2,234 research outputs found

    Dynamic PET-Tau Quantification for Progressive Supranuclear Palsy Diagnosis

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    Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2023-2024. Tutor: Raúl Tudela ; Director: Aida Niñerola, Raúl TudelaTauopathies are neurodegenerative diseases caused by the abnormal accumulation of tau proteins in the brain. One uncommon tauopathy is progressive supranuclear palsy (PSP), whose symptoms often overlap with other brain disorders, and its detection is only possible postmortem since there is not an available ideal biomarker. PET-tau imaging has the potential to revolutionize the early detection of this disease. PET is a nuclear imaging test which allows seeing the functionality of organs and tissues in vivo using a radiotracer that emits radiation from inside the body. A new PET tracer called 18F-PI-2620 has shown promising results concerning the detection of PSP, with high affinity to tau aggregates and low off-target binding. This project consists of designing and testing a software for the quantification of PET images of the brain with a dynamic acquisition, which show the radiotracer distribution through time. The software performs a coregistration of the images to the standard space, where the different regions of the brain can be segmented using an atlas, and provides two physiologically meaningful parameters which are the Distribution Volume Ratio (DVR) and Standardized Uptake Value Ratio (SUVR). It gives out the DVR and SUVR values for any region of interest, as well as parametric images which help visualizing the radiotracer distribution in the brain. A set of brain PET images from 13 subjects acquired using 18F-PI-2620 has been used for the development and testing of the software, divided into healthy controls, subjects with Down syndrome, some of whom have developed Alzheimer’s disease (AD), which also implies a higher amount of abnormal deposited tau proteins. The results have shown higher DVR and SUVR values for several brain regions in those subjects who have developed AD, confirming that they have a higher radiotracer uptake and a greater amount of deposited tau proteins. This proves the correct functionality of the software and its potential as a future tool for detecting tauopathies such as PSP in combination with the radiotracer

    Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review

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    This literature review rigorously examines the growing scientific interest in computational methods for Tissue Engineering and Regenerative Medicine biofabrication, a leading-edge area in biomedical innovation, emphasizing the need for accurate, multi-stage, and multi-component biofabrication process models. The paper presents a comprehensive bibliometric and contextual analysis, followed by a literature review, to shed light on the vast potential of computational methods in this domain. It reveals that most existing methods focus on single biofabrication process stages and components, and there is a significant gap in approaches that utilize accurate models encompassing both biological and technological aspects. This analysis underscores the indispensable role of these methods in understanding and effectively manipulating complex biological systems and the necessity for developing computational methods that span multiple stages and components. The review concludes that such comprehensive computational methods are essential for developing innovative and efficient Tissue Engineering and Regenerative Medicine biofabrication solutions, driving forward advancements in this dynamic and evolving field

    n-vitro time-kill assays and semi-mechanistic pharmacokinetic-pharmacodynamic modeling of a beta-lactam antibiotic combination against enterococcus faecalis: Optimizing dosing regimens for the geriatric population

