovery as well as stimulating interdisciplinary projects by connecting information across the disciplines.
High-resolution computational modeling of immune responses in the gut
Verma, Meghna; Bassaganya-Riera, Josep; Leber, Andrew; Tubau-Juni, Nuria; Hoops, Stefan; Abedi, Vida; Chen, Xi; Hontecillas, Raquel (Oxford University Press, 2019-06-01)
Background: Helicobacter pylori causes gastric cancer in 1-2% of cases but is also beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H. pylori-colonized individuals experience no detrimental effects. To study the mechanisms promoting host tolerance to the bacterium in the gastrointestinal mucosa and systemic regulatory effects, we investigated the dynamics of immunoregulatory mechanisms triggered by H. pylori using a high-performance computing-driven ENteric Immunity SImulator multiscale model. Immune responses were simulated by integrating an agent-based model, ordinary, and partial differential equations. Results: The outputs were analyzed using 2 sequential stages: The first used a partial rank correlation coefficient regression-based and the second a metamodel-based global sensitivity analysis. The influential parameters screened from the first stage were selected to be varied for the second stage. The outputs from both stages were combined as a training dataset to build a spatiotemporal metamodel. The Sobol indices measured time-varying impact of input parameters during initiation, peak, and chronic phases of infection. The study identified epithelial cell proliferation and epithelial cell death as key parameters that control infection outcomes. In silico validation showed that colonization with H. pylori decreased with a decrease in epithelial cell proliferation, which was linked to regulatory macrophages and tolerogenic dendritic cells. Conclusions: The hybrid model of H. pylori infection identified epithelial cell proliferation as a key factor for successful colonization of the gastric niche and highlighted the role of tolerogenic dendritic cells and regulatory macrophages in modulating the host responses and shaping infection outcomes.
Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients
Darabi, Negar; Hosseinichimeh, Niyousha; Noto, Anthony; Zand, Ramin; Abedi, Vida (Frontiers, 2021-03-31)
Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting–XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model’s AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64–0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems’ resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
Modeling Host Immune Responses in Infectious Diseases
Verma, Meghna (Virginia Tech, 2019-12-17)
Infectious diseases caused by bacteria, fungi, viruses and parasites have affected humans historically. Infectious diseases remain a major cause of premature death and a public health concern globally with increased mortality and significant economic burden. Unvaccinated individuals, people with suppressed and compromised immune systems are at higher risk of suffering from infectious diseases. In spite of significant advancements in infectious diseases research, the control or treatment process faces challenges. The mucosal immune system plays a crucial role in safeguarding the body from harmful pathogens, while being constantly exposed to the environment. To develop treatment options for infectious diseases, it is vital to understand the immune responses that occur during infection. The two infectious diseases presented here are: i) Helicobacter pylori infection and ii) human immunodeficiency (HIV) and human papillomavirus (HPV) co-infection. H pylori, is a bacterium that colonizes the stomach and causes gastric cancer in 1-2% but is beneficial for protection against allergies and gastroesophageal diseases. An estimated 85% of H pylori colonized individuals show no detrimental effects. HIV is a virus that causes AIDS, one of the deadliest and most persistent epidemics. HIV-infected patients are at an increased risk of co-infection with HPV, and report an increased incidence of oral cancer. The goal of this thesis is to elucidate the host immune responses in infectious diseases via the use of computational and mathematical models. First, the thesis reviews the need for computational and mathematical models to study the immune responses in the course of infectious diseases. Second, it presents a novel sensitivity analysis method that identifies important parameters in a hybrid (agent-based/equation-based) model of H. pylori infection. Third, it introduces a novel model representing the HIV/HPV coinfection and compares the simulation results with a clinical study. Fourth, it discusses the need of advanced modeling technologies to achieve a personalized systems wide approach and the challenges that can be encountered in the process. Taken together, the work in this dissertation presents modeling approaches that could lead to the identification of host immune factors in infectious diseases in a predictive and more resource-efficient manner.
