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This study examines the effects of social networks on the disclosure of stigmatizing and traumatic sexual assault experiences. We analyzed publicly archived oral histories of Korean “comfort women” from World War II, employing an innovative method combining word embedding analysis, word frequency comparison, and grounded theory. By extracting their significant social relationships from narrated survivor stories, we parsed two distinctive disclosure patterns according to timing of disclosure: early disclosers and late disclosers. The latter were more socially embedded than the former, indicating the constraining aspect of social networks, in which the size of social networks was positively associated with delayed disclosure. Qualitative findings further elaborated that social networks have double-edged effects. Survivors’ familial networks functioned as both social constraints and social support for public disclosure. Yet, the late disclosers tend to exploit it more as constraints for the fear of transgenerational transmission of social scorn and stigma. The findings contribute to enhancing a culturally relevant understanding of trauma and the repercussions of human trafficking.

The stroke incidence has increased rapidly in South Korea, calling for a national-wide system for long-term stroke management. We investigated the effects of socioeconomic status (SES) and geographic factors on chronic phase survival after stroke. We retrospectively enrolled 6994 patients who experienced a stroke event in 2009 from the Korean National Health Insurance database. We followed them up from 24 to 120 months after stroke onset. The endpoint was all-cause mortality. We defined SES using a medical-aid group and four groups divided by health insurance premium quartiles. Geographic factors were defined using Model 1 (capital, metropolitan, city, and county) and Model 2 (with or without university hospitals). The higher the insurance premium, the higher the survival rate tended to be (P P P = 0.006). Cox regression revealed that the medical-aid group was identified as an independent risk factor for chronic phase mortality. Further, NHIP level had a more significant effect than geographic factors on chronic stroke mortality. From these results, long-term nationwide efforts to reduce inter-regional as well as SES discrepancies affecting stroke management are needed.

This study merges multiple COVID-19 data sources from news articles and social media to propose an integrated infodemic surveillance system (IISS) that implements infodemiology for a well-tailored epidemic management policy. IISS is an à-la-carte infodemic surveillance solution that enables users to gauge the epidemic related consensus, which compiles epidemic-related data from multiple sources and equipped with various methodological toolkits - topic modeling, Word2Vec, and social network analysis. IISS can provide reliable empirical evidence for proper policymaking. We demonstrate the heuristic utilities of IISS using empirical data from the first wave of COVID-19 in South Korea. Measuring discourse congruence allows us to gauge the distance between the discourse corpus from different sources, which can highlight consensus and conflicts in epidemic discourse. Furthermore, IISS detects discrepancies between social concerns and main actors.

This study merges multiple COVID-19 data sources from news articles and social media to propose an integrated infodemic surveillance system (IISS) that implements infodemiology for a well-tailored epidemic management policy. IISS is an à-la-carte infodemic surveillance solution that enables users to gauge the epidemic related consensus, which compiles epidemic-related data from multiple sources and equipped with various methodological toolkits – topic modeling, Word2Vec, and social network analysis. IISS can provide reliable empirical evidence for proper policymaking. We demonstrate the heuristic utilities of IISS using empirical data from the first wave of COVID-19 in South Korea. Measuring discourse congruence allows us to gauge the distance between the discourse corpus from different sources, which can highlight consensus and conflicts in epidemic discourse. Furthermore, IISS detects discrepancies between social concerns and main actors.

The field of epidemic modeling has rapidly grown in recent years. However, the studies to date suffer from empirical limitations and lack the multi-layered relational investigations. The case under scrutiny is the Middle East Respiratory Syndrome (MERS) epidemic episode in South Korea 2015. Linking the confirmed MERS patients data with social media data mentioning MERS, we examine the relationship between the epidemic networks (EpiNet) and the corresponding discourse networks on Twitter (TweetNet). Using network analyses and simulations, we unpack the epidemic diffusion process and the epidemic-related social media discourse diffusion. The on-line discourse structure of the infectious is larger, dense and complex than the off-line epidemic diffusion network and it has its own unique grammar. When we differentiated tweets by their user types, we observed that they display distinct temporal and structural patterns. They showed the divergent sensitivities in the spike timing and retweet patterns compared to simulated RandomNet. High self-clustering patterns by governmental and public tweets can hinder efficient communication/information spreading. Epidemic related social media surveillance should pay customized attentions accordingly to different types of users. This study should generate discussion about the feasibility and future of liability of social media based epidemic surveillance.

