405 research outputs found

    RORฮณt-specific transcriptional interactomic inhibition suppresses autoimmunity associated with TH17 cells.

    Get PDF
    The nuclear hormone receptor retinoic acid-related orphan receptor gamma t (RORฮณt) is a transcription factor (TF) specific to TH17 cells that produce interleukin (IL)-17 and have been implicated in a wide range of autoimmunity. Here, we developed a novel therapeutic strategy to modulate the functions of RORฮณt using cell-transducible form of transcription modulation domain of RORฮณt (tRORฮณt-TMD), which can be delivered effectively into the nucleus of cells and into the central nerve system (CNS). tRORฮณt-TMD specifically inhibited TH17-related cytokines induced by RORฮณt, thereby suppressing the differentiation of naรฏve T cells into TH17, but not into TH1, TH2, or Treg cells. tRORฮณt-TMD injected into experimental autoimmune encephalomyelitis (EAE) animal model can be delivered effectively in the splenic CD4(+) T cells and spinal cord-infiltrating CD4(+) T cells, and suppress the functions of TH17 cells. The clinical severity and incidence of EAE were ameliorated by tRORฮณt-TMD in preventive and therapeutic manner, and significant reduction of both infiltrating CD4(+) IL-17(+) T cells and inflammatory cells into the CNS was observed. As a result, the number of spinal cord demyelination was also reduced after tRORฮณt-TMD treatment. With the same proof of concept, tTbet-TMD specifically blocking TH1 differentiation improved the clinical incidence of rheumatoid arthritis (RA). Therefore, tRORฮณt-TMD and tTbet-TMD can be novel therapeutic reagents with the natural specificity for the treatment of inflammatory diseases associated with TH17 or TH1. This strategy can be applied to treat various diseases where a specific transcription factor has a key role in pathogenesis.ope

    ๋น„๊ฐ€์šฐ์‹œ์•ˆ ์žก์Œ ์˜์ƒ ๋ณต์›์„ ์œ„ํ•œ ๊ทธ๋ฃน ํฌ์†Œ ํ‘œํ˜„

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ˆ˜๋ฆฌ๊ณผํ•™๋ถ€,2020. 2. ๊ฐ•๋ช…์ฃผ.For the image restoration problem, recent variational approaches exploiting nonlocal information of an image have demonstrated significant improvements compared with traditional methods utilizing local features. Hence, we propose two variational models based on the sparse representation of image groups, to recover images with non-Gaussian noise. The proposed models are designed to restore image with Cauchy noise and speckle noise, respectively. To achieve efficient and stable performance, an alternating optimization scheme with a novel initialization technique is used. Experimental results suggest that the proposed methods outperform other methods in terms of both visual perception and numerical indexes.์˜์ƒ ๋ณต์› ๋ฌธ์ œ์—์„œ, ์˜์ƒ์˜ ๋น„๊ตญ์ง€์ ์ธ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•˜๋Š” ์ตœ๊ทผ์˜ ๋‹ค์–‘ํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ ๊ตญ์ง€์ ์ธ ํŠน์„ฑ์„ ํ™œ์šฉํ•˜๋Š” ๊ธฐ์กด ๋ฐฉ๋ฒ•๊ณผ ๋น„๊ตํ•˜์—ฌ ํฌ๊ฒŒ ๊ฐœ์„ ๋˜์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ, ์šฐ๋ฆฌ๋Š” ๋น„๊ฐ€์šฐ์‹œ์•ˆ ์žก์Œ ์˜์ƒ์„ ๋ณต์›ํ•˜๊ธฐ ์œ„ํ•ด ์˜์ƒ ๊ทธ๋ฃน ํฌ์†Œ ํ‘œํ˜„์— ๊ธฐ๋ฐ˜ํ•œ ๋‘ ๊ฐ€์ง€ ๋ณ€๋ถ„๋ฒ•์  ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ๋ชจ๋ธ์€ ๊ฐ๊ฐ ์ฝ”์‹œ ์žก์Œ๊ณผ ์ŠคํŽ™ํด ์žก์Œ ์˜์ƒ์„ ๋ณต์›ํ•˜๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. ํšจ์œจ์ ์ด๊ณ  ์•ˆ์ •์ ์ธ ์„ฑ๋Šฅ์„ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด, ๊ต๋Œ€ ๋ฐฉํ–ฅ ์Šน์ˆ˜๋ฒ•๊ณผ ์ƒˆ๋กœ์šด ์ดˆ๊ธฐํ™” ๊ธฐ์ˆ ์ด ์‚ฌ์šฉ๋œ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์ด ์‹œ๊ฐ์ ์ธ ์ธ์‹๊ณผ ์ˆ˜์น˜์ ์ธ ์ง€ํ‘œ ๋ชจ๋‘์—์„œ ๋‹ค๋ฅธ ๋ฐฉ๋ฒ•๋ณด๋‹ค ์šฐ์ˆ˜ํ•จ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค.1 Introduction 1 2 Preliminaries 5 2.1 Cauchy Noise 5 2.1.1 Introduction 6 2.1.2 Literature Review 7 2.2 Speckle Noise 9 2.2.1 Introduction 10 2.2.2 Literature Review 13 2.3 GSR 15 2.3.1 Group Construction 15 2.3.2 GSR Modeling 16 2.4 ADMM 17 3 Proposed Models 19 3.1 Proposed Model 1: GSRC 19 3.1.1 GSRC Modeling via MAP Estimator 20 3.1.2 Patch Distance for Cauchy Noise 22 3.1.3 The ADMM Algorithm for Solving (3.7) 22 3.1.4 Numerical Experiments 28 3.1.5 Discussion 45 3.2 Proposed Model 2: GSRS 48 3.2.1 GSRS Modeling via MAP Estimator 50 3.2.2 Patch Distance for Speckle Noise 52 3.2.3 The ADMM Algorithm for Solving (3.42) 53 3.2.4 Numerical Experiments 56 3.2.5 Discussion 69 4 Conclusion 74 Abstract (in Korean) 84Docto

