Developmental endothelial locus-1 as a potential biomarker for the incidence of acute exacerbation in patients with chronic obstructive pulmonary disease

Background Despite the high disease burden of chronic obstructive pulmonary disease (COPD) and risk of acute COPD exacerbation, few COPD biomarkers are available. As developmental endothelial locus-1 (DEL-1) has been proposed to possess beneficial effects, including anti-inflammatory effects, we hypothesized that DEL-1 could be a blood biomarker for COPD. Objective To elucidate the role of plasma DEL-1 as a biomarker of COPD in terms of pathogenesis and for predicting acute exacerbation. Methods Cigarette smoke extract (CSE) or saline was intratracheally administered to wild-type (WT) and DEL-1 knockout (KO) C57BL/6 mice. Subsequently, lung sections were obtained to quantify the degree of emphysema using the mean linear intercept (MLI). Additionally, plasma DEL-1 levels were compared between COPD and non-COPD participants recruited in ongoing prospective cohorts. Using negative binomial regression analysis, the association between the plasma DEL-1 level and subsequent acute exacerbation risk was evaluated in patients with COPD. Results In the in vivo study, DEL-1 KO induced emphysema (KO saline vs. WT saline; P = 0.003) and augmented CSE-induced emphysema (KO CSE vs. WT CSE; P < 0.001) in 29 mice. Among 537 participants, patients with COPD presented plasma log (DEL-1) levels lower than non-COPD participants (P = 0.04), especially non-COPD never smokers (P = 0.019). During 1.2 ± 0.3 years, patients with COPD in the lowest quartile of Log(DEL-1) demonstrated an increased risk of subsequent acute exacerbation, compared with those in the highest quartile of Log(DEL-1) (adjusted incidence rate ratio, 3.64; 95% confidence interval, 1.03–12.9). Conclusion Low DEL-1 levels are associated with COPD development and increased risk of subsequent COPD acute exacerbation. DEL-1 can be a useful biomarker in patients with COPD.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1C1C1007918). This research was also supported by funds (2016ER670100, 2016ER670101, 2016ER670102 and 2018ER670100, 2018ER670101, 2018ER670102) from Research of Korea Centers for Disease Control and Prevention

A Validation Study of the Korean Version of SPAN

Purpose: The SPAN, which is acronym standing for its four components: Startle, Physiological arousal, Anger, and Numbness, is a short post-traumatic stress disorder (PTSD) screening scale. This study sought to develop and validate a Korean version of the SPAN (SPAN-K). Materials and Methods: Ninety-three PTSD patients (PTSD group), 73 patients with non-psychotic psychiatric disorders (psychiatric control group), and 88 healthy participants (normal control group) were recruited for this study. Participants completed a variety of psychiatric assessments including the SPAN-K, the Davidson Trauma Scale (DTS), the Clinician-Administered PTSD Scale (CAPS), and the State-Trait Anxiety Inventory (STAI). Results: Cronbach&#39;s alpha and test-retest reliability values for the SPAN-K were both 0.80. Mean SPAN-K scores were 10.06 for the PTSD group, 4.94 for the psychiatric control group, and 1.42 for the normal control group. With respect to concurrent validity, correlation coefficients were 0.87 for SPAN-K vs. CAPS total scores (p&lt;0.001) and 0.86 for SPAN-K vs. DTS scores (p&lt;0.001). Additionally, correlation coefficients were 0.31 and 0.42 for SPAN-K vs. STAI-S and STAI-T, respectively. Receiver operating characteristic analysis of SPAN-K showed good diagnostic accuracy with an area under the curve (AUC) of 0.87. The SPAN-K showed the highest efficiency at a cutoff score of 7, with a sensitivity of 0.83, a specificity of 0.81, positive predictive value (PPV) of 0.88, and negative predictive value (NPV) of 0.73. Conclusion: These results suggest that the SPAN-K had good psychometric properties and may be a useful instrument for rapid screening of PTSD patients.This study was supported by a grant of the Korean Academy of Anxiety Disorders, Korean Neuropsychiatric Association, and Korean Research Foundation (2006-2005152), Republic of Korea

Comparison of cardiovascular event predictability between the 2009 and 2021 Chronic Kidney Disease Epidemiology Collaboration equations in a Korean chronic kidney disease cohort: the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease study

Background The 2009 Chronic Kidney Disease Epidemiology Collaboration creatinine-based estimated glomerular filtration rate (eGFRcr) equation contains a race component that is not based on biology and may cause a bias in results. Therefore, the 2021 eGFRcr and creatinine-cystatin C–based eGFR (eGFRcr-cysC) equations were developed with no consideration of race. This study compared the cardiovascular event (CVE) and all-cause mortality and CVE combined predictability among the three eGFR equations in Korean chronic kidney disease (CKD) patients. Methods This study included 2,207 patients from the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease. Receiver operating characteristic (ROC) and net reclassification improvement (NRI) index were used to compare the predictability of the study outcomes according to the 2009 eGFRcr, 2021 eGFRcr, and 2021 eGFRcr-cysC equations. Results The overall prevalence of CVE and all-cause mortality were 9% and 7%, respectively. There was no difference in area under the curve of ROC for CVE and mortality and CVE combined among all three equations. Compared to the 2009 eGFRcr, both the 2021 eGFRcr (NRI, 0.013; 95% confidence interval [CI], –0.002 to 0.028) and the eGFRcr-cysC (NRI, –0.001; 95% CI, –0.031 to 0.029) equations did not show improved CVE predictability. Similar findings were observed for mortality and CVE combined predictability with both the 2021 eGFRcr (NRI, –0.019; 95% CI, –0.039–0.000) and the eGFRcr-cysC (NRI, –0.002; 95% CI, –0.023 to 0.018). Conclusion The 2009 eGFRcr equation was not inferior to either the 2021 eGFRcr or eGFRcr-cysC equation in predicting CVE and the composite of mortality and CVE in Korean CKD patients