The increased prevalence and frequency of infectious diseases are alarming with respect to the disproportionate fatalities across different regions, socio-economic conditions, and demographic groups. Combining pathological data, socio-environmental data, and extracted knowledge from white papers, we proposed a Globally Localized Epidemic Knowledge base (GLEK) that can be utilized for efficient and optimal epidemic surveillance. GLEK merges social, environmental, pathological, and governmental intervention data to provide efficient advice for epidemic control and intervention. Heuristically utilizing multi-locus data sources, GLEK can identify the best tailored intervention.

In this paper, we investigate the relationships between dwelling conditions and pediatric asthma prevalence, merging multiple data sources to unpack disproportionate pediatric asthma prevalence in Memphis, TN, USA. Using 32 097 pediatric asthma encounter data from a children’s hospital, we map the patient distributions in Memphis. Connecting these data with 244 000 property quality data and census data, we show that pediatric asthma is disproportionately prevalent in the bad quality neighborhoods, even after controlling for ethnicity and poverty level. Furthermore, we show that the neighborhood inequality has a more negative effect than the overall neighborhood degeneration. Our novel data integration provides a unique opportunity to directly test the relationship between residential qualities and asthma prevalences at the macro-level, and to the best of our knowledge, this study is the first study that investigates the association between the detailed property quality and pediatric asthma with a relatively large sample size. Moreover, directly connecting health outcomes with the detailed housing quality data enables us to unpack how the inequality in living conditions yields disproportionate public health outcome distributions. To further improve public health decision making, the health issue should be approached with a more holistic view with taking into account environmental, residential, and social conditions. Integrating multiple data sources helps us not only discover the hidden links between quality of housing and childhood asthma in an urban community but also provide more efficient health surveillance guidelines to identify the population at risk. We show that neighborhood blight and inequality are closely associated with childhood asthma and other consecutive health problems, and therefore, any effective health intervention should also consider consolidation of housing policies and procedures. In addition, the detailed neighborhood level data helps us to reveal the pathways of social inequalities to health disparities and improve the public health through a properly designed surveillance system.

Chronic diseases and conditions are the leading cause of death and disability in the United States. The number of people living with two or more chronic conditions has increased in the last decades and is expected to continue to rise over the upcoming years. Yet, traditional chronic disease surveillance practices have been specialized for a specific symptom or a single health condition. To better understand the complication and complexity of multimorbidity in chronic diseases, this paper suggests the use of network science for multimorbidity network surveillance (MNS). We discuss why the relational perspective in surveillance is critical and how network science can help and be integrated into surveillance and public health practice.

Adverse Childhood Experiences (ACEs) are negative events or states that affect children, with lasting impacts throughout their adulthood. ACES are considered one of the major risk factors for several adverse health outcomes and are associated with low quality of life and many detrimental social and economic consequences. In order to enact better surveillance of ACEs and their associated conditions, it is instrumental to provide tools to detect, monitor and respond effectively. In this paper, we present a recommender system tasked with simplifying data collection, access, and reasoning related to ACEs. The recommender system uses both semantic and statistical methods to enable content and context-based filtering.