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    Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation have emerged as pivotal tools in drug development and usage. Such models characterize typical trends in data and quantify the variability in relationships among dose, concentration, and desired effects. For antibacterial applications, models characterizing bacterial growth and antibiotic-induced bacterial killing offer insight into interactions between antibiotics, bacteria, and the host. Simulations from these models predict outcomes for untested scenarios, refine study designs, and optimize dosing regimens. Enterococcus faecalis, a significant opportunistic bacterial pathogen with increasing clinical relevance, is commonly found in the gastrointestinal tract but can lead to severe infection, such as endocarditis. Treatments for E. faecalis endocarditis involves combination antibiotic therapy, such as beta-lactam antibiotics and aminoglycosides. However, due to the toxicity of aminoglycosides, the primary treatment is typically double beta-lactam therapy—ampicillin and ceftriaxone. Eradicating an E. faecalis infection typically requires a lengthy six-week course of antibiotic treatment. However, keeping patients in hospitals for such an extended duration is impractical. Therefore, the objective of this thesis project is to explore the extension of double beta-lactam therapy to outpatient antibiotic treatment (OPAT). This approach is gaining importance due to the rising risks of hospital-acquired infections and escalating healthcare expenses. Leveraging the stability of penicillin G, which can be stored at room temperature for extended periods, makes it a promising candidate for OPAT, offering potential benefits in terms of both efficacy and cost-effectiveness. Despite limited evidence for penicillin G plus ceftriaxone, this research successfully bridges the gap through in-vitro time-kill assays and the subsequent development of a semi-mechanistic model for this antibiotic combination against E. faecalis isolates. This dissertation research evaluated 21 clinical strains of E. faecalis isolated from infected patients\u27 blood, sourced from Mount Sinai Health System and a hospital in Detroit as part of Dr. Jaclyn Cusumano’s American Association of Pharmacists (AACP) new investigator award research project. The first aim was to conduct susceptibility testing on these isolates. This testing played a pivotal role in guiding antibiotic therapy by determining a drug\u27s minimum inhibitory concentration (MIC) for a specific bacterial strain, offering insight into its effectiveness. The project highlights the importance of knowing a patient\u27s strain susceptibility since it influences the dosing regimen or treatment strategy. After susceptibility testing using broth microdilution techniques, strains were categorized as highly susceptible (MIC ≤ 2 μg/ml) or less susceptible (MIC = 4 μg/ml) to penicillin G. The next phase of the project involved in-vitro time-kill assays—a gold standard method for testing antibiotic concentrations and synergy in combination therapies. All 21 patient isolates were tested with penicillin G monotherapy and in combination with ceftriaxone, along with testing ampicillin and ceftriaxone combination therapies for comparison. It was noted that both combinations showed efficacy for strains highly susceptible to penicillin G (MIC ≤ 2 μg/ml), exhibiting bactericidal and synergistic activity. However, both treatments demonstrated poor performance for the less susceptible strains (MIC = 4 μg/ml). This observation focuses on the importance of in-vitro pharmacodynamic studies in understanding antibiotic action dynamics, forming the basis for the semi-mechanistic model. These 24-hour time-kill assays strongly suggested further investigation into the penicillin G and ceftriaxone combination, while considering the differential effects of the combination on more and less susceptible strains. Semi-mechanistic models were created for two out of the twenty-one tested strains, one with high susceptibility and another with lower susceptibility, with the goal of understanding the bacterial growth and drug kill effect in greater detail along with testing different dosing regimens. Following the typical progression of constructing a semi-mechanistic PK-PD model, a bacterial sub-model was created by employing intensive sampling during time-kill assays. This approach enabled the comprehension of the complete bacterial growth dynamics for both strains. By employing non-linear least squares regression within RStudio, the predictive model was effectively fitted to the observed data, providing estimates of essential bacterial growth parameters. The utilization of the Gompertz growth model yielded a remarkably close match between predicted and observed data, giving confidence in the accuracy of the estimated growth parameters. Subsequently, the focus shifted to obtaining the most suitable pharmacodynamic (PD) parameters to accurately encapsulate the drug\u27s antibacterial effects. This necessitated the use of a mathematical model. A widely employed model for this purpose is the Sigmoidal Emax model—an empirical model that is widely published. This model emerged as a valuable tool for formalizing the interpretation of experimental data and understanding the influence of altering penicillin G concentrations, both individually and in conjunction with ceftriaxone. Leveraging the data analysis capacity of RStudio, nonlinear least squares regression analysis was used to intricately fit the sigmoidal Emax equation to the observed data. This led to obtaining vital parameters, including Emax (maximum effect), EC50 (half-maximal effective concentration), and the sigmoidicity factor. Subsequent evaluation of goodness of fit based visual predictive checks and low standard errors in estimated parameters confirmed the favorable alignment between the predicted model and observed data. Physiologically based pharmacokinetic (PBPK) modeling and simulation stands as a well-established approach that bridges insights from preclinical studies to clinical outcomes. By combining drug-specific information with a comprehensive understanding of physiological and biological processes at the organism level, PBPK models mechanistically depict the behavior of drugs within biological systems. This enables the a priori simulation of drug concentration-time profiles. What distinguishes PBPK modeling is its unique capability to account for physiological variations within specific populations, offering predictive insights into pharmacokinetics tailored to those groups. This thesis project ventured into two vital applications of PBPK models: extrapolating novel clinical scenarios and exploring pharmacokinetics in special populations, particularly the geriatric demographic. With the aim of comprehending the pharmacokinetics of penicillin G and ceftriaxone, the project leveraged the Simcyp® Simulator, a modeling and simulation tool that is widely used in drug development. This platform pools the anatomical, physiological, drug-related, and trial design parameters to generate plasma drug concentration profiles. The simulated concentrations were compared against published data, with the fold error—a ratio of simulated to observed values—serving as a benchmark for model accuracy. Typically, predictions within a fold error range of 0.5 to 2 are deemed acceptable. Upon verification within the healthy population, the models were extended to geriatric subjects utilizing the Simcyp® population library. The same fold error criteria were applied, and the models adeptly predicted concentrations across both young and elderly populations. Remarkable differences in pharmacokinetics were seen in the geriatric cohort compared to a young adult population. Notably, for penicillin G, the AUC increased by 46% in the elderly due to an almost 47% decline in total clearance, stemming from a 49% reduction in glomerular filtration rate (GFR). Further expanding the PBPK model for penicillin G, the inclusion of a pharmacodynamic (PD) component led to the final goal of this project. Lua scripting in Simcyp® was utilized to build the PD model. This model used an equation that combined the bacterial growth model with the drug\u27s inhibitory effect via the Emax model. The impacts of monotherapy and combination were explored through the modulation of PD parameters. Consequently, when co-administered with ceftriaxone, kill rates for penicillin G increased, and IC50 values decreased, indicative of ceftriaxone\u27s augmentative effect. The free (unbound) plasma concentration-time profile from the developed PBPK model was linked as input to the PD model, facilitating testing and simulation of diverse penicillin G dosing regimens. Notably, penicillin G, a time-dependent beta-lactam antibiotic, exhibited a strong correlation with the PK/PD index %fT\u3eMIC (% of the dosing interval with a free concentration above MIC). This was especially pertinent for high-susceptibility strains, wherein continuous infusion of penicillin G led to the most significant reduction in bacterial density, irrespective of combination therapy or monotherapy. However, for low-susceptibility strains, the scenario differed, revealing that reliance on a single PK/PD index is not all-encompassing. For the geriatric population, the PBPK-PD model aligned with literature-backed dosing modifications for penicillin G. For highly susceptible strains, increasing the dosing interval or reducing the dose resulted in comparable reductions in bacterial density. Conversely, in low7 susceptibility strains, even an increase in AUC within the geriatric demographic failed to eradicate the bacteria. In summary, this comprehensive thesis journey navigates through the in-vitro bacterial studies and pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation. This project sheds light on the ability to integrate in-vitro data with PBPK models which not only predict untested scenarios but also help dosing strategies. Overall, by addressing the clinical challenge of E. faecalis infections, the project showcased the extension of double beta-lactam therapy to penicillin G and ceftriaxone combination through a stepwise development of semi-mechanistic PK/PD model