    Multivariable index for assessing the activity and predicting all-cause mortality in antineutrophil cytoplasmic antibody-associated vasculitis

    Get PDF
    BACKGROUND: So far, there has been no tool to estimate activity at diagnosis and predict all-cause mortality in patients with ANCA-associated vasculitis (AAV). Hence, we determined the initial predictors of them in patients with AAV. METHODS: We retrospectively reviewed the medical records of 182 patients with AAV. Severe AAV was defined as Birmingham Vasculitis Activity Score (BVAS) โ‰ฅ 16. The cutoffs were extrapolated by the receiver operator characteristic (ROC) curve. The odds ratio (OR) and the relative risk (RR) were assessed using the multivariable logistic regression analysis and the chi-square test, respectively. RESULTS: In the comparison analysis, patients with severe AAV exhibited the higher neutrophil and platelet counts, creatinine, erythrocyte sedimentation rate and C-reactive protein, and the lower lymphocyte count, hemoglobin, and serum albumin than those without. In the multivariable logistic regression analysis, creatinine โ‰ฅ 0.9 mg/dL (OR 2.264), lymphocyte count โ‰ค 1430.0/mm3 (OR 1.856), and hemoglobin โ‰ค 10.8 g/dL (OR 2.085) were associated with severe AAV. We developed a new equation of a multivariable index for AAV (MVIA) = 0.6 ร— (Lymphocyte count โ‰ค 1430.0/mm3 ) + 0.7 ร— (Hemoglobin โ‰ค 10.8 g/dL) + 0.8 ร— (Creatinine โ‰ฅ 0.9 mg/dL). The optimal cutoff of MVIA for severe AAV was set as 1.35. Severe AAV was identified more frequently in patients with MVIA at diagnosis โ‰ฅ1.35 than those without (RR 4.432). Patients with MVIA at diagnosis โ‰ฅ1.35 exhibited the lower cumulative patient survival rate than those without. CONCLUSION: Multivariable index for AAV could assess the cross-sectional activity and predict all-cause mortality in patients with AAV.ope

    Antineutrophil Cytoplasmic Antibody-Associated Vasculitis in Korea: A Narrative Review

    Get PDF
    Antineutrophil cytoplasmic antibody-associated vasculitis (AAV) is a group of systemic necrotising vasculitides, which often involve small vessels, and which lead to few or no immune deposits in affected organs. According to clinical manifestations and pathological features, AAV is classified into three variants: microscopic polyangiitis, granulomatosis with polyangiitis (GPA), and eosinophilic GPA. The American College of Rheumatology 1990 criteria contributed to the classification of AAV, although currently the algorithm suggested by the European Medicines Agency in 2007 and the Chapel Hill Consensus Conference Nomenclature of Vasculitides proposed in 2012 have encouraged physicians to classify AAV patients properly. So far, there have been noticeable advancements in studies on the pathophysiology of AAV and the classification criteria for AAV in Western countries. However, studies analysing clinical features of Korean patients with AAV have only been conducted and reported since 2000. One year-, 5 year-, and 10 year-cumulative patient survival rates are reported as 96.1, 94.8, and 92.8%. Furthermore, initial vasculitis activity, prognostic factor score, age and specific organ-involvement have been found to be associated with either all-cause mortality or poor disease course. The rate of serious infection is 28.6%, and 1 year-, 5 year- and 10 year-cumulative hospitalised infection free survival rates range from 85.1% to 72.7%. The overall standardised incidence ratio of cancer in AAV patients was deemed 1.43 compared to the general Korean population.ope

    Controlling Nutritional Status Score is Associated with All-Cause Mortality in Patients with Antineutrophil Cytoplasmic Antibody-Associated Vasculitis