Objective We introduce the Semantic Platform for Adverse Childhood Experiences (ACEs) Surveillance (SPACES). It facilitates the access to the relevant integrated information, enables discovering the causality pathways and assists researchers, clinicians, public health practitioners, social workers, and health organization in studying the ACEs, identifying the trends, as well as planning and implementing preventive and therapeutic strategies. Introduction Adverse Childhood Experiences (ACEs) have been linked to a variety of detrimental health and social outcomes. In the last 20 years, the association between ACEs with several adult health risk behaviors, conditions, and diseases including suicides, and substance abuse [1], mental health disturbances and impaired memory [2], nervous, endocrine and immune systems impairments [3], and criminal activities [4] have been studied. One of the challenges in studying and timely diagnosis of ACEs is that the links between specific childhood experiences and their health outcomes are not totally clear. Similarly, an integrated dataset builtfrom multiple sources is often required for effective ACEs surveillance. The SPACES project aims at providing a semantic infrastructure to facilitate data sharing and integration and answer causal queries [5] to improve ACEs surveillance. Methods We create the ACEs ontology to facilitate the integration of data coming from various distributed sources (e.g. ontologies, databases, surveys, interviews, and literature) and maintain interoperability between the data sources. We re-used some of the existing bio-ontologies in the domain, although they captured the domain’s knowledge in different levels of granularity, e.g., Homeless is defined in SNOMED CT and Medical Dictionary for Regulatory Activities (MedDRA) with different hierarchies, or some related concepts may be disconnected, e.g., SNOMED CT defines Abuse but not Verbal Abuse while MedDRA defines Verbal abuse but not as an Abuse. In order to improve reasoning with the aggregated data, we perform two different kinds of inference. Semantic inference uses the ACEs ontology to creates new conclusions by connecting similar concepts. On the other hand, statistical inference is used to formulate rules that link co-occurring concepts. These two kinds of inference, statistical and semantic, work in tandem and the conclusions from one method can then be used as the basis for the other kind of inference. Results The ACEs Ontology is a unique resource for capturing knowledge in the domain of Adverse Childhood Experiences. The ontology will be openly and freely available via the relevant online community’s portals (e.g. The NCBO BioPortal). The logical validation of the ontology is performed using description logic reasoners. A set of use-case scenarios are designed to demonstrate the feasibility and usability of the ontology for data integration and intelligent query answering. Conclusions In this paper, we present the SPACE project that aims to develop a platform to improve adverse childhood experiences surveillance. The tool uses semantic and statistical methods to improve data access, integration, and reasoning. References [1] Vincent J. Felitti, Robert F. Anda, Dale Nordenberg, David F. Williamson, Alison M. Spitz, Valerie Edwards, Mary P. Koss, and James S. Marks. Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The adverse childhood experiences (ace) study. American Journal of Preventive Medicine, 14(4):245–258, 5 1998. [2] Robert F Anda, Vincent J Felitti, J Douglas Bremner, John D Walker, Charles Whitfield, Bruce D Perry, Shanta R Dube, and Wayne H Giles. The enduring effects of abuse and related adverse experiences in childhood: A convergence of evidence from neurobiology and epidemiology. European archives of psychiatry and clinical neuroscience, 256(3):174–186, 04 2006. [3] Andrea Danese and Bruce S. McEwen. Adverse childhood experiences, allostasis, allostatic load, and age-related disease. Physiology & Behavior, 106(1):29 – 39, 2012. Allostasis and Allostatic Load. [4] James Garbarino. Aces in the criminal justice system. Academic Pediatrics, 17(7, Supplement): S32 – S33, 2017. Child Well-Being and Adverse Childhood Experiences in the US. [5] Okhmatovskaia A, Shaban-Nejad A, Lavigne M, Buckeridge DL. Addressing the challenge of encoding causal epidemiological knowledge in formal ontologies: a practical perspective. Stud Health Technol Inform. 2014; 205:1125-9.

Adverse Childhood Experiences (ACEs) have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond. To better understand the relationship between ACEs and the associated health outcomes and eventually to pan and implement preventive interventions, access to an integrated coherent actionable data set is crucial. In this paper, we introduce a formal reusable ontological framework to capture the knowledge in the domain of Adverse Childhood Experiences to improve ACEs surveillance and response.

BACKGROUND Adverse Childhood Experiences (ACEs), a set of negative events and processes that a person might encounter during childhood and adolescence, have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond. OBJECTIVE To better understand the relationship between ACEs and their relevant risk factors with associated health outcomes and to eventually design and implement preventive interventions, access to an integrated coherent dataset is needed. Therefore, we implemented a formal ontology as a resource to allow the mental health community to facilitate data integration and knowledge modeling and to improve ACEs’ surveillance and research. METHODS We use advanced knowledge representation and semantic Web tools and techniques to implement the ontology. The current implementation of the ontology is expressed in the description logic ALCRIQ(D), a sublogic of Web Ontology Language (OWL 2). RESULTS The ACEs Ontology has been implemented and made available to the mental health community and the public via the BioPortal repository. Moreover, multiple use-case scenarios have been introduced to showcase and evaluate the usability of the ontology in action. The ontology was created to be used by major actors in the ACEs community with different applications, from the diagnosis of individuals and predicting potential negative outcomes that they might encounter to the prevention of ACEs in a population and designing interventions and policies. CONCLUSIONS The ACEs Ontology provides a uniform and reusable semantic network and an integrated knowledge structure for mental health practitioners and researchers to improve ACEs’ surveillance and evaluation.

