Thl cloned cell line 28-4 which is an I-A + KLH - specific Th1 type clone of (C57BU6xC 3H) F1 origin was kindly provided by professor Tomio Tada. In these studies, employing these cloned cells, the author investigated both proliferation responses of Thl cells in the presence of various concentrations of cytokines, such as IL-2, IL-4 or IL-6 and proliferation of Thl cells to various concentration of mitogens such as PHA, ConA or PWM. In addition, the author also investigated the proliferation response of Th1 cells to the optimal dose of PHA, ConA or PWM in the presence or absence of above mentioned cytokines. It was found that IL-2, IL-4 or IL-6 alone their growth stimulation degree was dependent on cytokine concentration and that PHA, ConA or PWM stimulated Thl cell proliferation and optimal dose of PHA ConA and PWM was 3 g, 4 g and 2 g per ml, respectively. In addition, proliferation response of Th1 cells to ConA or PWM in the presence of IL-2 was significantly enhanced, but the proliferation response to PHA was not increased significantly. However, IL-4 did not significantly modulate mitogen-activated Thl cell proliferation response. Interestingly, IL-6 decreased PHA- or ConA-activated proliferation of Thl cells, but did not change PWM-activated proliferation. Taken together, these studies strongly suggested that IL-2, IL-4 or IL-6 itself clone stimulated the Thl cell proliferation and that PHA, ConA or PWM also stimulated Thl cell proliferation. In addition, these studies also indicated that IL-2 increased ConA- or PWM-activated Thl cell proliferation, but IL6 inhibited PHA- or ConA-activated Th1 cell proliferation and that IL-4 did not significantly change the mitogen-activated Th1 cell proliferation.
Cell Line ; Cell Proliferation ; Clone Cells ; Cytokines* ; Interleukin-2 ; Interleukin-4 ; Interleukin-6 ; Mitogens ; Th1 Cells*

No abstract available.
Diagnosis*

Background: Recent studies have reported the burden of attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and depressive disorder. Also, there is mounting evidence on the effects of environmental factors, such as ambient air pollution, on these disorders among children and adolescents. However, few studies have evaluated the burden of mental disorders attributable to air pollution exposure in children and adolescents. Methods: We estimated the risk ratios of major mental disorders (ADHD, ASD, and depressive disorder) associated with air pollutants among children and adolescents using time-series data (2011–2019) obtained from a nationwide air pollution monitoring network and healthcare utilization claims data in the Republic of Korea. Based on the estimated risk ratios, we determined the population attributable fraction (PAF) and calculated the medical costs of major mental disorders attributable to air pollution. Results: A total of 33,598 patients were diagnosed with major mental disorders during 9 years. The PAFs for all the major mental disorders were estimated at 6.9% (particulate matter < 10 μm [PM10 ]), 3.7% (PM2.5 ), and 2.2% (sulfur dioxide [SO2 ]). The PAF of PM10 was highest for depressive disorder (9.2%), followed by ASD (8.4%) and ADHD (5.2%). The direct medical costs of all major mental disorders attributable to PM10 and SO2 decreased during the study period. Conclusion This study assessed the burden of major mental disorders attributable to air pollution exposure in children and adolescents. We found that PM10, PM2.5 , and SO2 attributed 7%, 4%, and 2% respectively, to the risk of major mental disorders among children and adolescents.

Background: Recent studies have reported the burden of attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and depressive disorder. Also, there is mounting evidence on the effects of environmental factors, such as ambient air pollution, on these disorders among children and adolescents. However, few studies have evaluated the burden of mental disorders attributable to air pollution exposure in children and adolescents. Methods: We estimated the risk ratios of major mental disorders (ADHD, ASD, and depressive disorder) associated with air pollutants among children and adolescents using time-series data (2011–2019) obtained from a nationwide air pollution monitoring network and healthcare utilization claims data in the Republic of Korea. Based on the estimated risk ratios, we determined the population attributable fraction (PAF) and calculated the medical costs of major mental disorders attributable to air pollution. Results: A total of 33,598 patients were diagnosed with major mental disorders during 9 years. The PAFs for all the major mental disorders were estimated at 6.9% (particulate matter < 10 μm [PM10 ]), 3.7% (PM2.5 ), and 2.2% (sulfur dioxide [SO2 ]). The PAF of PM10 was highest for depressive disorder (9.2%), followed by ASD (8.4%) and ADHD (5.2%). The direct medical costs of all major mental disorders attributable to PM10 and SO2 decreased during the study period. Conclusion This study assessed the burden of major mental disorders attributable to air pollution exposure in children and adolescents. We found that PM10, PM2.5 , and SO2 attributed 7%, 4%, and 2% respectively, to the risk of major mental disorders among children and adolescents.

Background: Recent studies have reported the burden of attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and depressive disorder. Also, there is mounting evidence on the effects of environmental factors, such as ambient air pollution, on these disorders among children and adolescents. However, few studies have evaluated the burden of mental disorders attributable to air pollution exposure in children and adolescents. Methods: We estimated the risk ratios of major mental disorders (ADHD, ASD, and depressive disorder) associated with air pollutants among children and adolescents using time-series data (2011–2019) obtained from a nationwide air pollution monitoring network and healthcare utilization claims data in the Republic of Korea. Based on the estimated risk ratios, we determined the population attributable fraction (PAF) and calculated the medical costs of major mental disorders attributable to air pollution. Results: A total of 33,598 patients were diagnosed with major mental disorders during 9 years. The PAFs for all the major mental disorders were estimated at 6.9% (particulate matter < 10 μm [PM10 ]), 3.7% (PM2.5 ), and 2.2% (sulfur dioxide [SO2 ]). The PAF of PM10 was highest for depressive disorder (9.2%), followed by ASD (8.4%) and ADHD (5.2%). The direct medical costs of all major mental disorders attributable to PM10 and SO2 decreased during the study period. Conclusion This study assessed the burden of major mental disorders attributable to air pollution exposure in children and adolescents. We found that PM10, PM2.5 , and SO2 attributed 7%, 4%, and 2% respectively, to the risk of major mental disorders among children and adolescents.