    Get PDF
    PURPOSE: The controlling nutritional status (CONUT) score was developed to detect undernutrition in patients. Here, we investigated whether the CONUT score estimated at diagnosis could help predict poor outcomes [all-cause mortality, relapse, and end-stage renal disease (ESRD)] of antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). MATERIALS AND METHODS: We retrospectively reviewed and collated data, including baseline characteristics, clinical manifestations (to calculate AAV-specific indices), and laboratory results, from 196 newly diagnosed AAV patients. Serum albumin, peripheral lymphocyte, and total cholesterol levels (at diagnosis) were used to calculate CONUT scores. RESULTS: In total, 111 patients had high CONUT scores (โ‰ฅ3), which showed higher frequency of myeloperoxidase-ANCA and ANCA positivity, and demonstrated higher AAV-specific indices. The optimal cut-offs of CONUT score (at diagnosis) for predicting all-cause mortality and ESRD were โ‰ฅ3.5 and โ‰ฅ2.5, respectively. Patients with CONUT scores higher than the cut-off at diagnosis exhibited lower cumulative and ESRD-free survival rates compared to those with lower scores than the cut-off. In multivariable analyses, diabetes mellitus [hazard ratio (HR): 4.394], five-factor score (HR: 3.051), and CONUT score โ‰ฅ3.5 (HR: 4.307) at diagnosis were independent predictors of all-cause mortality, while only serum creatinine (HR: 1.714) was an independent predictor of ESRD occurrence.ope

    Rituximab Biosimilar Prevents Poor Outcomes of Microscopic Polyangiitis and Granulomatosis with Polyangiitis as Effectively as Rituximab Originator

    Get PDF
    Purpose: There has been no extensive study to compare the efficacy between rituximab originator (Mabtheraยฎ) and its biosimilar (Truximaยฎ) for microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA). Here, we investigated the clinical effects of rituximab on poor outcomes of MPA and GPA in Korean patients, and compared those between Mabtheraยฎ and Truximaยฎ. Materials and methods: We retrospectively reviewed the medical records of a total of 139 patients, including 97 MPA patients and 42 GPA patients. At diagnosis, antineutrophil cytoplasmic antibody positivity and comorbidities were assessed. During follow-up, all-cause mortality, relapse, end-stage renal disease, cerebrovascular accident and acute coronary syndrome were evaluated as poor outcomes. In this study, rituximab was used as either Mabtheraยฎ or Truximaยฎ. Results: The median age at diagnosis was 60.1 years and 46 patients were men (97 MPA and 42 GPA patients). Among poor outcomes, patients receiving rituximab exhibited a significantly lower cumulative relapse-free survival rate compared to those not receiving rituximab (p=0.002). Nevertheless, rituximab use did not make any difference in other poor outcomes of MPA and GPA except for relapse, which might be a rebuttal to the fact that rituximab use after relapse eventually led to better prognosis. There were no significant differences in variables at diagnosis and during follow-up between patients receiving Mabtheraยฎ and those receiving Truximaยฎ. Patients receiving Truximaยฎ exhibited a similar pattern of the cumulative survival rates of each poor outcome to those receiving Mabtheraยฎ. Conclusion: Truximaยฎ prevents poor outcomes of MPA and GPA as effectively as does Mabtheraยฎ.ope

    Total Haemolytic Complement Activity at Diagnosis as an Indicator of the Baseline Activity of Antineutrophil Cytoplasmic Antibody-associated Vasculitis

    Get PDF
    Objective. The total haemolytic complement activity (CH50) assay evaluates the functioning of the complement system. Accumulating evidence indicates that the activation of the complement system plays a critical role in the pathogenesis of antineutrophil cytoplasmic antibody-associated vasculitis (AAV). Therefore, this study aimed to investigate whether CH50 levels at diagnosis could reflect the baseline activity of AAV. Methods. This retrospective study included 101 immunosuppressive drug-naรฏve patients with AAV. At diagnosis, all patients underwent clinical assessments for disease activity, including measurement of the Birmingham Vasculitis Activity Score (BVAS) and Five Factor Score (FFS), and laboratory evaluations, such as tests for CH50, C3, and C4 levels. The association between CH50 levels and disease activity was determined. Results. The median BVAS and FFS at diagnosis were 12.0 and 1.0, respectively, whereas the median CH50 level was 60.4 U/mL. There was a negative correlation between the CH50 level and BVAS (r=โˆ’0.241; p=0.015). A CH50 cut-off value of 62.1 U/mL was used to classify the patients into two groups: patients with CH50 levels ๏ผœ62.1 U/mL (low-CH50 group) and those with CH50 levels โ‰ฅ 62.1 U/mL (high-CH50 group). The low-CH50 group had a higher proportion of patients with high disease activity, based on the BVAS, than the high-CH50 group (52.5% vs. 23.8%, p=0.004). Additionally, the low-CH50 group exhibited a lower relapse- free survival rate than the high-CH50 group; however, this difference was not statistically significant (p=0.082). Conclusion. Low CH50 levels at diagnosis may reflect high baseline activity of AAV.ope

