Browsing by Author "Kim, Seongho"

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    A New Method of Peak Detection for Analysis of Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry Data
    (Duke University Press, 2014) Kim, Seongho; Ouyang, Ming; Jeong, Jaesik; Shen, Changyu; Zhang, Xiang; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    We develop a novel peak detection algorithm for the analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm first performs baseline correction and denoising simultaneously using the NEB model, which also defines peak regions. Peaks are then picked using a mixture of probability distribution to deal with the co-eluting peaks. Peak merging is further carried out based on the mass spectral similarities among the peaks within the same peak group. The algorithm is evaluated using experimental data to study the effect of different cut-offs of the conditional Bayes factors and the effect of different mixture models including Poisson, truncated Gaussian, Gaussian, Gamma, and exponentially modified Gaussian (EMG) distributions, and the optimal version is introduced using a trial-and-error approach. We then compare the new algorithm with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used EMG mixture models.
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    COVID-19 Vaccine Hesitancy among Arab Americans
    (Springer Nature, 2022-04-14) Kheil, Mira H.; Jain, Deepti; Jomaa, Jamil; Askar, Brandon; Alcodray, Yasmeen; Wahbi, Shatha; Brikho, Salar; Kadouh, Ali; Harajli, Deanna; Jawad, Zain N.; Fehmi, Ziad; Elhage, Malaak; Tawil, Tala; Fehmi, Omar; Alzouhayli, Suma J.; Ujayli, Deema; Suleiman, Noor; Kazziha, Omar; Saleh, Rawan; Abada, Evi; Shallal, Anita; Kim, Seongho; Kumar, Vijaya Arun; Zervos, Marcus; Cote, Michele L.; Ali-Fehmi, Rouba; Epidemiology, School of Public Health
    (1) Background: Coronavirus disease-2019 (COVID-19) vaccines have a significant impact on reducing morbidity and mortality from infection. However, vaccine hesitancy remains an obstacle in combating the pandemic. The Arab American (AA) population is understudied; thus, we aimed to explore COVID-19 attitudes within this community. (2) Methods: This was a cross-sectional study. An anonymous online survey was distributed to members of different AA associations and to the community through the snowball method. (3) Results: A total of 1746 participants completed the survey. A total of 92% of respondents reported having received at least one dose of a COVID-19 vaccine. A total of 73% reported willingness to receive a booster, and 72% plan to give their children the vaccine. On multivariate analysis, respondents were more likely to be vaccine-hesitant if they were hesitant about receiving any vaccine in general. They were less likely to be vaccine-hesitant if they were immigrants, over the age of 40, up to date on their general vaccination and if they believed that COVID-19 vaccines are safe and effective in preventing an infection. The belief that all vaccines are effective at preventing diseases was also associated with lower hesitancy. (4) Conclusions: This sample of AAs have higher vaccination rates and are more willing to vaccinate their children against COVID-19 when compared to the rest of the population. However, a reemergence of hesitancy might be arising towards the boosters.
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    Evaluation of COVID-19 Vaccine Attitudes among Arab American Healthcare Professionals Living in the United States
    (MDPI, 2021-08-24) Shallal, Anita; Abada, Evi; Musallam, Rami; Fehmi, Omar; Kaljee, Linda; Fehmi, Ziad; Alzouhayli, Suma; Ujayli, Deema; Dankerlui, Doreen; Kim, Seongho; Cote, Michele L.; Kumar, Vijaya Arun; Zervos, Marcus; Ali-Fehmi, Rouba; Epidemiology, School of Public Health
    Background: Vaccine hesitancy is the next great barrier for public health. Arab Americans are a rapidly growing demographic in the United States with limited information on the prevalence of vaccine hesitancy. We therefore sought to study the attitudes towards the coronavirus disease 2019 (COVID-19) vaccine amongst Arab American health professionals living in the United States. Methods: This was a cross sectional study utilizing an anonymous online survey. The survey was distributed via e-mail to National Arab American Medical Association members and Arab-American Center for Economic and Social Services healthcare employees. Respondents were considered vaccine hesitant if they selected responses other than a willingness to receive the COVID-19 vaccine. Results: A total of 4000 surveys were sent via e-mail from 28 December 2020 to 31 January 2021, and 513 responses were received. The highest group of respondents were between the ages of 18-29 years and physicians constituted 48% of the respondents. On multivariable analysis, we found that respondents who had declined an influenza vaccine in the preceding 5 years (p < 0.001) and allied health professionals (medical assistants, hospital administrators, case managers, researchers, scribes, pharmacists, dieticians and social workers) were more likely to be vaccine hesitant (p = 0.025). In addition, respondents earning over $150,000 US dollars annually were less likely to be vaccine hesitant and this finding was significant on multivariable analysis (p = 0.011). Conclusions: Vaccine hesitancy among health care providers could have substantial impact on vaccine attitudes of the general population, and such data may help inform vaccine advocacy efforts.
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    Non-compartment model to compartment model pharmacokinetics transformation meta-analysis – a multivariate nonlinear mixed model
    (BMC, 2010-05-28) Wang, Zhiping; Kim, Seongho; Quinney, Sara K.; Zhou, Jihao; Li, Lang; Medicine, School of Medicine
    Background To fulfill the model based drug development, the very first step is usually a model establishment from published literatures. Pharmacokinetics model is the central piece of model based drug development. This paper proposed an important approach to transform published non-compartment model pharmacokinetics (PK) parameters into compartment model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed model. A conditional first-order linearization approach was developed for statistical estimation and inference. Results Using MDZ as an example, we showed that this approach successfully transformed 6 non-compartment model PK parameters from 10 publications into 5 compartment model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed model had little relative bias (<1%) in estimating compartment model PK parameters if all non-compartment PK parameters were reported in every study. If there missing non-compartment PK parameters existed in some published literatures, the relative bias of compartment model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%. Conclusions This non-compartment model PK parameter transformation into compartment model meta-analysis approach possesses valid statistical inference. It can be routinely used for model based drug development.
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    Phosphatidylethanolamines Are Associated with Nonalcoholic Fatty Liver Disease (NAFLD) in Obese Adults and Induce Liver Cell Metabolic Perturbations and Hepatic Stellate Cell Activation
    (MDPI, 2023-01-05) Shama, Samaa; Jang, Hyejeong; Wang, Xiaokun; Zhang, Yang; Shahin, Nancy Nabil; Motawi, Tarek Kamal; Kim, Seongho; Gawrieh, Samer; Liu, Wanqing; Medicine, School of Medicine
    Pathogenesis roles of phospholipids (PLs) in nonalcoholic fatty liver disease (NAFLD) remain incompletely understood. This study investigated the role of PLs in the progression of NAFLD among obese individuals via studying the alterations in serum PL composition throughout the spectrum of disease progression and evaluating the effects of specific phosphatidylethanolamines (PEs) on FLD development in vitro. A total of 203 obese subjects, who were undergoing bariatric surgery, were included in this study. They were histologically classified into 80 controls (C) with normal liver histology, 93 patients with simple hepatic steatosis (SS), 16 with borderline nonalcoholic steatohepatitis (B-NASH) and 14 with progressive NASH (NASH). Serum PLs were profiled by automated electrospray ionization tandem mass spectrometry (ESI-MS/MS). HepG2 (hepatoma cells) and LX2 (immortalized hepatic stellate cells or HSCs) were used to explore the roles of PL in NAFLD/NASH development. Several PLs and their relative ratios were significantly associated with NAFLD progression, especially those involving PE. Incubation of HepG2 cells with two phosphatidylethanolamines (PEs), PE (34:1) and PE (36:2), resulted in significant inhibition of cell proliferation, reduction of mitochondrial mass and membrane potential, induction of lipid accumulation and mitochondrial ROS production. Meanwhile, treatment of LX2 cells with both PEs markedly increased cell activation and migration. These effects were associated with a significant change in the expression levels of genes involved in lipogenesis, lipid oxidation, autophagy, apoptosis, inflammation, and fibrosis. Thus, our study demonstrated that elevated level of PEs increases susceptibility to the disease progression of obesity associated NAFLD, likely through a causal cascade of impacts on the function of different liver cells.
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    Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization
    (Elsevier, 2014-02) Kim, Seongho; Li, Lang; Department of Medical & Molecular Genetics, IU School of Medicine
    The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis-Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model.