Background: Recent studies have reported the burden of attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and depressive disorder. Also, there is mounting evidence on the effects of environmental factors, such as ambient air pollution, on these disorders among children and adolescents. However, few studies have evaluated the burden of mental disorders attributable to air pollution exposure in children and adolescents. Methods: We estimated the risk ratios of major mental disorders (ADHD, ASD, and depressive disorder) associated with air pollutants among children and adolescents using time-series data (2011–2019) obtained from a nationwide air pollution monitoring network and healthcare utilization claims data in the Republic of Korea. Based on the estimated risk ratios, we determined the population attributable fraction (PAF) and calculated the medical costs of major mental disorders attributable to air pollution. Results: A total of 33,598 patients were diagnosed with major mental disorders during 9 years. The PAFs for all the major mental disorders were estimated at 6.9% (particulate matter < 10 μm [PM10 ]), 3.7% (PM2.5 ), and 2.2% (sulfur dioxide [SO2 ]). The PAF of PM10 was highest for depressive disorder (9.2%), followed by ASD (8.4%) and ADHD (5.2%). The direct medical costs of all major mental disorders attributable to PM10 and SO2 decreased during the study period. Conclusion This study assessed the burden of major mental disorders attributable to air pollution exposure in children and adolescents. We found that PM10, PM2.5 , and SO2 attributed 7%, 4%, and 2% respectively, to the risk of major mental disorders among children and adolescents.

Autosomal dominant chronic mucocutaneous candidiasis (AD-CMC) is a subtype of CMC caused by gain-of-function (GOF) mutation of the signal transducer and the activator of transcription 1 (STAT1) protein. GOF mutation of STAT1 disrupts Th17 cell differentiation and causes susceptibility to candida infection in mucous membranes. Although genetic testing is crucial to diagnose AD-CMC, a simple and fast diagnostic tool is required for the management and reduction of complications associated with infection. Flow cytometry (FCM) is suggested for the measurement of intracellular phosphorylated STAT1 (pSTAT1) in a stimulated status. Here, we report the application of FCM to show the activation status of STAT signaling in a 24-year-old female patient diagnosed with AD-CMC. Compared to the controls, the patient’s T cells showed increased levels of pSTAT1 after stimulation by interferon-γ and lesser extent of inhibition caused by an inhibitor. To the best of our knowledge, this is the first evaluation of the usefulness of FCM as an alternative diagnostic and monitoring tool of GOF STAT1 in Korea.

Autosomal dominant chronic mucocutaneous candidiasis (AD-CMC) is a subtype of CMC caused by gain-of-function (GOF) mutation of the signal transducer and the activator of transcription 1 (STAT1) protein. GOF mutation of STAT1 disrupts Th17 cell differentiation and causes susceptibility to candida infection in mucous membranes. Although genetic testing is crucial to diagnose AD-CMC, a simple and fast diagnostic tool is required for the management and reduction of complications associated with infection. Flow cytometry (FCM) is suggested for the measurement of intracellular phosphorylated STAT1 (pSTAT1) in a stimulated status. Here, we report the application of FCM to show the activation status of STAT signaling in a 24-year-old female patient diagnosed with AD-CMC. Compared to the controls, the patient’s T cells showed increased levels of pSTAT1 after stimulation by interferon-γ and lesser extent of inhibition caused by an inhibitor. To the best of our knowledge, this is the first evaluation of the usefulness of FCM as an alternative diagnostic and monitoring tool of GOF STAT1 in Korea.

PURPOSE: The purpose of this study is to compare the performance of the T1 3D subtraction technique and the conventional 2D dynamic contrast enhancement (DCE) technique in diagnosing Cushing's disease. MATERIALS AND METHODS: Twelve patients with clinically and biochemically proven Cushing's disease were included in the study. In addition, 23 patients with a Rathke's cleft cyst (RCC) diagnosed on an MRI with normal pituitary hormone levels were included as a control, to prevent non-blinded positive results. Postcontrast T1 3D fast spin echo (FSE) images were acquired after DCE images in 3T MRI and image subtraction of pre- and postcontrast T1 3D FSE images were performed. Inter-observer agreement, interpretation time, multiobserver receiver operating characteristic (ROC), and net benefit analyses were performed to compare 2D DCE and T1 3D subtraction techniques. RESULTS: Inter-observer agreement for a visual scale of contrast enhancement was poor in DCE (κ = 0.57) and good in T1 3D subtraction images (κ = 0.75). The time taken for determining contrast-enhancement in pituitary lesions was significantly shorter in the T1 3D subtraction images compared to the DCE sequence (P < 0.05). ROC values demonstrated increased reader confidence range with T1 3D subtraction images (95% confidence interval [CI]: 0.94–1.00) compared with DCE (95% CI: 0.70–0.92) (P < 0.01). The net benefit effect of T1 3D subtraction images over DCE was 0.34 (95% CI: 0.12–0.56). For Cushing's disease, both reviewers misclassified one case as a nonenhancing lesion on the DCE images, while no cases were misclassified on T1 3D subtraction images. CONCLUSION: The T1 3D subtraction technique shows superior performance for determining the presence of enhancement on pituitary lesions compared with conventional DCE techniques, which may aid in diagnosing Cushing's disease.
Humans ; Magnetic Resonance Imaging ; ROC Curve ; Subtraction Technique