https://scholarworks.unist.ac.kr/handle/201301/95 2026-04-08T22:02:09Z 2026-04-08T22:02:09Z LINA, LAIFA https://scholarworks.unist.ac.kr/handle/201301/91075 2026-03-26T13:15:44Z 2026-01-31T15:00:00Z

Title: Integrating Population Pharmacokinetic Modeling and a Genome-Wide Association Study of Sulindac in Pregnancy: An Exploratory Study in a Small Cohort Author(s): LINA, LAIFA Abstract: Sulindac is an anti-inflammatory drug (NSAID) that can be used as a second line tocolytic therapy in pregnant women facing the risk of preterm labor, particularly when first-line intravenous treatment fail to produce effective response. Although sulindac is used in clinical practice, we still don’t fully understand why it behaves differently from one person to another, especially in pregnant women, whose bodies undergo major changes that can affect how drugs are absorbed, distributed, and eliminated. This variation is also possible to be caused by genetic differences. We believe that understanding these variations is not just a scientific curiosity, it is key to finding the right dose for each patient , reducing the risk of side effects and increasing efficacy. The two primary objectives of our study were to come up with a population pharmacokinetic (popPK) model of sulindac and its two metabolites (sulfide and sulfone) in pregnant women with preterm labor and also to investigate the hypothesis of genetic variation that may alter individual PK parameters in a population genome-wide association (GWAS). In the initial phase of the study, we developed a nonlinear mixed-effects (popPK) model using Monolix software which incorporates the SAEM (Stochastic Approximation Expectation-Maximization) algorithm to ensure robust and reliable convergence. The modeling was based on a one-compartment model in which the absorption follows a first order with the conversion of sulindac to its metabolites by a parent-metabolite structure. Several (popPK) models were tested, and the most appropriate model was chosen based on diagnostic measures including goodness-of-fit plots, shrinkage, relative standard error (RSE), visual predictive check (VPC) and bootstrap analysis. Following this, the second part of our research involved extracting individual empirical Bayes estimates (EBEs) of eight important pharmacokinetic parameters [Tlag, Ka, V/F, Cl1, Kred, Kre-ox, Kox, Cl2] from the final model. These parameters were treated as quantitative traits for association analysis. Association tests between single nucleotide polymorphisms (SNPs) and PK parameters were performed using a generalized linear regression model (GLM) under an additive allelic effect, implemented in PLINK v2.0.0, where maternal age, gestational age, weight and the first 10 PCs were incorporated in the model to account for potential hidden structure. Following the completion of QC, The analysis identified several SNPs with suggestive associations to model-derived PK parameters, highlighting the potential value of this approach. Through integrating pharmacokinetics and genomics, our study holds the hope of contributing to the transformation of pharmacological research and the improvement of individualized therapies. Major: Graduate School of Health Science and Technology Health Science and Technology

2026-01-31T15:00:00Z Hikmawati, Nurul https://scholarworks.unist.ac.kr/handle/201301/91074 2026-03-26T13:15:43Z 2026-01-31T15:00:00Z

Title: Digital Health Transformation in Indonesia: SATUSEHAT Platform and AI-Based Medical Device Regulation Author(s): Hikmawati, Nurul Abstract: Indonesia’s fragmented healthcare system, stretched across more than 17,000 islands, faces persistent challenges in access, data continuity, and service quality, which were further exposed during the COVID-19 pandemic. In response, the government introduced a national Digital Health Transformation Blueprint and the SATUSEHAT platform to standardize and integrate health data, supported by the citizen-facing SATUSEHAT Mobile application that enables access to personal health records, self-monitoring, and navigation of health services. This thesis analyzes the architecture, governance, and effectiveness of SATUSEHAT as the backbone of Indonesia’s digital health ecosystem, alongside the regulatory framework for AI-based medical software, including risk-based device classification, marketing authorization (IDAK), and Domestic Component Level (TKDN) local-content requirements that shape market access for foreign and domestic companies. Using document analysis of national policies, regulatory guidelines, and recent empirical studies, the research maps key stakeholders, evaluates the completeness and implementation of regulations, and identifies bottlenecks such as interoperability constraints, usability issues in SATUSEHAT Mobile, regulatory capacity limits, and the tension between TKDN targets and globalized supply chains. The findings show that Indonesia has established a comparatively comprehensive legal and technical scaffold for digital health and AI-enabled medical devices, but still needs clearer AI-specific operational standards, improved user experience, and more flexible localization mechanisms to fully unlock innovation and equitable access. The thesis proposes policy and industry recommendations to strengthen regulatory capacity, refine TKDN application for software-based technologies, and support collaborative innovation models that can accelerate safe, inclusive digital health transformation in Indonesia. Major: Graduate School of Health Science and Technology Health Science and Technology

