46 research outputs found

    Discovery and preclinical evaluation of diacetoxyscirpenol as a HIF-targeting anticancer agent

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์˜๊ณผ๋Œ€ํ•™ ์˜๊ณผํ•™๊ณผ, 2017. 8. ๋ฐ•์ข…์™„.์ €์‚ฐ์†Œ ์œ ๋„์ธ์ž (HIF)๋Š” ์„ธํฌ์˜ ์ €์‚ฐ์†Œ ์ ์‘๊ณผ์ •์—์„œ ํ•ต์‹ฌ์ ์ธ ์—ญํ• ์„ ํ•œ๋‹ค. ํŠนํžˆ ๊ณ ํ˜•์•”์€ ์ €์‚ฐ์†Œ ์ƒํ™ฉ์—์„œ ์•”์„ธํฌ์˜ ์ƒ์กด๊ณผ ํ˜ˆ๊ด€ํ˜•์„ฑ์— ํ•„์š”ํ•œ HIF๋ฅผ ๋‹ค๋Ÿ‰์œผ๋กœ ๋ฐœํ˜„ํ•˜๋ฏ€๋กœ HIF๋ฅผ ํ‘œ์ ์œผ๋กœ ํ•˜๋Š” ์น˜๋ฃŒ์ œ๋ฅผ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” HIF๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ์–ต์ œํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ์•ฝ๋ฌผ์„ ์ฐพ๊ธฐ ์œ„ํ•œ ์‹คํ—˜์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ด 7,280๊ฐœ์˜ ํ™”ํ•ฉ๋ฌผ ํ›„๋ณด๊ตฐ์€ EPO ํ”„๋กœ๋ชจํ„ฐ์™€ Luciferase ํ“จ์ „ ๋ฒกํ„ฐ๋ฅผ ๋ฆฌํฌํ„ฐ๋กœ ๋„ฃ์–ด ์ค€ ๊ฐ„์•”์„ธํฌ์ฃผ์ธ Hep3B๋ฅผ ์ด์šฉํ•˜์—ฌ HIF์˜ ํ™œ์„ฑ๋„๋ฅผ ์ธก์ •ํ•˜์˜€์œผ๋ฉฐ ๊ทธ ์ค‘์—์„œ ์ตœ์ข… ํ›„๋ณด๊ตฐ์„ ์„ ์ •ํ•˜์˜€๋‹ค. ๊ตฌ์กฐ๋ถ„์„์„ ํ†ตํ•ด ์ตœ์ข…ํ›„๋ณด๊ตฐ์œผ๋กœ ์„ ํƒ๋œ ๋‹จ์ผ๋ฌผ์งˆ์ด diacetoxyscirpenol์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. Diacetoxyscirpenol์€ ์—ฌ๋Ÿฌ ์•”์„ธํฌ์—์„œ HIF์˜ ๋‹จ๋ฐฑ์งˆ ๋ฐœํ˜„์„ ์–ต์ œํ•  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, HIF์˜ ํ™œ์„ฑ ๋ฐ HIF์— ์˜ํ•ด ์œ ๋„๋˜๋Š” ํ•˜์œ„์œ ์ „์ž๋“ค์˜ ๋ฐœํ˜„์„ ์–ต์ œํ•˜์˜€๋‹ค. ๋˜ํ•œ ํ˜ˆ๊ด€ํ˜•์„ฑ๊ณผ ์•”์„ธํฌ์˜ ์„ฑ์žฅ์„ ์–ต์ œํ•˜๋ฉฐ ๋™๋ฌผ์‹คํ—˜์—์„œ๋„ ์•”์˜ ์„ฑ์žฅ์„ ํšจ๊ณผ์ ์œผ๋กœ ์–ต์ œํ•˜์˜€๋‹ค. Diacetoxyscirpenol์€ HIF์˜ ํ•ฉ์„ฑ๊ณผ์ •์„ ์–ต์ œํ•˜๋ฉฐ HIF์™€ ARNT์˜ ๊ฒฐํ•ฉ์„ ์–ต์ œํ•จ์œผ๋กœ์„œ ์ €์‚ฐ์†Œ ๊ด€๋ จ์ธ์ž์˜ ๋ฐœํ˜„์„ ์–ต์ œํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋ฅผ ํ†ตํ•ด ๋ณธ ์—ฐ๊ตฌ๋Š” Diacetoxyscirpenol๊ฐ€ ์ €์‚ฐ์†Œ ์กฐ๊ฑด์— ๋…ธ์ถœ๋˜์–ด ์žˆ๋Š” ๊ณ ํ˜•์•”์„ ์น˜๋ฃŒํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ์•ฝ๋ฌผ๋กœ์„œ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค.Hypoxia activates hypoxia-inducible factor 1, which promotes the progression of malignancy by stimulating angiogenesis and by augmenting the ability of tumors to survive. Thus, HIF-1 is one of the most compelling targets for treating cancers. The aim of this study was to find a small molecule that inhibits HIF-1 under hypoxia in cancer cells. 7,280 compounds in a chemical library were tested in a cancer cell line expressing luciferase HIF-dependently. Through three rounds of screening, I finally picked up a compound that originates from a marine bacterium parasitizing red alga. The antibiotic potently inhibited HIF-1 expression and its transcriptional activity in cancer cells exposed to hypoxia. Through two-step fractionation, diacetoxyscirpenol was purified and identified as a HIF-inhibiting ingredient. Mechanistically, diacetoxyscirpenol inhibits the synthesis of HIF-1ฮฑ protein and also interferes with the dimerization of HIF-1ฮฑ and ARNT. It attenuates HIF-mediated gene expression in cancer cells exposed to hypoxia, and by doing so reduces tumorigenic and angiogenic potentials of cancer cells. More importantly, diacetoxyscirpenol retarded tumor growth in mice, and reduced HIF-1ฮฑ expression and vascular formation in the tumors. Overall, diacetoxyscirpenol is considered a potential drug deregulating the HIF-1 signaling pathway, and it could be beneficially employed for treating malignant tumors with hypoxic microenvironment.Introduction 1 Materials and Methods 6 Results 15 Discussion 54 References 59 Abstract in Korean 69Docto