    ์žฌ๋ฌด์„ฑ๊ณผ์™€ ๋น„์žฌ๋ฌด์„ฑ๊ณผ๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ๊ณต๊ธฐ์—…์ •์ฑ…ํ•™๊ณผ, 2023. 8. ๋ฐ•์ƒ์ธ.With the increasing crisis of low birth rates, aging population, and youth unemployment, there is growing social interest in the overall life of young people. In an effort to strengthen the function of overseeing youth policies and to solve youth problems, the government has established a Youth Policy Coordination Office within the Prime Minister's Office and enacted the "Youth Basic Act" to expand and substantiate the foundation for promoting youth policies. While support for young people has increased quantitatively as a result of these efforts, analysis of the results and effectiveness of such support is still lacking. This study aims to empirically analyze the growth and employment effects of promising young startup companies supported by guarantees from the Credit Guarantee Fund. To examine the impact on financial and non-financial performance, we analyzed the relationship between growth, profitability, stability, and employment indicators and the support provided by the Promising Youth Startup Guarantee compared to general guarantees, and derived policy implications. The study analyzed the impact of each guarantee support on the business performance of young (under 39 years old) and startup companies (less than 7 years of experience) receiving guarantee support from the Credit Guarantee Fund between 2016 and 2018, distinguishing between Promising Youth Startup Guarantee and General Guarantee support, from T+1 to T+3. In this study, we aimed to empirically analyze the growth and job creation effects of start-up companies supported by the Credit Guarantee Fund through the Young Entrepreneur Start-up Guarantee. To determine the impact on financial and non-financial performance indicators, we compared the growth, profitability, stability, and employment metrics of start-ups that received the Young Entrepreneur Start-up Guarantee with those of start-ups that received the General Guarantee from 2016 to 2018. We analyzed the effects of each guarantee on the companies' business performance from T+1 to T+3. Regarding the effects of credit guarantee support, we selected sales revenue, sales growth rate, and total asset growth rate as indicators of growth and compared them. Both the Young Entrepreneur Start-up Guarantee and the General Guarantee showed a tendency for sales revenue to increase from T+1 to T+3, but start-ups that received the Young Entrepreneur Start-up Guarantee showed a greater increase. Negative effects were observed in the sales growth rate and total asset growth rate, with ratios decreasing from T+1 to T+3, and the negative impact was greater for start-ups that received the Young Entrepreneur Start-up Guarantee. In terms of profitability indicators, the sales operating profit margin did not produce significant results, and the total asset net profit margin showed a negative impact for the General Guarantee in all periods. In terms of stability indicators, the debt ratio had a negative impact on the General Guarantee, with the supported companies' ratios increasing at T+1. The interest coverage ratio did not produce significant results. Regarding non-financial indicators, we found that the Young Entrepreneur Start-up Guarantee showed