African American children are more than twice as likely as white American children to die after surgery, and have increased risk for longer hospital stays, post-surgical complications, and higher hospital costs. Prior research into disparities in pediatric surgery outcomes has not considered interactions between patient-level Clinical Risk Factors (CRFs) and population-level Social, Economic, and Environmental Factors (SEEFs) primarily due to the lack of integrated data sets. In this study, we analyze correlations between SEEFs and CRFs and correlations between CRFs and surgery outcomes. We used a dataset from a cohort of 460 surgical cases who underwent surgery at a children's hospital in Memphis, Tennessee in the United States. The analysis was conducted on 23 CRFs, 9 surgery outcomes, and 10 SEEFs and demographic variables. Our results show that population-level SEEFs are significantly associated with both patient-level CRFs and surgery outcomes. These findings may be important in the improved understanding of health disparities in pediatric surgery outcomes.

Objective Asthma is one of the most common chronic childhood diseases in the United States [2, 3]. In addition to its pervasiveness, pediatric asthma shows high sensitivity to the environment. Combining medical-social dataset with machine learning methods we demonstrate how socio-markers play an important role in identifying patients at risk of hospital revisits due to pediatric asthma within a year. Introduction A socio-marker is a measurable indicator of social conditions where a patient is embedded in and exposed to, being analogous with a biomarker indicating the severity or presence of some disease state. Social factors are one of the most clinical health determinants [1], which play a critical role in explaining health outcomes. Socio-markers can help medical practitioners and researchers to reliably identify high-risk individuals in a timely manner. Methods We collected data from three different sources: pediatric asthma encounter records from Jan 1 st , 2016 to Dec 31 st , 2016 at a children’s hospital, the 2010 U.S census data and neighborhood quality survey data by Memphis Property Hub. After merging these datasets we examine the effect of social features in identifying the patients who visited the hospital more than once during the observation period. We only use the first time visit (3,678 cases) to avoid over-counting of the same patients. In addition to demographic features (age, gender, insurance type, and race (African American and White)), we incorporate the social features such as the proportion of individuals living below the federal poverty level, blight prevalence, neighborhood quality, neighborhood quality inequality, trash dumping presence, the broken window pervasiveness within the zip code area of patients’ residence are included. We then implemented a Support Vector Machine (SVM) based classification model using abovementioned 11 social features. The classification outcome is either patient visits the hospital only one-time (class 0) or revisits the hospital within a year (class 1). Among 3,678 unique patients in the dataset, only 823 pediatric patients revisited hospital with asthma. So, to overcome the class imbalance issue, we have used 823 patients’ data (randomly selected in 1,000 iterations) from each class. Further, to avoid overfitting and ensure generalizability, we divided the dataset as training, test, and validation with a proportion of 60%, 20%, and 20%, respectively. The reported test (5-folds cross-validation using training and testing data) and validation accuracy of the SVM method are averaged over 1,000 iterations to avoid sampling error and bias. Results The proposed socio-marker model resulted in an average classification accuracy of 63.70% for the test set and 63.67 % for the validation set. Further, the average specificity (the total true negative cases divided by the sum of true negative and false positive) and sensitivity (the total number of true positive cases divided by the sum of positive predicted cases) is found to be 62.79% and 64.77%, respectively for the test set and 62.79% and 64.83%, respectively for the validation set. Results of this study suggest that socio-marker features that are not directly related to a patient’s medical conditions can still predict whether the patient will come back to the hospital within a year or not with approximately 64% accuracy. Conclusions Bringing the socio-marker features in the surveillance system may ease the burden of detecting the patients at risk of revisiting the hospital. The results should be interpreted with caution because we only used 12-month period of observation and the visit beyond the observation window is not considered. Also the patients may have visited different hospitals which are not captured in the data. References 1. Booske BC, Athens JK, Kindig DA, Park H, Remington PL: Different perspectives for assigning weights to determinants of health. University of Wisconsin: Population Health Institute 2010. 2. Subbarao P, Mandhane PJ, Sears MR: Asthma: epidemiology, etiology and risk factors. Canadian Medical Association Journal 2009, 181(9):E181-E190. 3. Gold DR, Wright R: Population disparities in asthma. Annu Rev Public Health 2005, 26:89-113.