    Placental origins of health & disease:Therapeutic opportunities

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    A clinical decision support system for detecting and mitigating potentially inappropriate medications

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    Background: Medication errors are a leading cause of preventable harm to patients. In older adults, the impact of ageing on the therapeutic effectiveness and safety of drugs is a significant concern, especially for those over 65. Consequently, certain medications called Potentially Inappropriate Medications (PIMs) can be dangerous in the elderly and should be avoided. Tackling PIMs by health professionals and patients can be time-consuming and error-prone, as the criteria underlying the definition of PIMs are complex and subject to frequent updates. Moreover, the criteria are not available in a representation that health systems can interpret and reason with directly. Objectives: This thesis aims to demonstrate the feasibility of using an ontology/rule-based approach in a clinical knowledge base to identify potentially inappropriate medication(PIM). In addition, how constraint solvers can be used effectively to suggest alternative medications and administration schedules to solve or minimise PIM undesirable side effects. Methodology: To address these objectives, we propose a novel integrated approach using formal rules to represent the PIMs criteria and inference engines to perform the reasoning presented in the context of a Clinical Decision Support System (CDSS). The approach aims to detect, solve, or minimise undesirable side-effects of PIMs through an ontology (knowledge base) and inference engines incorporating multiple reasoning approaches. Contributions: The main contribution lies in the framework to formalise PIMs, including the steps required to define guideline requisites to create inference rules to detect and propose alternative drugs to inappropriate medications. No formalisation of the selected guideline (Beers Criteria) can be found in the literature, and hence, this thesis provides a novel ontology for it. Moreover, our process of minimising undesirable side effects offers a novel approach that enhances and optimises the drug rescheduling process, providing a more accurate way to minimise the effect of drug interactions in clinical practice