an increase in employment from T+2 to T+3. However, as a policy financial instrument, guarantee support should achieve not only simple financial growth but also various non-financial achievements such as enhancing economic vitality, driving innovation growth, creating jobs, and activating investment. Therefore, it is necessary to consider intangible effects such as creating a favorable atmosphere for youth entrepreneurship, increasing employment, and accumulating youth experience. From this perspective, considering that the policy focus from 2016 to 2018 was job creation, it appears that certain policy goals were achieved through the guarantee support examined in this study. Despite this, in order to enhance the financial impact of guarantee support, it appears necessary to expand the screening capabilities to identify promising youth startups with excellent future growth potential. It is necessary to strengthen human and physical capacities, including organization and infrastructure, to enable the selection of companies with promising growth potential through the advanced future growth evaluation system and to support tailored financial solutions for each company. It is also necessary to strengthen continuous post-management after guarantee support. Continuous management, including various management guidance activities such as tax and management consulting, is needed to help young people overcome obstacles more wisely and cleverly. In addition to simple guarantee and loan support, it is also necessary to diversify support measures through the introduction of innovative financial products such as factoring and new forms of investment. This study is significant in that it attempts to analyze the effectiveness of supporting promising youth start-ups with the guarantee system compared to general guarantees, which has recently received a lot of social attention, in a reasonable way. However, the study has limitations in that it had to exclude start-up companies in their early stages from the analysis due to difficulties in calculating ratio indicators, and it could only analyze short-term performance for up to 3 years after support due to limitations in data collection, and it was unable to conduct an analysis of non-quantitative and intangible aspects, as well as considering various external factors such as follow-up investments and private financing. Through this study, we hope for more substantial support and systematic management of promising youth start-ups.์ €์ถœ์‚ฐยท๊ณ ๋ นํ™”์— ๋”ฐ๋ฅธ ๋ถ€์–‘๋น„ ์ฆ๊ฐ€, ์ฒญ๋…„์‹ค์—… ๋“ฑ์˜ ์œ„๊ธฐ์†์—์„œ ์ฒญ๋…„์˜ ์‚ถ ์ „๋ฐ˜์— ๋Œ€ํ•œ ์‚ฌํšŒ์ ์ธ ๊ด€์‹ฌ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ •๋ถ€ ๋˜ํ•œ ์ฒญ๋…„๋ฌธ์ œ ํ•ด๊ฒฐ ๋ฐ ์ฒญ๋…„์ •์ฑ… ์ด๊ด„ ๊ธฐ๋Šฅ ๊ฐ•ํ™”๋ฅผ ์œ„ํ•ด ๊ตญ๋ฌด์กฐ์ •์‹ค ๋‚ด ์ฒญ๋…„์ •์ฑ…์กฐ์ •์‹ค์„ ๋งˆ๋ จํ•˜๊ณ  ใ€Œ์ฒญ๋…„๊ธฐ๋ณธ๋ฒ•ใ€์„ ์ œ์ •ํ•˜๋Š” ๋“ฑ ์ฒญ๋…„์ •์ฑ… ์ถ”์ง„์— ๋Œ€ํ•œ ๊ธฐ๋ฐ˜ํ™•๋Œ€ ๋ฐ ๋‚ด์‹คํ™”๋ฅผ ์œ„ํ•ด ๋…ธ๋ ฅ์„ ์ง€์†ํ•˜๊ณ  ์žˆ๋Š” ์ค‘์ด๋‹ค. ์ด๋Ÿฌํ•œ ๋…ธ๋ ฅ์— ํž˜์ž…์–ด ์ฒญ๋…„์— ๋Œ€ํ•œ ์ง€์›์€ ์–‘์ ์œผ๋กœ ๊ธ‰์ฆํ•˜์˜€์œผ๋‚˜, ์•„์ง ๊ทธ์— ๋Œ€ํ•œ ์„ฑ๊ณผ ๋ฐ ์‹คํšจ์„ฑ์— ๋Œ€ํ•œ ๋ถ„์„์€ ๋ฏธ๋น„ํ•œ ์ƒํ™ฉ์ด๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹ ์šฉ๋ณด์ฆ๊ธฐ๊ธˆ์œผ๋กœ๋ถ€ํ„ฐ ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๊ธฐ์—… ๋ณด์ฆ์„ ์ง€์›๋ฐ›์€ ๊ธฐ์—…์˜ ์„ฑ์žฅ ๋ฐ ๊ณ ์šฉ์ฐฝ์ถœ ํšจ๊ณผ๋ฅผ ์‹ค์ฆ ๋ถ„์„ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์žฌ๋ฌด์  ์„ฑ๊ณผ, ๋น„์žฌ๋ฌด์  ์„ฑ๊ณผ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด, ์„ฑ์žฅ์„ฑ, ์ˆ˜์ต์„ฑ, ์•ˆ์ •์„ฑ, ๊ณ ์šฉ์ธ์› ์ง€ํ‘œ๊ฐ€ ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๋ณด์ฆ์ง€์›๊ณผ ์–ด๋– ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ณด์ด๋Š”์ง€๋ฅผ ์ผ๋ฐ˜๋ณด์ฆ๊ณผ ๋น„๊ตํ•˜์—ฌ ๋ถ„์„ํ•˜๊ณ  ์ •์ฑ…์  ์‹œ์‚ฌ์ ์„ ๋„์ถœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. 