2026-01-31T15:00:00Z Heo, Jeong in https://scholarworks.unist.ac.kr/handle/201301/91073 2026-03-26T13:15:42Z 2026-01-31T15:00:00Z

Title: Toward Optimized Mobile EMI for Managing Mild Depression and Anxiety in University Students Author(s): Heo, Jeong in Abstract: This study investigates the efficacy of Ecological Momentary Intervention (EMI) and the determi- nants of user engagement among university students, a population increasingly vulnerable to mental health challenges such as depression and anxiety. Given the limitations of static mobile interventions in addressing the fluctuating psychological states of users, we conducted a six week Micro-Randomized Trial (MRT) involving 215 students with mild to moderate symptoms. Analysis using Generalized Estimating Equations (GEE) revealed that EMI exerted a significant proximal causal effect on reducing stress levels the following day (β = −0.080, p = .020). While im- mediate effects on anxiety were not statistically significant, a cumulative learning effect was observed, where the reduction in anxiety became more pronounced as the number of interventions increased (β =−0.056, p = .008). Furthermore, to understand engagement dynamics, a two-step residual analysis integrating Generalized Additive Mixed Models (GAMM) and LightGBM was employed. The results indicated that even after controlling for stable individual traits, within-person contextual factors, includ- ing specific delivery times (lunch and dinner), previous day engagement (inertia), and recent symptom variability (high fluctuations in stress and anxiety), significantly predicted intervention adherence. These findings provide robust empirical evidence that EMI is an effective tool for immediate stress alleviation and long-term anxiety management. Moreover, the dynamic nature of engagement highlights the necessity of transitioning from traditional, uniform delivery methods to Just-In-Time Adaptive In- terventions (JITAI) that optimize intervention timing and content based on a users real-time state and context. Major: Graduate School of Health Science and Technology Health Science and Technology

2026-01-31T15:00:00Z Lee, Jiyeon https://scholarworks.unist.ac.kr/handle/201301/91072 2026-03-26T13:15:41Z 2026-01-31T15:00:00Z

Title: Enhanced Melanocyte Lineage Reprogramming from Human Fibroblasts via Small Molecule Combination Author(s): Lee, Jiyeon Abstract: Epidermal melanocytes play a crucial role in protecting the skin against ultraviolet radiation by producing a pigment called melanin. Current research highlights that the generation of melanocytes is essential for investigating melanocyte development, pigmentation diseases, and novel therapeutics. However, not only does isolation of melanocytes from adult skin encounter technical challenges, but also amplification of primary melanocyte culture systems. To overcome these limitations, differentiation of melanocytes from pluripotent stem cells (PSCs) has been established; however, safety issues, such as the risk of tumorigenesis, arise as another obstacle. Direct conversion offers a promising strategy for generating target cell types from somatic cells by ectopically overexpressing defined transcription factors. A previous study has demonstrated successful direct conversion of melanocytes, but with relatively low efficacy. In this study, we employ two transcription factors, SOX10 and MITF, with two small molecules and convert human fibroblasts into cells with a melanocyte-like phenotype. Induced melanocyte-like cells (iMeLs) adopt the molecular features of melanocytes, confirmed by morphology, gene expression, and functional profiles. Our findings demonstrate that direct melanocyte lineage conversion using two transcription factors and two small molecules has improved the efficiency and functionality of generating iMeLs. This provides an efficient and time-saving strategy for further studies of melanocyte differentiation mechanisms and potential clinical applications for pigmentation disorders. Major: Graduate School of Health Science and Technology Health Science and Technology

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