    Molecular mechanism of the activation-induced cell death (AICD) inhibition mediated by a p70 inhibitory killer cell immunoglobulin-like receptor (KIR) in Jurkat T cells

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    Dept. of Medicine/๋ฐ•์‚ฌ[ํ•œ๊ธ€] ์ž์—ฐ์‚ด ์„ธํฌ ๋ฐ ์ผ๋ถ€ CD8^+ T ์„ธํฌ์— ํ‘œํ˜„๋˜๋Š” killer cell immunoglobulin-like receptor (KIR)๋Š” ํ‘œ์  ์„ธํฌ์˜ class I MHC ๋ถ„์ž์™€ ์ ‘ํ•ฉํ•˜์—ฌ ์ž์—ฐ์‚ด ์„ธํฌ ๋˜๋Š” T ์„ธํฌ์˜ ํ™œ์„ฑ์„ ์–ต์ œํ•˜๋Š” ์ˆ˜์šฉ์ฒด์ด๋‹ค. KIR ๋ถ„์ž๋ฅผ ํ‘œํ˜„ํ•˜๋Š” CD8+ T ์„ธํฌ๋Š” ์ฒด๋‚ด์—์„œ memory ์„ธํฌ๋กœ์„œ, oligoclonal ๋˜๋Š” monoclonal expansionํ•˜๋Š” ํŠน์ง•์„ ๊ฐ€์ง„๋‹ค๋Š” ์ ๊ณผ, KIR ํ˜•์งˆ๋ณ€ํ™˜ ๋งˆ์šฐ์Šค์—์„œ KIR ๋ถ„์ž๊ฐ€ CD8^+ T ์„ธํฌ์˜ ์ƒ์กด์„ ๋„์™€์ฃผ์–ด, ๊ฒฐ๊ณผ์ ์œผ๋กœ T ์„ธํฌ๊ฐ€ ํ™œ์„ฑํ™” ์ž๊ทน ํ›„ ์†Œ๋ฉธํ•˜๋Š” ๊ณผ์ •์ธ activation-induced cell death (AICD)๋กœ๋ถ€ํ„ฐ T ์„ธํฌ๋ฅผ ๋ณดํ˜ธํ•œ๋‹ค๋Š” ์ตœ๊ทผ์˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋Š” KIR์™€ T ์„ธํฌ AICD์˜ ๊ด€๋ จ์„ฑ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” KIR ๋ถ„์ž์˜ ํ‘œํ˜„์ด Jurkat T ์„ธํฌ์ฃผ์˜ AICD์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๊ทธ ๊ธฐ์ „์„ ์•Œ์•„ ๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, KIR ๋ถ„์ž์˜ ํ•˜๋‚˜์ธ p70 KIR๋ฅผ ๊ณผ๋ฐœํ˜„์‹œํ‚จ Jurkat ์„ธํฌ ์ฃผ๋Š” ๋ฆฌ๊ฐ„๋“œ ์ ‘์ด‰๊ณผ ๋ฌด๊ด€ํ•˜๊ฒŒ phytohemagglutinin (PHA)์ด๋‚˜ phorbol ester/ionomycin ์ž๊ทน์— ์˜ํ•ด ์œ ๋„๋œ AICD์— ์ €ํ•ญ์„ฑ์„ ๋ณด์˜€์œผ๋ฉฐ, ๊ทธ ๊ธฐ์ „์€ T ์„ธํฌ ์ž๊ทน ํ›„ ์œ ๋„๋˜๋Š” FasL ๋ฐœํ˜„์˜ ์–ต์ œ์— ์˜ํ•จ์„ ๋ฐํ˜”๋‹ค. ๊ทธ๋ฆฌ๊ณ  Jurkat ์„ธํฌ์ฃผ์˜ AICD ์–ต์ œ์— ๊ด€์—ฌํ•˜๋Š” KIR ๋ถ„์ž์˜ ์„ธํฌ ๋‚ด ๋ถ€์œ„์ธ ์„ธํฌ๋ง‰ ์ธ์ ‘ 20๊ฐœ์˜ ์•„๋ฏธ๋…ธ์‚ฐ์— protein kinase C (PKC)์˜ ๊ธฐ์งˆ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ๋Š” serine๊ธฐ๊ฐ€ ์กด์žฌํ•œ๋‹ค๋Š” ์ ์— ์ฐฉ์•ˆํ•˜์—ฌ ํ™œ์„ฑํ™” ์ž๊ทน์ „ํ›„ KIR๋ถ„์ž์™€ PKC์˜ ์ ‘ํ•ฉ ์—ฌ๋ถ€์™€ KIR ํ‘œํ˜„์ด PKC ํ™œ์„ฑ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ณด์•˜๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, PHA์— ์˜ํ•ด ์œ ๋„๋œ PKC(์™€ (์˜ ํ™œ์„ฑ์ด KIR ํ‘œํ˜„ Jurkat T ์„ธํฌ ์ฃผ์—์„œ ํ˜„์ €ํžˆ ์–ต์ œ๋˜์–ด ์žˆ์Œ์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋Œ€์žฅ๊ท ์—์„œ ๋ฐœํ˜„๋œ KIR ์„ธํฌ ๋‚ด ๋ถ€์œ„ ๋‹จ๋ฐฑ์€ in vitro PKC( ํ™œ์„ฑ์„ ํšจ๊ณผ์ ์œผ๋กœ ์–ต์ œํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ, KIR ๋ถ„์ž๊ฐ€ Jurkat ์„ธํฌ์ฃผ์˜ AICD ๊ณผ์ •์„ FasL์˜ ๋ฐœํ˜„ ์–ต์ œ๋ฅผ ํ†ตํ•ด ์ €์ง€ํ•˜๋ฉฐ, ์ด ๊ณผ์ •์€ KIR์˜ ์„ธํฌ๋ง‰ ์ธ์ ‘ PKC ๋ถ€์œ„๋ฅผ ํ†ตํ•œ PKC์˜ ์ง์ ‘์ ์ธ ์–ต์ œ๊ฐ€ ๊ด€์—ฌํ•  ๊ฒƒ์œผ๋กœ ์ถ”์ •๋œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” KIR ๋ถ„์ž๋ฅผ ํ†ตํ•œ ์ƒˆ๋กœ์šด ์‹ ํ˜ธ ์ „๋‹ฌ ๊ธฐ์ „์ด ์กด์žฌํ•จ์„ ๋ฐํ˜”์œผ๋ฉฐ, ์ด๋Š” ์–ต์ œ์„ฑ ์ˆ˜์šฉ์ฒด์ธ KIR์˜ ๋ฉด์—ญํ•™์ ์ธ ์—ญํ• ์„ ์ดํ•ดํ•˜๋Š”๋ฐ ๊ธฐ์—ฌํ•  ๊ฒƒ์œผ๋กœ ์ƒ๊ฐ๋˜๋ฉฐ, ์ •ํ™•ํ•œ ๋ถ„์ž ๊ธฐ์ „์„ ๋ฐํžˆ๊ณ ์ž ํ•˜๋Š” ์ถ”๊ฐ€์˜ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•  ๊ฒƒ์œผ๋กœ ์‚ฌ๋ฃŒ๋œ๋‹ค. -------------------- ํ•ต์‹ฌ๋˜๋Š” ๋ง : KIR, AICD, Jurkat T ์„ธํฌ, PKC, FasL [์˜๋ฌธ] Killer cell immunoglobulin-like receptors (KIRs), expressed on NK cells and a subset of CD8+ T cells, transmit an inhibitory signal upon engagement with specific class I MHC molecules on target cells. CD8^+ T cells expressing KIRs are characterized by memory or effector phenotypes and by monoclonal or oligoclonal expansion patterns. Recent studies with KIR transgenic mice have demonstrated that KIR expression increases the survival of CD8+ T cells by protecting the cells from activation-induced cell death (AICD). In this study, it has been investigated the molecular mechanisms of the AICD inhibition mediated by a p70 KIR in Jurkat T cell. Using stable Jurkat T cell lines expressing a p70 KIR (KIR3DL1, also called NKB1) and CD8-KIR fusion proteins, it was demonstrated for the first time that the p70 KIR inhibited the AICD induced by PHA or PMA/ionomycin in a ligation-independent manner. The AICD inhibition mediated by the 70 KIR appeared to be due to the blockade of FasL induction upon activating the Jurkat transfectants. According to the information that the membrane proximal 20 amino acids of the KIR cytoplasmic tail containing a putative PKC substrate site, play a crucial role in the AICD inhibition of the Jurkat T cells and the p70 KIR constitutively binds to PKC(, a conventional Ca^(2+)-dependent PKC, and PKC(, a novel Ca^(2+)-independent PKC, it was investigated whether the p70 KIR affected PKC activation after PHA stimulation. In vitro kinase assay showed that PKC activation was blocked after PHA stimulation in Jurkat transfectants expressing the p70 KIR. These observations were confirmed by showing that a recombinant KIR cytoplasmic tail also inhibited the PKC( activation in vitro. Taken together, these data strongly suggest that KIR inhibits the AICD of T cells by blocking FasL induction upon stimulation, and this process seems to be accomplished by PKC recruitment to the membrane proximal PKC binding site and subsequent inhibition of PKC activation against activating stimuli.ope