    Placental origins of health & disease:Therapeutic opportunities

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    An in-depth investigation of five machine learning algorithms for optimizing mixed-asset portfolios including REITs

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    Real estate is a favored investment option as it allows investors to diversify their portfolios and minimize risk. Investors can invest in real estate directly by purchasing a property, or through real estate investment funds (REITs) where they can purchase shares in companies that own and manage real estate. Investing in REITs has become increasingly popular because it eliminates some of the disadvantages associated with direct real estate investment, such as the need for a large upfront payment. When investing in mixed asset portfolios, it is crucial to predict future prices accurately to ensure profitable and less risky asset allocation. However, literature on price prediction often focuses on only one or two algorithms, and there is no research that explores REITs’ price prediction in the context of portfolio optimization. To address this gap, we conducted a thorough evaluation of 5 machine learning algorithms (ML), including Ordinary Least Squares Linear Regression (LR), Support Vector Regression (SVR), k-Nearest Neighbors Regression (KNN), Extreme Gradient Boosting (XGBoost), and Long/Short-Term Memory Neural Networks (LSTM), as well as other financial benchmarks like Holt’s Exponential Smoothing (HES), Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend, and Seasonal Components (TBATS), and Auto-Regression Integrated Moving Average (ARIMA). We applied these algorithms to predict future prices for 30 REITs from the US, UK, and Australia, as well as 30 stocks and 30 bonds. The assets were then used as part of a portfolio, which we optimized using a genetic algorithm. Our results showed that using ML algorithms for price prediction provided at least three times the return over benchmark models and reduced risk by almost two-fold. For REITs, we observed that the use of ML algorithms led to a higher allocation to REITs diversified by country. In particular, our results showed that SVR was the best-performing algorithm in terms of risk-adjusted returns across different time horizons, as confirmed by our Friedman test results (Sharpe ratio). Overall, our study highlights the effectiveness of ML algorithms in predicting asset prices and optimizing portfolio allocation

    Identifying alterations in adipose tissue-derived islet GPCR peptide ligand mRNAs in obesity: implications for islet function