Most pediatric asthma cases occur in complex interdependencies, exhibiting complex manifestation of multiple symptoms. Studying asthma comorbidities can help to better understand the etiology pathway of the disease. Albeit such relations of co-expressed symptoms and their interactions have been highlighted recently, empirical investigation has not been rigorously applied to pediatric asthma cases. In this study, we use computational network modeling and analysis to reveal the links and associations between commonly co-observed diseases/conditions with asthma among children in Memphis, Tennessee. We present a novel method for geo-parsed comorbidity network analysis to show the distinctive patterns of comorbidity networks in urban and suburban areas in Memphis.

The importance of social components of health has been emphasized both in epidemiology and public health. This paper highlights the significant impact of social components on health outcomes in a novel way. Introducing the concept of sociomarkers, which are measurable indicators of social conditions in which a patient is embedded, we employed a machine learning approach that uses both biomarkers and sociomarkers to identify asthma patients at risk of a hospital revisit after an initial visit with an accuracy of 66%. The analysis has been performed over an integrated dataset consisting of individual-level patient information such as gender, race, insurance type, and age, along with ZIP code-level sociomarkers such as poverty level, blight prevalence, and housing quality. Using this uniquely integrated database, we then compare the traditional biomarker-based risk model and the sociomarker-based risk model. A biomarker-based predictive model yields an accuracy of 65% and the sociomarker-based model predicts with an accuracy of 61%. Without knowing specific symptom-related features, the sociomarker-based model can correctly predict two out of three patients at risk. We systematically show that sociomarkers play an important role in predicting health outcomes at the individual level in pediatric asthma cases. Additionally, by merging multiple data sources with detailed neighborhood-level data, we directly measure the importance of residential conditions for predicting individual health outcomes.

Most chronic diseases are a result of a complex web of causative and correlated factors. As a result, effective public health or clinical interventions that intend to generate a sustainable change in these diseases most often use a combination of strategies or programs. To optimize comparative effectiveness evaluations and select the most efficient intervention(s), stakeholders (i.e. public health institutions, policy-makers and advocacy groups, practitioners, insurers, clinicians, and researchers) need access to reliable assessment methods. Building on the theory of Evidence-Based Public Health (EBPH) we introduce a knowledge-based framework for evaluating the consistency and effectiveness of public health programs, interventions, and policies. We use a semantic inference model that assists decision-makers in finding inconsistencies, identifying selection and information biases, and with identifying confounding and hidden dependencies in different public health programs and interventions. The use of formal ontologies for automatic evaluation and assessment of public health programs improves program transparency to stakeholders and decision makers, which in turn increases buy-in and acceptance of methods, connects multiple evaluation activities, and strengthens cost analysis.

The increasing role of Internet and the World Wide Web as a widely accessible health information source creates opportunities, along with challenges, for researchers, healthcare workers and organizations across the globe, enabling them to collect and analyze data to improving patient care, disease surveillance, and delivering online preventive or therapeutic interventions. In this chapter, after reviewing some of the applications of Internet in public health, we analyze the use of online space in clinical trials, experiments, or observations performed in clinical research, in the United States. We provide visual analytics for health data and preliminary findings from a database that systematically comprises clinical trial records. Our study focuses on clinical trial data with an “online” component in its study design. We first parse out historical trajectories of online clinical trials since its first introduction to show the use of the Internet space in health studies.