    Current status of treatment of acute respiratory failure in Korea

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    Background: Acute respiratory failure (ARF) is one of the most common causes of intensive care unit (ICU) admission and in-hospital mortality. In South Korea, about 25% of patients admitted to the ICU require mechanical ventilation. The in-hospital mortality rate of these patients is 48%. Respiratory failure can be categorized based on pathophysiologic derangements, and the treatment options vary depending on their classification. This study discusses the status and treatment strategies of patients with ARF in Korea. Current Concepts: The most common treatment for ARF was conventional oxygen therapy, being used at least once in 7.0% of all admitted adult patients and 85.1% of patients admitted with respiratory failure. High-flow oxygen therapy was required in 1.4% of all admissions and 17.2% of respiratory failure-related admissions. High-flow oxygen therapy was attempted in 19.1% of patients who needed invasive mechanical ventilation. Non-invasive positive pressure ventilation (NIV) was used in 0.4% of all admissions and 5.1% of respiratory failure-related admissions. Hypercapnic respiratory failure (57.1%) was the most common reason for NIV use. Invasive mechanical ventilation was required in 2.8% of all admissions and 33.8% of respiratory failure-related admissions. Discussion and Conclusion: Despite its clinical significance, no large-scale studies have been performed on the etiology, treatment, and prognosis of patients with ARF in South Korea. A multicenter or a Korean National Health Insurance Service database study is necessary to accurately identify the characteristics, diagnose problems, and develop treatment guidelines for patients with ARF in South Korea.ope