2016๋…„๋ถ€ํ„ฐ 2018๋…„๊นŒ์ง€ ์‹ ์šฉ๋ณด์ฆ๊ธฐ๊ธˆ์œผ๋กœ๋ถ€ํ„ฐ ๋ณด์ฆ์ง€์›์„ ๋ฐ›์€ ์ฒญ๋…„(๋งŒ39์„ธ์ดํ•˜)ยท์ฐฝ์—…(์—…๋ ฅ7๋…„๋ฏธ๋งŒ)๊ธฐ์—…์„ ๋Œ€์ƒ์œผ๋กœ ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๊ธฐ์—… ๋ณด์ฆ, ์ผ๋ฐ˜๋ณด์ฆ ์ง€์› ๊ธฐ์—…์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ๊ฐ ๋ณด์ฆ์ง€์›์ด ๊ธฐ์—…์˜ ๊ฒฝ์˜์„ฑ๊ณผ์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋Š”์ง€๋ฅผ T+1๊ธฐ๋ถ€ํ„ฐ T+3๊ธฐ๊นŒ์ง€ ๋ถ„์„ํ•˜์˜€๋‹ค. ์‹ ์šฉ๋ณด์ฆ ์ง€์›ํšจ๊ณผ ์ค‘ ์„ฑ์žฅ์„ฑ ์ง€ํ‘œ๋Š” ๋งค์ถœ์•ก, ๋งค์ถœ์•ก์ฆ๊ฐ€์œจ ๋ฐ ์ด์ž์‚ฐ์ฆ๊ฐ€์œจ์„ ์„ ์ •ํ•˜์—ฌ ๋น„๊ต๋ถ„์„ ํ•˜์˜€๋‹ค. ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๊ธฐ์—… ๋ณด์ฆ๊ณผ ์ผ๋ฐ˜๋ณด์ฆ ์ง€์›๊ธฐ์—… ๋ชจ๋‘ ๋งค์ถœ์•ก์ด T+1๊ธฐ๋ถ€ํ„ฐ T+3๊ธฐ๊นŒ์ง€ ๋ชจ๋‘ ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์ด๋‚˜, ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๊ธฐ์—… ๋ณด์ฆ ์ง€์›๊ธฐ์—…์€ ๋งค์ถœ์•ก ์ฆ๊ฐ€ ํญ์ด ๋” ํฐ ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ๋งค์ถœ์•ก์ฆ๊ฐ€์œจ, ์ด์ž์‚ฐ์ฆ๊ฐ€์œจ์—์„œ๋Š” T+1๊ธฐ๋ถ€ํ„ฐ T+3๊ธฐ๊นŒ์ง€ ๋ชจ๋‘ ๋น„์œจ์ด ๊ฐ์†Œํ•˜๋Š” ๋ถ€์ •์  ํšจ๊ณผ๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ์œผ๋ฉฐ, ๊ทธ ํญ์€ ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๊ธฐ์—… ๋ณด์ฆ์—์„œ ๋” ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ˆ˜์ต์„ฑ ์ง€ํ‘œ์—์„œ๋Š” ๋งค์ถœ์•ก์˜์—…์ด์ต๋ฅ  ์ง€ํ‘œ๋Š” ์œ ์˜๋ฏธํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜์ง€ ๋ชปํ•˜์˜€๊ณ , ์ด์ž์‚ฐ์ˆœ์ด์ต๋ฅ ์€ ์ผ๋ฐ˜๋ณด์ฆ์€ ๋ชจ๋“  ๊ธฐ๊ฐ„์—์„œ ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นœ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์•ˆ์ •์„ฑ ์ง€ํ‘œ์—์„œ ๋ถ€์ฑ„๋น„์œจ์€ T+1๊ธฐ์— ์ผ๋ฐ˜๋ณด์ฆ ์ง€์›๊ธฐ์—…์˜ ๋น„์œจ์ด ์ƒ์Šนํ•˜๋Š” ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ , ์ด์ž๋ณด์ƒ๋ฐฐ์œจ์—์„œ๋Š” ์œ ์˜๋ฏธํ•œ ๋ถ„์„๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜์ง€ ๋ชปํ–ˆ๋‹ค. ๋น„์žฌ๋ฌด์ง€ํ‘œ์—์„œ๋Š” ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๋ณด์ฆ์˜ ๊ฒฝ์šฐ T+2๊ธฐ์™€ T+3๊ธฐ์— ๊ณ ์šฉ์ธ์›์ด ์ฆ๊ฐ€ํ•œ ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‹ค๋งŒ, ์ •์ฑ…๊ธˆ์œต ์ˆ˜๋‹จ์˜ ํ•˜๋‚˜์ธ ๋ณด์ฆ ์ง€์›์€ ๋‹จ์ˆœ ์žฌ๋ฌด์  ์„ฑ์žฅ๋ฟ ์•„๋‹ˆ๋ผ ์ถ”๊ฐ€์ ์œผ๋กœ ๊ฒฝ์ œํ™œ๋ ฅ ์ œ๊ณ , ํ˜์‹ ์„ฑ์žฅ ๊ฒฌ์ธ, ์ผ์ž๋ฆฌ ์ฐฝ์ถœ, ํˆฌ์ž ํ™œ์„ฑํ™” ๋“ฑ ๋‹ค์–‘ํ•œ ๋น„์žฌ๋ฌด์  ์„ฑ๊ณผ๋ฅผ ๋‹ฌ์„ฑํ•ด์•ผ ํ•˜๋ฉฐ, ์ด์— ๋”ฐ๋ผ ์ฒญ๋…„ ์ฐฝ์—… ๋ถ„์œ„๊ธฐ ์กฐ์„ฑ ๋ฐ ๊ณ ์šฉ ์ฆ๋Œ€ ๋ฐ ์ฒญ๋…„๊ฒฝํ—˜ ์ถ•์ ๊ณผ ๊ฐ™์€ ๋ฌดํ˜•์  ํšจ๊ณผ ๋˜ํ•œ ๊ณ ๋ คํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ด€์ ์—์„œ ๋ณธ ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ๋˜๋Š” 2016๋…„์—์„œ 2018๋…„๊นŒ์ง€์˜ ์ •์ฑ… ํ™”๋‘๊ฐ€ ์ผ์ž๋ฆฌ ์ฐฝ์ถœ์ด์—ˆ๋‹ค๋Š” ์ ์„ ๊ฐ์•ˆํ•  ๋•Œ, ํ•ด๋‹น ๋ณด์ฆ์„ ํ†ตํ•ด ์ผ์ •๋ถ€๋ถ„์˜ ์ •์ฑ…๋ชฉํ‘œ๋Š” ๋‹ฌ์„ฑํ•œ ๊ฒƒ์œผ๋กœ ๋ณด์—ฌ์ง„๋‹ค. ๊ทธ๋Ÿผ์—๋„ ๋ณด์ฆ์ง€์›์˜ ์žฌ๋ฌด์  ํšจ๊ณผ๋ฅผ ์ œ๊ณ ํ•˜๊ธฐ ์œ„ํ•ด ๋ฏธ๋ž˜์„ฑ์žฅ์„ฑ์ด ์šฐ์ˆ˜ํ•œ ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๊ธฐ์—…์„ ๋ฐœ๊ตดํ•ด ๋‚ผ ์ˆ˜ ์žˆ๋Š” ์‹ฌ์‚ฌ์—ญ๋Ÿ‰์„ ๋ณด๋‹ค ํ™•์ถฉํ•  ํ•„์š”๊ฐ€ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๋ฏธ๋ž˜์„ฑ์žฅ์„ฑ ํ‰๊ฐ€์ฒด๊ณ„๋ฅผ ๊ณ ๋„ํ™”ํ•˜์—ฌ ์„ฑ์žฅ์„ฑ์ด ์–‘ํ˜ธํ•œ ๊ธฐ์—…์„ ์„ ๋ณ„ํ•˜๊ณ , ๊ธฐ์—…์— ๋งž๋Š” ๋งž์ถคํ˜• ๊ธˆ์œต์„ ์ง€์›ํ•  ์ˆ˜ ์žˆ๋„๋ก ์กฐ์ง๊ณผ ์ธํ”„๋ผ ๋“ฑ์„ ํฌํ•จํ•œ ์ธ์ ยท๋ฌผ์ ์—ญ๋Ÿ‰์„ ํ™•์ถฉํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋ณด์ฆ์ง€์› ์ดํ›„ ์ง€์†์  ์‚ฌํ›„๊ด€๋ฆฌ๋„ ๊ฐ•ํ™”ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ์ฒญ๋…„๋“ค์ด ๊ฒช๊ฒŒ ๋  ๋‚œ๊ด€์„ ๋ณด๋‹ค ์ง€ํ˜œ๋กญ๊ณ  ์Šฌ๊ธฐ๋กญ๊ฒŒ ํ—ค์ณ๋‚˜๊ฐˆ์ˆ˜ ์žˆ๋„๋ก ์„ธ๋ฌดยท๊ฒฝ์˜ ์ปจ์„คํŒ… ๋“ฑ ๋‹ค์–‘ํ•œ ๊ฒฝ์˜์ง€๋„ ํ™œ๋™์„ ํฌํ•จํ•œ ์ง€์†์  ๊ด€๋ฆฌ๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ๋ณด์ด๊ณ , ๋‹จ์ˆœ ๋ณด์ฆ์ง€์› ๋ฐ ๋Œ€์ถœ์ง€์›์˜ ํ˜•ํƒœ๋ฟ ์•„๋‹ˆ๋ผ ํŒฉํ† ๋ง, ์ƒˆ๋กœ์šด ๋ฐฉ์‹์˜ ํˆฌ์ž ๋“ฑ ํ˜์‹ ๊ธˆ์œต์ƒํ’ˆ ๋„์ž…์„ ํ†ตํ•ด ์ง€์›๋ฐฉ์•ˆ์„ ๋‹ค๊ฐํ™”ํ•  ํ•„์š”๋„ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ตœ๊ทผ ์‚ฌํšŒ์  ๊ด€์‹ฌ์ด ๋†’์€ ์ฒญ๋…„์ธต์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๊ธฐ์—…๋ณด์ฆ์˜ ์ง€์›์„ฑ๊ณผ๋ฅผ ์ผ๋ฐ˜๋ณด์ฆ๊ณผ ๋น„๊ตํ•˜์—ฌ ํ•ฉ๋ฆฌ์ ์œผ๋กœ ์ ‘๊ทผ์„ ์‹œ๋„ํ•˜์—ฌ ๋ถ„์„ํ•œ ์ ์— ์˜๋ฏธ๊ฐ€ ์žˆ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‹ค๋งŒ, ๋น„์œจ์ง€ํ‘œ ์‚ฐ์ • ๊ณค๋ž€์œผ๋กœ ์ธํ•ด ์ฐฝ์—…์ดˆ๊ธฐ๊ธฐ์—…๋“ค์„ ๋ถ„์„๋Œ€์ƒ์—์„œ ์ œ์™ธํ•  ์ˆ˜ ๋ฐ–์— ์—†์—ˆ๋˜ ์ ๊ณผ, ์ž๋ฃŒ์ˆ˜์ง‘ ํ•œ๊ณ„๋กœ ์ธํ•ด ์ง€์› ํ›„ 3๋…„๊นŒ์ง€์˜ ๋‹จ๊ธฐ์  ์„ฑ๊ณผ๋งŒ์„ ๋Œ€์ƒ์œผ๋กœ ๋ถ„์„ํ•˜์—ฌ ์žฅ๊ธฐ์ ์ธ ์ธก๋ฉด์—์„œ์˜ ๋ถ„์„์ด ์ด๋ฃจ์–ด์ง€์ง€ ๋ชปํ•œ ์ , ๊ทธ๋ฆฌ๊ณ  ๋น„๊ณ„๋Ÿ‰์ ยท๋ฌดํ˜•์  ์ธก๋ฉด์˜ ๋ถ„์„์„ ์‹ค์‹œํ•˜์ง€ ๋ชปํ•œ ์  ๋ฐ ํ›„์†ํˆฌ์ž, ๋ฏผ๊ฐ„๊ธˆ์œต ๋“ฑ ๋‹ค์–‘ํ•œ ์™ธ์ ์š”์ธ๋“ค์„ ๊ณ ๋ คํ•˜์ง€ ๋ชปํ•œ ์  ๋“ฑ์„ ํ•œ๊ณ„๋กœ ์ œ์‹œํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๊ธฐ์—…์— ๋Œ€ํ•œ ๋ณด๋‹ค ๋‚ด์‹ค์žˆ๋Š” ์ง€์›๊ณผ ์ฒด๊ณ„์  ๊ด€๋ฆฌ๊ฐ€ ์ด๋ฃจ์–ด์ง€๊ธธ ๊ธฐ๋Œ€ํ•œ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 3 ์ œ 2 ์žฅ ์ด๋ก ์  ๋…ผ์˜์™€ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  4 ์ œ 1 ์ ˆ ์ด๋ก ์  ๋…ผ์˜ 4 1. ์ฒญ๋…„์˜ ์ •์˜ 4 2. ์ฒญ๋…„์ •์ฑ…์— ๋Œ€ํ•œ ๋…ผ์˜ 5 3. ์ •์ฑ…๊ธˆ์œต 7 4. ์‹ ์šฉ๋ณด์ฆ 10 5. ์œ ๋ง์ฒญ๋…„์ฐฝ์—…๊ธฐ์—… ๋ณด์ฆ 13 ์ œ 2 ์ ˆ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  15 1. ์‹ ์šฉ๋ณด์ฆ ์ง€์›์— ๋”ฐ๋ฅธ ์žฌ๋ฌด ์„ฑ๊ณผ๋ถ„์„ 15 2. ์‹ ์šฉ๋ณด์ฆ ์ง€์›์— ๋”ฐ๋ฅธ ๊ณ ์šฉ์ฐฝ์ถœ ํšจ๊ณผ 18 3. ์„ ํ–‰์—ฐ๊ตฌ์™€์˜ ์ฐจ๋ณ„์„ฑ 19 ์ œ 3 ์žฅ ์—ฐ๊ตฌ์„ค๊ณ„ 21 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์ ˆ์ฐจ 21 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๋ฌธ์ œ ๋ฐ ๊ฐ€์„ค์„ค์ • 22 1. ์—ฐ๊ตฌ๋ฌธ์ œ 22 2. ๊ฐ€์„ค์„ค์ • 22 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€๊ณผ ๋ณ€์ˆ˜์˜ ์ •์˜ 24 1. ์—ฐ๊ตฌ์˜ ๋ถ„์„ํ‹€ 24 2. ๋ณ€์ˆ˜์˜ ์กฐ์ž‘์  ์ •์˜ 25 ์ œ 4 ์ ˆ ์ž๋ฃŒ์ˆ˜์ง‘ ๋ฐ ๋ถ„์„๋ฐฉ๋ฒ• 29 1. ์ž๋ฃŒ์ˆ˜์ง‘ 29 2. ๋ถ„์„๋ฐฉ๋ฒ• 29 ์ œ 4 ์žฅ ์‹ค์ฆ ๋ถ„์„๊ฒฐ๊ณผ 31 ์ œ 1 ์ ˆ ๋ถ„์„๋Œ€์ƒ ๋ฐ ๊ธฐ์ˆ ํ†ต๊ณ„๋Ÿ‰ ๋ถ„์„ 31 1. ๋ถ„์„๋Œ€์ƒ ์ž๋ฃŒ ๋ฐ ํ‘œ๋ณธ ์ถ”์ถœ 31 2. ๊ธฐ์ˆ ํ†ต๊ณ„๋Ÿ‰ 33 ์ œ 2 ์ ˆ ์ƒ๊ด€๋ถ„์„ ๊ฒฐ๊ณผ 39 ์ œ 3 ์ ˆ ๋‹ค์ค‘ํšŒ๊ท€๋ถ„์„๊ฒฐ๊ณผ 40 1. ์„ฑ์žฅ์„ฑ์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ ๋ถ„์„ 40 2. ์ˆ˜์ต์„ฑ์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ ๋ถ„์„ 44 3. ์•ˆ์ •์„ฑ์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ ๋ถ„์„ 46 4. ๊ณ ์šฉ์ฐฝ์ถœํšจ๊ณผ์— ๋ฏธ์น˜๋Š” ํšจ๊ณผ ๋ถ„์„ 49 ์ œ 5 ์žฅ ๊ฒฐ๋ก  50 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ ์š”์•ฝ 50 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ์‹œ์‚ฌ์  52 ์ œ 3 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ–ฅํ›„๊ณผ์ œ 53 ์ฐธ๊ณ ๋ฌธํ—Œ 55 ๋ถ€ ๋ก 58 Abstract 64์„