This cohort study of mother-child pairs from Tennessee investigates whether variations in maternal social networks are associated with cognitive development in children., Key Points Question How are social relationships and structures, such as dyads, families, and neighborhoods, associated with early cognitive development in children? Findings In this cohort study of 1082 mother-child pairs, the mother’s social networks were significantly positively associated with early childhood cognitive development. Being in a large family network was significantly associated with lower cognitive performance. Meaning The findings suggest that maternal social relationships are associated with cognitive development in children and that social relationships beyond the mother-child-father triad are significantly associated with cognitive development., Importance This study examines how different types of social network structures are associated with early cognitive development in children. Objectives To assess how social relationships and structures are associated with early cognitive development and to elucidate whether variations in the mother’s social networks alter a child’s early cognitive development patterns. Design, Setting, and Participants This cohort study used data from 1082 mother-child pairs in the University of Tennessee Health Science Center–Conditions Affecting Neurocognitive Development and Learning and Early Childhood project to examine the association between networks of different levels of complexity (triad, family, and neighborhood) and child cognitive performance after adjustment for the mother’s IQ, birth weight, and age, and the father’s educational level. The final model was adjusted for the household poverty level. Data were collected from December 2006 through January 2014 and analyzed from October through November 2018. Exposures The child-mother relationship, child-mother-father triad, family setting, child’s dwelling network, mother’s social support network, and neighborhood networks. Main Outcomes and Measures Measure of cognitive development of the child using Bayley Scales of Infant Development (BSID) at 2 years of age. Results Of 1082 participants, 544 (50.3%) were males and 703 (65.1%) were African American; the mean (SD) age was 2.08 (0.12) years. Large family size had a negative association with early cognitive development, with a mean 2.21-point decrease in BSID coefficient score (95% CI, 0.40 to 4.02; P = .01). Mother’s social support network size was positively associated early cognitive development, with a mean 0.40-point increase in BSID coefficient score (95% CI, 0.001 to 0.80; P = .05). Knowing many neighbors was not statistically significantly associated with early cognitive development, with a mean 1.39-point increase in BSID coefficient score (95% CI, −0.04 to 2.83; P = .06). Conclusions and Relevance The findings suggest that maternal social relationships are associated with cognitive development in children and that social relationships beyond the mother-child-father triad are significantly associated with children’s cognitive development. This study investigates the environmental influences on child health outcomes and, specifically, how early cognitive development is associated with social networks for the primary caregiver.

Background and objective: The neurotransmitter, 5-hydroxytryptamine, acts as an immunomodulator by stimulating the release of inflammatory cytokines and regulating the function of dendritic cells and monocytes. The 5-hydroxytryptamine receptor 4 (HTR4) gene is located in a region previously linked to an increased risk of asthma and atopy. The aim of this study was to investigate the association between HTR4 and asthma. Methods: Thirty-two single nucleotide polymorphisms (SNP) in HTR4 were investigated by direct sequencing of 24 DNA samples from unrelated Korean subjects. Results: The 32 genetic variants comprised 22 intronic SNP, two SNP in the 3′-untranslated region (exon 7) and eight SNP in the 3′-downstream region. Logistic regression analysis showed that two intronic polymorphisms were significantly associated with the risk of asthma. Two minor HTR4 alleles, +142828G > A and +122769G > A, occurred at significantly higher frequencies in the asthmatic group than in the healthy control group (49.59% vs 42.29%, P = 0.003, and 47.99% vs 40.35%, P = 0.008, respectively), and these differences remained significant after correction for multiple testing (P = 0.05, dominant mode of inheritance; and P = 0.03, dominant mode, respectively). Haplotype analysis revealed three haplotype blocks. The frequency of haplotype 1 in block 2 was significantly higher in asthmatics (P = 0.003, dominant mode), whereas the frequency of haplotype 4 in block 3 was significantly lower in asthmatics (P = 0.0009, dominant mode). Conclusions: SNP and haplotypes of the HTR4 gene were associated with the asthma phenotype and genetic variation of HTR4 may affect susceptibility to the development of asthma.