    Mortality prediction of patients in intensive care units using machine learning algorithms based on electronic health records

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    Improving predictive models for intensive care unit (ICU) inpatients requires a new strategy that periodically includes the latest clinical data and can be updated to reflect local characteristics. We extracted data from all adult patients admitted to the ICUs of two university hospitals with different characteristics from 2006 to 2020, and a total of 85,146 patients were included in this study. Machine learning algorithms were trained to predict in-hospital mortality. The predictive performance of conventional scoring models and machine learning algorithms was assessed by the area under the receiver operating characteristic curve (AUROC). The conventional scoring models had various predictive powers, with the SAPS III (AUROC 0.773 [0.766-0.779] for hospital S) and APACHE III (AUROC 0.803 [0.795-0.810] for hospital G) showing the highest AUROC among them. The best performing machine learning models achieved an AUROC of 0.977 (0.973-0.980) in hospital S and 0.955 (0.950-0.961) in hospital G. The use of ML models in conjunction with conventional scoring systems can provide more useful information for predicting the prognosis of critically ill patients. In this study, we suggest that the predictive model can be made more robust by training with the individual data of each hospital.ope

    Long-Term Clinical Outcomes of Patients with Chronic Obstructive Pulmonary Disease with Sarcopenia

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    Background and objective: Sarcopenia with muscle wasting and weakness is a common occurrence among patients with chronic obstructive pulmonary disease (COPD). We aimed to evaluate the clinical outcomes of sarcopenia in patients with COPD. Methods: We reviewed the electronic medical records of 71 patients with COPD between 1 January 2012, and 31 December 2018. We longitudinally analyzed clinical outcomes in patients with COPD with and without sarcopenia. Results: Compared to the non-sarcopenia group COPD, the sarcopenia group showed a higher rate of acute exacerbation events of COPD (AE COPD, 84.6% vs. 31.0%, p = 0.001), all-cause mortality (30.8% vs. 5.2%, p = 0.022), and pneumonia occurrence per year (median [first quartileโ€“third quartile]; 0.2 [0.0โ€“1.6] vs. 0.0 [0.0โ€“0.2], p = 0.025). Sarcopenia was an independent risk factor for AE COPD in Cox regression analysis (hazard ratio, 5.982; 95% confidence interval, 1.576โ€“22.704). Hand grip strength was associated with the COPD Assessment Test (CAT) score and annual Charlsonโ€™s comorbidity index score change. Total skeletal muscle mass index (SMMI) was associated with the modified medical research council dyspnea scale score, CAT score, body mass index, airflow obstruction, dyspnea, and exercise (BODE) index, and alanine transaminase. Trunk SMMI was significantly associated with AE COPD, while appendicular SMMI was associated with BODE index and annual intensive care unit admissions for AE COPD. Conclusions: Sarcopenia is associated with clinical prognosis, pneumonia occurrence, and the acute exacerbation of COPD requiring intensive care in patients with COPD. Therefore, it is important to carefully monitor sarcopenia development as well as recommend appropriate exercise and nutritional supplementation in patients with COPD.ope

    Accumulation of plasmacytoid dendritic cell is associated with a treatment response to DNA-damaging treatment and favorable prognosis in lung adenocarcinoma