Modeling the Mechanisms by Which HIV Associated Immunosuppression Influences HPV Persistence at the Oral Mucosa
Verma, Meghna; Erwin, Samantha; Abedi, Vida; Hontecillas, Raquel; Hoops, Stefan; Leber, Andrew; Bassaganya-Riera, Josep; Ciupe, Stanca M. (PLOS, 2017-01-06)
Human immunodeficiency virus (HIV)-infected patients are at an increased risk of co-infection with human papilloma virus (HPV), and subsequent malignancies such as oral cancer. To determine the role of HIV-associated immune suppression on HPV persistence and pathogenesis, and to investigate the mechanisms underlying the modulation of HPV infection and oral cancer by HIV, we developed a mathematical model of HIV/HPV co-infection. Our model captures known immunological and molecular features such as impaired HPV-specific effector T helper 1 (Th1) cell responses, and enhanced HPV infection due to HIV. We used the model to determine HPV prognosis in the presence of HIV infection, and identified conditions under which HIV infection alters HPV persistence in the oral mucosa system. The model predicts that conditions leading to HPV persistence during HIV/HPV co-infection are the permissive immune environment created by HIV and molecular interactions between the two viruses. The model also determines when HPV infection continues to persist in the short run in a co-infected patient undergoing antiretroviral therapy. Lastly, the model predicts that, under efficacious antiretroviral treatment, HPV infections will decrease in the long run due to the restoration of CD4+ T cell numbers and protective immune responses.
Modeling the Regulatory Mechanisms by Which NLRX1 Modulates Innate Immune Responses to Helicobacter pylori Infection
Philipson, Casandra W.; Bassaganya-Riera, Josep; Viladomiu, Monica; Kronsteiner, Barbara; Abedi, Vida; Hoops, Stefan; Michalak, Pawel; Kang, Lin; Girardin, Stephen E.; Hontecillas, Raquel (PLOS, 2015-09-14)
Helicobacter pylori colonizes half of the world’s population as the dominant member of the gastric microbiota resulting in a lifelong chronic infection. Host responses toward the bacterium can result in asymptomatic, pathogenic or even favorable health outcomes; however, mechanisms underlying the dual role of H. pylori as a commensal versus pathogenic organism are not well characterized. Recent evidence suggests mononuclear phagocytes are largely involved in shaping dominant immunity during infection mediating the balance between host tolerance and succumbing to overt disease. We combined computational modeling, bioinformatics and experimental validation in order to investigate interactions between macrophages and intracellular H. pylori. Global transcriptomic analysis on bone marrow-derived macrophages (BMDM) in a gentamycin protection assay at six time points unveiled the presence of three sequential host response waves: an early transient regulatory gene module followed by sustained and late effector responses. Kinetic behaviors of pattern recognition receptors (PRRs) are linked to differential expression of spatiotemporal response waves and function to induce effector immunity through extracellular and intracellular detection of H. pylori. We report that bacterial interaction with the host intracellular environment caused significant suppression of regulatory NLRC3 and NLRX1 in a pattern inverse to early regulatory responses. To further delineate complex immune responses and pathway crosstalk between effector and regulatory PRRs, we built a computational model calibrated using time-series RNAseq data. Our validated computational hypotheses are that: 1) NLRX1 expression regulates bacterial burden in macrophages; and 2) early host response cytokines down-regulate NLRX1 expression through a negative feedback circuit. This paper applies modeling approaches to characterize the regulatory role of NLRX1 in mechanisms of host tolerance employed by macrophages to respond to and/or to co-exist with intracellular H. pylori.
Modeling the Role of Lanthionine Synthetase C-Like 2 (LANCL2) in the Modulation of Immune Responses to Helicobacter pylori Infection
Leber, Andrew; Bassaganya-Riera, Josep; Tubau-Juni, Nuria; Zoccoli-Rodriguez, Victoria; Viladomiu, Monica; Abedi, Vida; Lu, Pinyi; Hontecillas, Raquel (PLOS, 2016-12-09)
Immune responses to Helicobacter pylori are orchestrated through complex balances of host-bacterial interactions, including inflammatory and regulatory immune responses across scales that can lead to the development of the gastric disease or the promotion of beneficial systemic effects. While inflammation in response to the bacterium has been reasonably characterized, the regulatory pathways that contribute to preventing inflammatory events during H. pylori infection are incompletely understood. To aid in this effort, we have generated a computational model incorporating recent developments in the understanding of H. pylori-host interactions. Sensitivity analysis of this model reveals that a regulatory macrophage population is critical in maintaining high H. pylori colonization without the generation of an inflammatory response. To address how this myeloid cell subset arises, we developed a second model describing an intracellular signaling network for the differentiation of macrophages. Modeling studies predicted that LANCL2 is a central regulator of inflammatory and effector pathways and its activation promotes regulatory responses characterized by IL-10 production while suppressing effector responses. The predicted impairment of regulatory macrophage differentiation by the loss of LANCL2 was simulated based on multiscale linkages between the tissue-level gastric mucosa and the intracellular models. The simulated deletion of LANCL2 resulted in a greater clearance of H. pylori, but also greater IFNγ responses and damage to the epithelium. The model predictions were validated within a mouse model of H. pylori colonization in wild-type (WT), LANCL2 whole body KO and myeloid-specific LANCL2-/- (LANCL2Myeloid) mice, which displayed similar decreases in H. pylori burden, CX3CR1+ IL-10-producing macrophages, and type 1 regulatory (Tr1) T cells. This study shows the importance of LANCL2 in the induction of regulatory responses in macrophages and T cells during H. pylori infection.