Bone marrow stroma-derived growth factor (SF20/IL25) regulates proliferation of lymphoid cells. We previously observed the upregulation of SF20/IL25 in alveolar macrophages treated with fine TiO2 particles and in a TiO2 particle-treated animal model. To identify the mechanism behind TiO2 particle-induced expression of SF20/IL25, we examined the dependence of the induction process on reactive oxygen species and on cytokine-mediated signaling transduction. For in vivo studies, TiO2 particles were intratracheally instilled in Sprague-Dawley rats. For in vitro studies, alveolar macrophages obtained from rat bronchoalveolar lavage cells were cultured in the presence of TiO2 particles. The antioxidant N-acetyl-L-cysteine (NAC) was used to block the formation of reactive oxygen species both in vivo and in vitro, and TPCK, SB203580, and GF10923X were used to inhibit signal transduction mediated by nuclear factor (NF)-κB, p38 MAP kinase, and protein kinase C, respectively, in vitro. In TiO2 particle-treated rats, intraperitoneal administration of NAC significantly decreased lung inflammation and attenuated the production of SF20/IL25 mRNA and protein. In TiO2 particle-stimulated alveolar macrophages, the upregulation of SF20/IL25 mRNA expression was abolished by NAC in a dose-dependent manner. The expression of SF20/IL25 mRNA in the stimulated macrophages was dose-dependently attenuated by TPCK, but not by SB203580 or GF10923X. Fine TiO2 particles stimulate alveolar macrophages to produce SF20/IL25 via the NF-κB-dependent generation of reactive oxygen species.

Summary Background The peroxisome proliferator-activated receptors (PPAR) are the nuclear hormone receptor superfamily of ligand-activated transcriptional factors. PPAR-gamma (PPARG) activation downregulates production of Th2 type cytokines and eosinophil function. Additionally, treatment with a synthetic PPARG ligand can reduce lung inflammation and IFN-gamma, IL-4, and IL-2 production in experimental allergic asthma. In patients with asthma, PPARG gene expression is known to be associated with the airway inflammatory and remodeling responses. Thus, genetic variants of PPARG may be associated with the development of asthma. Methods We genotyped two single nucleotide polymorphisms on the PPARG gene, + 34C > G (Pro12Ala) and + 82466C > T (His449His), in Korean subjects (839 subjects with asthma and 449 normal controls). Results Association analysis using logistic regression analysis showed that + 82466C > T and haplotypes 1(CC) and 2(CT) were associated with the development of asthma ( p =0.01–0.04). The frequency of PPARG - ht2 was significantly lower in the patients with asthma compared to the normal controls in codominant and dominant models ( p =0.01, p corr =0.03 and p =0.02, p corr =0.03, respectively). Conversely, the frequency of PPARG - ht1 was significantly higher in the patients with asthma compared to the normal controls in the codominant model [ p =0.04, OR: 1.27 (1.01–1.6)]. In addition, the rare allele frequency of + 82466C > T was significantly lower in patients with asthma in comparison to normal controls in the codominant model (OR: 0.78, p =0.04). Thus, polymorphism of the PPARG gene may be linked to an increased risk of asthma development.

The neurotransmitter, 5-hydroxytryptamine, acts as an immunomodulator by stimulating the release of inflammatory cytokines and regulating the function of dendritic cells and monocytes. The 5-hydroxytryptamine receptor 4 (HTR4) gene is located in a region previously linked to an increased risk of asthma and atopy. The aim of this study was to investigate the association between HTR4 and asthma.Thirty-two single nucleotide polymorphisms (SNP) in HTR4 were investigated by direct sequencing of 24 DNA samples from unrelated Korean subjects.The 32 genetic variants comprised 22 intronic SNP, two SNP in the 3'-untranslated region (exon 7) and eight SNP in the 3'-downstream region. Logistic regression analysis showed that two intronic polymorphisms were significantly associated with the risk of asthma. Two minor HTR4 alleles, +142828GA and +122769GA, occurred at significantly higher frequencies in the asthmatic group than in the healthy control group (49.59% vs 42.29%, P=0.003, and 47.99% vs 40.35%, P=0.008, respectively), and these differences remained significant after correction for multiple testing (P=0.05, dominant mode of inheritance; and P=0.03, dominant mode, respectively). Haplotype analysis revealed three haplotype blocks. The frequency of haplotype 1 in block 2 was significantly higher in asthmatics (P=0.003, dominant mode), whereas the frequency of haplotype 4 in block 3 was significantly lower in asthmatics (P=0.0009, dominant mode).SNP and haplotypes of the HTR4 gene were associated with the asthma phenotype and genetic variation of HTR4 may affect susceptibility to the development of asthma.