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    Introduction: Favorable responses to the treatment including immune checkpoint inhibitors (ICIs) have been consistently reported in lung cancer with smoking history. As the tumor microenvironment (TME) may be involved in the treatment response to ICIs, we aimed to investigate the TME of lung cancer with different smoking status. Methods: Lung adenocarcinoma (LUAD) tissue (Tu) and adjacent normal?appearing lung tissue (NL) from current and never smokers were investigated by single-cell RNA sequencing and immunofluorescence and immunohistochemical staining. The clinical implications of identified biomarkers were validated using open-source datasets. Results: The lungs of smokers had an increased proportion of innate immune cells in NL tissues, whereas Tu tissues had a lower proportion of these cells than those of non-smokers. Monocyte-derived macrophages (mono-Mc), CD163-LGMN macrophages, monocyte-derived dendritic cells (DCs), and plasmacytoid DCs (pDCs) were significantly enriched in smokersโ€™ Tu. Among these clusters, pDCs, specifically enriched in the Tu of smokers. The expression of representative pDC markers, leukocyte immunoglobulin-like receptor A4 (LILRA4) and Toll-like receptor 9 (TLR9), was increased in the stromal cells of LUAD in patients with a smoking history. In an animal model of lung cancer, ionizing radiation induced robust TLR9 expressing immune cells in peritumoral area. Survival analysis using a TCGA-LUAD dataset indicated that patients overexpressing pDC markers exhibited superior clinical outcomes to age-, sex-, and smoking-matched control groups. Top 25% patients with high TLR9 expression exhibited significantly higher tumor mutational burden than that of low TLR9 expression group (bottom 25% patients) (5.81 mutations/Mb vs 4.36 mutations/Mb; P = 0.0059, Welchโ€™s two-sample t-test) Conclusion: There is an increased pDC in the TME of smokersโ€™ lung cancer, and the response of pDC to DNA damaging treatment would lead a conducive environment to ICIs containing regimens. These findings suggest that R&D that induces an increase in the activated pDC population is continuously required to enhance therapeutic effectiveness of ICIs-containing therapies in lung cancer.ope

    FEF 25-75% Values in Patients with Normal Lung Function Can Predict the Development of Chronic Obstructive Pulmonary Disease

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    Purpose: The forced mid-expiratory flow (FEF25-75%) value is a potentially sensitive marker of obstructive peripheral airflow. We aimed to assess whether FEF25-75% can be an early predictor of chronic obstructive pulmonary disease (COPD). Patients and methods: Between July 1, 2007 and June 31, 2009, we identified 3624 patients who underwent a pulmonary function test (PFT) in Gangnam Severance Hospital. We selected 307 patients aged over 40 years without COPD who had normal PFT results at baseline and who had follow-up PFT records more than 1 year later. A FEF25-75% z-score less than -0.8435 was considered low. We defined COPD as a forced expiratory volume in one second/forced vital capacity value of less than 0.7 before July 31, 2019. Results: Among 307 patients, 91 (29.6%) had low FEF25-75% at baseline. After 10 years, the incidence rate of COPD in the low FEF25-75% group was significantly higher than that in the normal FEF25-75% group (41.8% vs 7.4%; P-value<0.001). The Cox proportional hazard model showed that age (hazard ratio [HR] 1.09; P-value<0.001), smoking status (occasional smoker HR, 4.59; P-value<0.001 and long-term smoker HR, 2.18; P-value=0.023), and low FEF25-75% (HR, 3.31; P-value<0.001) were predictive factors for the development of COPD. Conclusion: The FEF25-75% value in patients with normal lung function is a useful predictor for the development of COPD. We should carefully monitor patients who present with low FEF25-75% values, even if they have normal lung function.ope

    Variable effects of underlying diseases on the prognosis of patients with COVID-19