Modeling-Enabled Characterization of Novel NLRX1 Ligands
Lu, Pinyi; Hontecillas, Raquel; Abedi, Vida; Kale, Shiv D.; Leber, Andrew; Heltzel, Chase; Langowski, Mark; Godfrey, Victoria; Philipson, Casandra; Tubau-Juni, Nuria; Carbo, Adria; Girardin, Stephen; Uren, Aykut; Bassaganya-Riera, Josep (PLOS, 2015-12-29)
Nucleotide-binding domain and leucine-rich repeat containing (NLR) family are intracellular sentinels of cytosolic homeostasis that orchestrate immune and inflammatory responses in infectious and immune-mediated diseases. NLRX1 is a mitochondrial-associated NOD-like receptor involved in the modulation of immune and metabolic responses. This study utilizes molecular docking approaches to investigate the structure of NLRX1 and experimentally assesses binding to naturally occurring compounds from several natural product and lipid databases. Screening of compound libraries predicts targeting of NLRX1 by conjugated trienes, polyketides, prenol lipids, sterol lipids, and coenzyme A-containing fatty acids for activating the NLRX1 pathway. The ligands of NLRX1 were identified by docking punicic acid (PUA), eleostearic acid (ESA), and docosahexaenoic acid (DHA) to the C-terminal fragment of the human NLRX1 (cNLRX1). Their binding and that of positive control RNA to cNLRX1 were experimentally determined by surface plasmon resonance (SPR) spectroscopy. In addition, the ligand binding sites of cNLRX1 were predicted in silico and validated experimentally. Target mutagenesis studies demonstrate that mutation of 4 critical residues ASP677, PHE680, PHE681, and GLU684 to alanine resulted in diminished affinity of PUA, ESA, and DHA to NLRX1. Consistent with the regulatory actions of NLRX1 on the NF-κB pathway, treatment of bone marrow derived macrophages (BMDM)s with PUA and DHA suppressed NF-κB activity in a NLRX1 dependent mechanism. In addition, a series of pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the regulatory function of PUA on colitis is NLRX1 dependent. Thus, we identified novel small molecules that bind to NLRX1 and exert anti-inflammatory actions.
Modeling-enabled Systems Nutritional Immunology
Verma, Meghna; Hontecillas, Raquel; Abedi, Vida; Leber, Andrew; Tubau-Juni, Nuria; Philipson, Casandra; Carbo, Adria; Bassaganya-Riera, Josep (Frontiers, 2016-02-16)
This review highlights the fundamental role of nutrition in the maintenance of health, the immune response, and disease prevention. Emerging global mechanistic insights in the field of nutritional immunology cannot be gained through reductionist methods alone or by analyzing a single nutrient at a time. We propose to investigate nutritional immunology as a massively interacting system of interconnected multistage and multiscale networks that encompass hidden mechanisms by which nutrition, microbiome, metabolism, genetic predisposition, and the immune system interact to delineate health and disease. The review sets an unconventional path to apply complex science methodologies to nutritional immunology research, discovery, and development through "use cases" centered around the impact of nutrition on the gut microbiome and immune responses. Our systems nutritional immunology analyses, which include modeling and informatics methodologies in combination with pre-clinical and clinical studies, have the potential to discover emerging systems-wide properties at the interface of the immune system, nutrition, microbiome, and metabolism.