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    In addition to acting as an energy reservoir, white adipose tissue is a vital endocrine organ involved in the modulation of cellular function and the maintenance of metabolic homeostasis through the synthesis and secretion of peptides, known as adipokines. It is known that some of these secretory peptides play important regulatory roles in glycaemic control by acting directly on islet β-cells or on insulin-sensitive tissues. Excess adiposity causes alterations in the circulating levels of some adipokines which, depending on their mode of action, can have pro-inflammatory, pro-diabetic or anti-inflammatory, anti-diabetic properties. Some adipokines that are known to act at β-cells have actions that are transduced by binding to G protein- coupled receptors (GPCRs). This large family of receptors represents ~35% of all current drug targets for the treatment of a wide range of diseases, including type 2 diabetes (T2D). Islets express ~300 GPCRs, yet only one islet GPCR is currently directly targeted for T2D treatment. This deficit represents a therapeutic gap that could be filled by the identification of adipose tissue-derived islet GPCR peptide ligands that increase insulin secretion and overall β-cell function. Thus, by defining their mechanisms of action, there is potential for the development of new pharmacotherapies for T2D. Therefore, this thesis describes experiments which aimed to compare the expression profiles of adipose tissue-derived islet GPCR peptide ligand mRNAs under lean and obese conditions, and to characterise the functional effects of a selected candidate of interest on islet cells. Visceral fat depots were retrieved from high-fat diet-induced and genetically obese mouse models, and from human participants. Fat pads were either processed as whole tissue, or mature adipocyte cells were separated from the stromal vascular fraction (SVF) which contains several other cell populations, including preadipocytes and macrophages. The expression levels of 155 islet GPCR peptide ligand mRNAs in whole adipose tissue or in isolated mature adipocytes were quantified using optimised RNA extraction and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) protocols. Comparisons between lean and obese states in mice models and humans revealed significant modifications in the expression levels of several adipokine mRNAs. As expected, mRNAs encoding the positive control genes, Lep and AdipoQ were quantifiable, with the expression of Lep mRNA increasing and that of AdipoQ mRNA decreasing in obesity. Expression of Ccl4 mRNA, encoding chemokine (C-C motif) ligand 4, was significantly upregulated in whole adipose tissue across all models of obesity compared to their lean counterparts. This coincided with elevated circulating Ccl4 peptide levels. This increase was not replicated in isolated mature adipocytes, indicating that the source of upregulated Ccl4 expression in obesity was the SVF of adipose tissue. Based on this significant increase in Ccl4 mRNA expression within visceral fat and its undetermined effects on β-cell function, Ccl4 was selected for further investigation in MIN6 β-cells and mouse islets. PRESTO-Tango β-arrestin reporter assays were performed to determine which GPCRs were activated by exogenous Ccl4. Experiments using HTLA cells expressing a protease-tagged β- arrestin and transfected with GPCR plasmids of interest indicated that 100ng/mL Ccl4 significantly activated Cxcr1 and Cxcr5, but it was not an agonist at the previously identified Ccl4-target GPCRs Ccr1, Ccr2, Ccr5, Ccr9 and Ackr2. RNA extraction and RT-qPCR experiments using MIN6 β-cells and primary islets from lean mice revealed the expression of Cxcr5 mRNA in mouse islets, but it was absent in MIN6 β-cells. The remaining putative Ccl4 receptors (Ccr1, Ccr2, Ccr5, Ccr9, Cxcr1 and Ackr2) were either absent or present at trace levels in mouse islets and MIN6 β-cells. Recombinant mouse Ccl4 protein was used for functional experiments at concentrations of 5, 10, 50 and 100ng/mL, based on previous reports of biological activities at these concentrations. Trypan blue exclusion testing was initially performed to assess the effect of exogenous Ccl4 on MIN6 β-cell viability and these experiments indicated that all concentrations (5-100ng/mL) were well-tolerated. Since β-cells have a low basal rate of apoptosis, cell death was induced by exposure to the saturated free fatty acid, palmitate, or by a cocktail of pro-inflammatory cytokines (interleukin-1β, tumour necrosis factor-α and interferon-γ). In MIN6 β-cells, Ccl4 demonstrated concentration-dependent protective effects against palmitate-induced and cytokine-induced apoptosis. Conversely, while palmitate and cytokines also increased apoptosis of mouse islets, Ccl4 did not protect islets from either inducer. Quantification of bromodeoxyuridine (BrdU) incorporation into β-cell DNA indicated that Ccl4 caused a concentration-dependent reduction in proliferation of MIN6 β-cells in response to 10% fetal bovine serum (FBS). In contrast, immunohistochemical quantification of Ki67-positive mouse islet β-cells showed no differences in β-cell proliferation between control- and Ccl4-treated islets. Whilst the number of β-cells and δ-cells were unaffected, α- cells were significantly depleted by Ccl4 treatment. Exogenous Ccl4 had no effect on nutrient- stimulated insulin secretion from both MIN6 β-cells and primary mouse islets. The 3T3-L1 preadipocyte cell line was used to assess potential Ccl4-mediated paracrine and/or autocrine signalling within adipose tissue. Ccl4 did not alter the mRNA expression of Pparγ, a master regulator of adipocyte differentiation, but did significantly downregulate the mRNA expression of the crucial adipogenic gene, adiponectin. Oil Red O staining and Western blotting were performed to assess lipid accumulation, and insulin and lipolytic signalling, respectively, and these experiments indicated that the observed Ccl4-induced decrease in adiponectin expression failed to correlate with any changes in adipocyte function. In summary, these data demonstrated anti-apoptotic and anti-proliferative actions of the adipokine, Ccl4, on MIN6 β-cells that were not replicated in mouse islets. The absence of any anti-apoptotic, insulin secretory and/or pro-proliferative effects of Ccl4 in islet β-cells suggests that it is unlikely to play a role in regulating β-cell function via crosstalk between adipose tissue and islets. The divergent functional effects highlight that whilst MIN6 cells are a useful primary β-cell surrogate for some studies, primary islets should always be used to confirm physiological relevance. On the other hand, significant α-cell depletion following Ccl4 treatment suggests a cell-specific function within the islets. Furthermore, Ccl4 impaired adiponectin mRNA expression in adipocytes, although, how adipocyte function is affected as a result requires further investigation. Collectively, these data have contributed increased understanding of the role of obesity in modifying the expression of adipose tissue-derived islet GPCR peptide ligands

    Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

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    As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
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