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    Underlying diseases might be risk factors for poor prognosis in patients with coronavirus disease (COVID-19); however, we still do not know whether these diseases are independent factors affecting prognosis, which type of underlying diseases are risk factors, and which type of clinical outcomes are affected. We retrospectively reviewed cohort data from 7,590 de-identified patients with COVID-19 who were diagnosed using severe acute respiratory syndrome-coronavirus-2 RNA polymerase chain reaction test up to May 15, 2020. We used linked-medical claims data provided by the Health Insurance Review and Assessment Service in South Korea. Underlying diseases were identified using the diagnostic codes in the patients' files from January 1, 2019 to December 31, 2019. The total mortality rate was 3.0% in patients with COVID-19. After adjusting for age, sex, and concomitant chronic conditions, we found that congestive heart failure, chronic pulmonary diseases, diabetes without chronic complications, renal diseases, and malignancy were factors that significantly increased the cost of treatment. Cerebrovascular disease, chronic pulmonary disease, and paralysis were found to be independent factors significant in prolonging hospital stay. Diabetes with chronic complications was independently associated with intensive care unit admission. In addition, underlying congestive heart failure (odds ratio [OR], 1.724; P = 0.003), dementia (OR, 1.598; P = 0.012), diabetes with and without chronic complications (OR, 1.821; P = 0.002 and OR, 1.518; P = 0.022, respectively), renal disease (OR, 2.299; P = 0.002), and malignancy (OR, 1.529; P = 0.039) were significant factors associated with death, even after adjustments. Underlying diseases were significant independent factors of the poor prognosis in patients with COVID-19. The effects were variable according to the type of underlying disease and clinical outcome. Therefore, patients with COVID-19 with underlying diseases should be monitored more closely because they are more at risk of a poor prognosis.ope

    Effects of Asthma Medication Type on Asthma Exacerbation in a Real-World Setting

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    Purpose: Currently, there are multiple options for the pharmacological treatment of asthma. This study aimed to compare the effects of different asthma medications on exacerbation in a real-world setting. Materials and methods: We retrospectively reviewed electronic medical records of asthma patients who visited the hospital from November 1, 2016 to October 31, 2019. The number of asthma exacerbations requiring administration of systemic steroids was the primary outcome. A time-varying Cox regression analysis was used to reflect the real-world setting: variable usage times, discontinuation, and switching of medication. Results: Among 937 patients with asthma, 228 (24.3%) experienced asthma exacerbation during the study period. Asthma exacerbation was observed in patients using short-acting ฮฒ2-agonists (SABA) alone (50.4% vs. 28.6%, p<0.001) as well as in patients not using inhaled corticosteroids (ICS) (58.8% vs. 40.3%, p<0.001), long-acting ฮฒ2-agonists (LABA) (54.8% vs. 36.1%, p<0.001), and leukotriene receptor antagonists (71.5% vs. 50.8%, p<0.001). A time-varying Cox regression analysis of asthma exacerbations according to the duration of asthma medication showed that SABA alone increased the risk of asthma exacerbation [hazard ratio (HR), 1.834; 95% confidence interval (CI), 1.299-2.588; p=0.001], whereas ICS-LABA decreased the risk (HR, 0.733; 95% CI, 0.538-0.997; p=0.048). However, in the subgroup analysis according to medication type, specific ingredients showed no significant differences. Conclusion: In the real world, asthma medications affect asthma exacerbation variably according to the medication type.ope

    Low alanine aminotransferase as a risk factor for chronic obstructive pulmonary disease in males

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    Alanine aminotransferase (ALT) levels reflect skeletal muscle volume and general performance, which are associated with chronic obstructive pulmonary disease (COPD) development and prognosis. This study aimed to investigate ALT levels as a risk factor for COPD development. This 13-year population-based retrospective observational cohort study included 422,452 participants for analysis. We classified groups according to the baseline ALT levels (groups 1-5: ALT (IU/L) < 10; 10-19; 20-29; 30-39; and โ‰ฅ 40, respectively). The incidence of COPD was the highest in group 1, decreasing as the group number increased in males, but not in females. The Cox regression analysis in males revealed that a lower ALT level, as a continuous variable, was a significant risk factor for COPD development [univariable, hazard ratio (HR): 0.992, 95% confidence interval (CI): 0.991-0.994; multivariable, HR: 0.998, 95% CI: 0.996-0.999]. In addition, COPD was more likely to develop in the lower ALT level groups (groups 1-4; < 40 IU/L), than in the highest ALT level group (group 5; โ‰ฅ 40 IU/L) (univariable, HR: 1.341, 95% CI: 1.263-1.424; multivariable, HR: 1.097, 95% CI: 1.030-1.168). Our findings suggest that males with low ALT levels should be carefully monitored for COPD development.ope
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