A Multi-Level Analysis of Major Health Challenges in the United States Using Data Analytics Approaches
Darabi, Negar (Virginia Tech, 2020-09-04)
The U.S. healthcare system is facing many public health challenges that affect population health, societal well-being, and quality of healthcare. Infant mortality, opioid overdose death, and hospital readmission after stroke are some of these important public health concerns that can impact the effectiveness and outcomes of the healthcare system. We analyze these problems through the industrial engineering and data analytics lens. The major goal of this dissertation is to enhance understanding of these three challenges and related interventions using different levels of analysis to improve the health outcomes. To attain this objective, I introduced three stand-alone papers to answer the related research questions. In essay 1, we focused on the performance of the state's healthcare systems in reducing unfavorable birth outcomes such as infant mortality, preterm birth, and low birthweight using Data Envelopment Approach. We constructed a unique state-level dataset to answer this main research question: what does make a healthcare system more successful in improving the birth outcomes? Our results indicated that socioeconomic and demographic factors may facilitate or obstruct health systems in improving their outcomes. We realized that states with a lower rate of poverty and African-American women were more successful in effectively reduce unfavorable birth outcomes. In the second essay, we looked into the trends of the opioid overdose mortalities in each state from 2008 to 2017. We investigated the effect of four state laws and programs that have been established to curb the epidemic (i.e., dose and duration limitations on the initial prescription, pain management clinic laws, mandated use of prescription drug monitoring programs, and medical cannabis laws) in short and long-term, while we controlled for several protentional risk factors. The results of fixed-effect regression and significant tests indicated that state policies and laws were unlikely to result in an immediate reduction in overdose mortalities and comprehensive interventions were needed to restrain the epidemic. The third essay investigated the risk factors of 30-day readmission in patients with ischemic stroke at an individual level. We aimed to identify the main risk factors of stroke readmissions and prioritized them using machine learning techniques and logistic regression. We also introduced the most effective predictive model based on different performance metrics. We used the electronic health records of stroke patients extracted from two stroke centers within the Geisinger Health System from 2015 to 2018. This data set included a comprehensive list of clinical features, patients' comorbidities, demographical characteristics, discharge status, and type of health insurance. One of the major findings of this study was that stroke severity, insert an indwelling urinary catheter, and hypercoagulable state were more important than generally known diagnoses such as diabetes and hypertension in the prediction of stroke 30-day readmission. Furthermore, machine learning-based models can be designed to provide a better predictive model. Overall, this dissertation provided new insights to better understand the three major challenges of the U.S. healthcare system and improve its outcomes.
Multi-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter pylori Infection via Spatio-Temporal Metamodeling
Chen, Xi; Wang, Wenjing; Xie, Guangrui; Hontecillas, Raquel; Verma, Meghna; Leber, Andrew; Bassaganya-Riera, Josep; Abedi, Vida (Frontiers, 2019-02-05)
Computational immunology studies the interactions between the components of the immune system that includes the interplay between regulatory and inflammatory elements. It provides a solid framework that aids the conversion of pre-clinical and clinical data into mathematical equations to enable modeling and in silico experimentation. The modeling-driven insights shed lights on some of the most pressing immunological questions and aid the design of fruitful validation experiments. A typical system of equations, mapping the interaction among various immunological entities and a pathogen, consists of a high-dimensional input parameter space that could drive the stochastic system outputs in unpredictable directions. In this paper, we perform spatio-temporal metamodel-based sensitivity analysis of immune response to Helicobacter pylori infection using the computational model developed by the ENteric Immune SImulator (ENISI). We propose a two-stage metamodel-based procedure to obtain the estimates of the Sobol’ total and first-order indices for each input parameter, for quantifying their time-varying impacts on each output of interest. In particular, we fully reuse and exploit information from an existing simulated dataset, develop a novel sampling design for constructing the two-stage metamodels, and perform metamodel-based sensitivity analysis. The proposed procedure is scalable, easily interpretable, and adaptable to any multi-input multi-output complex systems of equations with a high-dimensional input parameter space.
Obesity and mortality after the first ischemic stroke: Is obesity paradox real?