    Novel mortality-predicting index at diagnosis can effectively predict all-cause mortality in patients with antineutrophil cytoplasmic antibody-associated vasculitis

    Get PDF
    Background: This study investigated whether the inflammation prognostic index (IPI) and the mortality predicting index (MPI) at diagnosis could predict all-cause mortality in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). Methods: We included 223 AAV patients and reviewed their medical records. Clinical and laboratory data and AAV-specific indices at diagnosis were assessed. The IPI was calculated as neutrophil-to-lymphocyte ratio (NLR) ร— C-reactive protein to albumin ratio (CAR). Here, we newly developed an MPI (NLR ร— CAR ร— monocyte counts). Results: The mean age of 223 patients (122 MPA, 57 GPA and 44 EGPA patients) was 59 years. The rate of mortality was 11.2%. Using the receiver operator characteristic curve for all-cause mortality, the cut-offs were calculated as NLR: 3.22, CAR: 3.25, IPI: 18.53 and MPI: 8367.82. In the univariable Cox hazard analysis, age, gender, smoking history, BVAS, FFS and over the cut-off of each index showed statistical significance. As the indices share at least two mutual variables, the multivariable analysis was conducted four times based on each index. An IPI โ‰ฅ18.53 (HR 3.162) and MPI โ‰ฅ8367.82 (HR 3.356) were significantly associated with all-cause mortality. Conclusions: This study developed a novel indicator, MPI, that uses the existing NLR and CAR indices and proved that it could predict all-cause mortality in AAV patients.ope
    • โ€ฆ
    corecore