Chaudhary, Durgesh; Khan, Ayesha; Gupta, Mudit; Hu, Yirui; Li, Jiang; Abedi, Vida; Zand, Ramin (2021-02-10)
Background and purpose Obesity is an established risk factor for ischemic stroke but the association of increased body mass index (BMI) with survival after ischemic stroke remains controversial. Many studies have shown that increased BMI has a "protective" effect on survival after stroke while other studies have debunked the "obesity paradox". This study aimed at examining the relationship between BMI and all-cause mortality at one year in first-time ischemic stroke patients using a large dataset extracted from different resources including electronic health records. Methods This was a retrospective cohort study of consecutive ischemic stroke patients captured in our Geisinger NeuroScience Ischemic Stroke (GNSIS) database. Survival in first-time ischemic stroke patients in different BMI categories was analyzed using Kaplan Meier survival curves. The predictors of mortality at one-year were assessed using a stratified Cox proportional hazards model. Results Among 6,703 first-time ischemic stroke patients, overweight and obese patients were found to have statistically decreased hazard ratio (HR) compared to the non-overweight patients (overweight patients- HR = 0.61 [95% CI, 0.52-0.72]; obese patients- HR = 0.56 [95% CI, 0.48-0.67]). Predictors with a significant increase in the hazard ratio for one-year mortality were age at the ischemic stroke event, history of neoplasm, atrial fibrillation/flutter, diabetes, myocardial infarction and heart failure. Conclusion Our study results support the obesity paradox in ischemic stroke patients as shown by a significantly decreased hazard ratio for one-year mortality among overweight and obese patients in comparison to non-overweight patients.
A predictive analytics model for differentiating between transient ischemic attacks (TIA) and its mimics
Stanciu, Alia; Banciu, Mihai; Sadighi, Alireza; Marshall, Kyle A.; Holland, Neil R.; Abedi, Vida; Zand, Ramin (2020-06-18)
Background Transient ischemic attack (TIA) is a brief episode of neurological dysfunction resulting from cerebral ischemia not associated with permanent cerebral infarction. TIA is associated with high diagnostic errors because of the subjective nature of findings and the lack of clinical and imaging biomarkers. The goal of this study was to design and evaluate a novel multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, to predict the likelihood of TIA, TIA mimics, and minor stroke. Methods We conducted our modeling on consecutive patients who were evaluated in our health system with an initial diagnosis of TIA in a 9-month period. We established the final diagnoses after the clinical evaluation by independent verification from two stroke neurologists. We used Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) for prediction modeling. Results The RFE-based classifier correctly predicts 78% of the overall observations. In particular, the classifier correctly identifies 68% of the cases labeled as “TIA mimic” and 83% of the “TIA” discharge diagnosis. The LASSO classifier had an overall accuracy of 74%. Both the RFE and LASSO-based classifiers tied or outperformed the ABCD2 score and the Diagnosis of TIA (DOT) score. With respect to predicting TIA, the RFE-based classifier has 61.1% accuracy, the LASSO-based classifier has 79.5% accuracy, whereas the DOT score applied to the dataset yields an accuracy of 63.1%. Conclusion The results of this pilot study indicate that a multinomial classification model, based on a combination of feature selection mechanisms coupled with logistic regression, can be used to effectively differentiate between TIA, TIA mimics, and minor stroke.
SARS-CoV-2 Is a Culprit for Some, but Not All Acute Ischemic Strokes: A Report from the Multinational COVID-19 Stroke Study Group
Shahjouei, Shima; Anyaehie, Michelle; Koza, Eric; Tsivgoulis, Georgios; Naderi, Soheil; Mowla, Ashkan; Avula, Venkatesh; Vafaei Sadr, Alireza; Chaudhary, Durgesh; Farahmand, Ghasem; Griessenauer, Christoph J.; Azarpazhooh, Mahmoud Reza; Misra, Debdipto; Li, Jiang; Abedi, Vida; Zand, Ramin (2021-03)
Background. SARS-CoV-2 infected patients are suggested to have a higher incidence of thrombotic events such as acute ischemic strokes (AIS). This study aimed at exploring vascular comorbidity patterns among SARS-CoV-2 infected patients with subsequent stroke. We also investigated whether the comorbidities and their frequencies under each subclass of TOAST criteria were similar to the AIS population studies prior to the pandemic. Methods. This is a report from the Multinational COVID-19 Stroke Study Group. We present an original dataset of SASR-CoV-2 infected patients who had a subsequent stroke recorded through our multicenter prospective study. In addition, we built a dataset of previously reported patients by conducting a systematic literature review. We demonstrated distinct subgroups by clinical risk scoring models and unsupervised machine learning algorithms, including hierarchical K-Means (ML-K) and Spectral clustering (ML-S). Results. This study included 323 AIS patients from 71 centers in 17 countries from the original dataset and 145 patients reported in the literature. The unsupervised clustering methods suggest a distinct cohort of patients (ML-K: 36% and ML-S: 42%) with no or few comorbidities. These patients were more than 6 years younger than other subgroups and more likely were men (ML-K: 59% and ML-S: 60%). The majority of patients in this subgroup suffered from an embolic-appearing stroke on imaging (ML-K: 83% and ML-S: 85%) and had about 50% risk of large vessel occlusions (ML-K: 50% and ML-S: 53%). In addition, there were two cohorts of patients with large-artery atherosclerosis (ML-K: 30% and ML-S: 43% of patients) and cardioembolic strokes (ML-K: 34% and ML-S: 15%) with consistent comorbidity and imaging patterns. Binominal logistic regression demonstrated that ischemic heart disease (odds ratio (OR), 4.9; 95% confidence interval (CI), 1.6-14.7), atrial fibrillation (OR, 14.0; 95% CI, 4.8-40.8), and active neoplasm (OR, 7.1; 95% CI, 1.4-36.2) were associated with cardioembolic stroke. Conclusions. Although a cohort of young and healthy men with cardioembolic and large vessel occlusions can be distinguished using both clinical sub-grouping and unsupervised clustering, stroke in other patients may be explained based on the existing comorbidities.
Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection
Alam, Maksudul; Deng, Xinwei; Philipson, Casandra; Bassaganya-Riera, Josep; Bisset, Keith R.; Carbo, Adria; Eubank, Stephen; Hontecillas, Raquel; Hoops, Stefan; Mei, Yongguo; Abedi, Vida; Marathe, Madhav (PLOS, 2015-09-01)
Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close “neighborhood” of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.
Supervised learning methods in modeling of CD4+ T cell heterogeneity
Lu, Pinyi; Abedi, Vida; Mei, Yongguo; Hontecillas, Raquel; Hoops, Stefan; Carbo, Adria; Bassaganya-Riera, Josep (2015-09-04)
Background Modeling of the immune system – a highly non-linear and complex system – requires practical and efficient data analytic approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types, such as neutrophils, eosinophils, macrophages, dendritic cells, T cells, and B cells. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. For example, CD4+ T cells can be differentiated into Th1, Th2, Th17, Th9, Th22, Treg, Tfh, as well as Tr1. Each subset plays different roles in the immune system. To study molecular mechanisms of cell differentiation, computational systems biology approaches can be used to represent these processes; however, the latter often requires building complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. Furthermore, studying the immune system entails integration of complex processes which occur at different time and space scales. Methods This study presents and compares four supervised learning methods for modeling CD4+ T cell differentiation: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Linear Regression (LR). Application of supervised learning methods could reduce the complexity of Ordinary Differential Equations (ODEs)-based intracellular models by only focusing on the input and output cytokine concentrations. In addition, this modeling framework can be efficiently integrated into multiscale models. Results Our results demonstrate that ANN and RF outperform the other two methods. Furthermore, ANN and RF have comparable performance when applied to in silico data with and without added noise. The trained models were also able to reproduce dynamic behavior when applied to experimental data; in four out of five cases, model predictions based on ANN and RF correctly predicted the outcome of the system. Finally, the running time of different methods was compared, which confirms that ANN is considerably faster than RF. Conclusions Using machine learning as opposed to ODE-based method reduces the computational complexity of the system and allows one to gain a deeper understanding of the complex interplay between the different related entities.
Systems Immunology Approaches for Precision Medicine
Leber, Andrew James (Virginia Tech, 2017-06-20)
The mucosal immune system encompasses a wide array of interactions that work in concert to protect an individual from harmful agents while retaining tolerance to molecules, microbes, and self-antigens that present no danger. The upheaval in the regulation-response balance is a critical aspect in both infectious and immune-mediated disease. To understand this balance and methods of its restoration, iterative and integrative modeling cycles on the pathogenesis of disease are necessary. In this thesis, I present three studies highlighting phases of a systems immunology cycle. Firstly, the thesis provides a description of the construction of a computational ordinary differential equation based model on the host-pathogen-microbiota interactions during Clostridium difficile infection and the use of this model for the development of the hypothesis that host-antimicrobial peptide production may correlate with increased disease severity and promote increased recurrence. Secondly, it provides insight into the necessity of trans-disciplinary analysis for the understanding of novel molecular targets in disease through the immunometabolic regulation of CD4+ T cell by NLRX1 in inflammatory bowel disease. Third, it provides the assessment of novel therapeutics in disease through the evaluation of LANCL2 activation in influenza virus infection. In total, the computational and experimental strategies used in this dissertation are critical foundational pieces in the framework of precision medicine initiatives that can assist in the diagnosis, understanding, and treatment of disease.

Browsing by Author "Abedi, Vida"

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