Predictive performance of the new race-free Chronic Kidney Disease Epidemiology Collaboration equations for kidney outcome in Korean patients with chronic kidney disease

Background The new Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations without a race coefficient have gained recognition across the United States. We aimed to test whether these new equations performed well in Korean patients with chronic kidney disease (CKD). Methods This study included 2,149 patients with CKD G1–G5 without kidney replacement therapy from the Korean Cohort Study for Outcome in Patients with CKD (KNOW-CKD). The estimated glomerular filtration rate (eGFR) was calculated using the new CKD-EPI equations with serum creatinine and cystatin C. The primary outcome was 5-year risk of kidney failure with replacement therapy (KFRT). Results When we adopted the new creatinine equation [eGFRcr (NEW)], 81 patients (23.1%) with CKD G3a based on the current creatinine equation (eGFRcr) were reclassified as CKD G2. Accordingly, the number of patients with eGFR of <60 mL/min/1.73 m2 decreased from 1,393 (64.8%) to 1,312 (61.1%). The time-dependent area under the receiver operating characteristic curve for 5-year KFRT risk was comparable between the eGFRcr (NEW) (0.941; 95% confidence interval [CI], 0.922–0.960) and eGFRcr (0.941; 95% CI, 0.922–0.961). The eGFRcr (NEW) showed slightly better discrimination and reclassification than the eGFRcr. However, the new creatinine and cystatin C equation [eGFRcr-cys (NEW)] performed similarly to the current creatinine and cystatin C equation. Furthermore, eGFRcr-cys (NEW) did not show better performance for KFRT risk than eGFRcr (NEW). Conclusion Both the current and the new CKD-EPI equations showed excellent predictive performance for 5-year KFRT risk in Korean patients with CKD. These new equations need to be further tested for other clinical outcomes in Koreans

Glycemic Control and Adverse Clinical Outcomes in Patients with Chronic Kidney Disease and Type 2 Diabetes Mellitus: Results from KNOW-CKD

Background The optimal level of glycosylated hemoglobin (HbA1c) to prevent adverse clinical outcomes is unknown in patients with chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM). Methods We analyzed 707 patients with CKD G1-G5 without kidney replacement therapy and T2DM from the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease (KNOW-CKD), a nationwide prospective cohort study. The main predictor was time-varying HbA1c level at each visit. The primary outcome was a composite of development of major adverse cardiovascular events (MACEs) or all-cause mortality. Secondary outcomes included the individual endpoint of MACEs, all-cause mortality, and CKD progression. CKD progression was defined as a ≥50% decline in the estimated glomerular filtration rate from baseline or the onset of end-stage kidney disease. Results During a median follow-up of 4.8 years, the primary outcome occurred in 129 (18.2%) patients. In time-varying Cox model, the adjusted hazard ratios (aHRs) for the primary outcome were 1.59 (95% confidence interval [CI], 1.01 to 2.49) and 1.99 (95% CI, 1.24 to 3.19) for HbA1c levels of 7.0%–7.9% and ≥8.0%, respectively, compared with <7.0%. Additional analysis of baseline HbA1c levels yielded a similar graded association. In secondary outcome analyses, the aHRs for the corresponding HbA1c categories were 2.17 (95% CI, 1.20 to 3.95) and 2.26 (95% CI, 1.17 to 4.37) for MACE, and 1.36 (95% CI, 0.68 to 2.72) and 2.08 (95% CI, 1.06 to 4.05) for all-cause mortality. However, the risk of CKD progression did not differ between the three groups. Conclusion This study showed that higher HbA1c levels were associated with an increased risk of MACE and mortality in patients with CKD and T2DM

Quality of life in patients with diabetic nephropathy: findings from the KNOW-CKD (Korean Cohort Study for Outcomes in Patients with Chronic Kidney Disease) cohort

Background Diabetic nephropathy (DN) can affect quality of life (QoL) because it requires arduous lifelong management. This study analyzed QoL differences between DN patients and patients with other chronic kidney diseases (CKDs). Methods The analysis included subjects (n = 1,766) from the KNOW-CKD (Korean Cohort Study for Outcomes in Patients with Chronic Kidney Disease) cohort who completed the Kidney Disease Quality of Life Short Form questionnaire. After implementing propensity score matching (PSM) using factors that affect the QoL of DN patients, QoL differences between DN and non-DN participants were examined. Results Among all DN patients (n = 390), higher QoL scores were found for taller subjects, and lower scores were found for those who were unemployed or unmarried, received Medical Aid, had lower economic status, had higher platelet counts or alkaline phosphatase levels, or used clopidogrel or insulin. After PSM, the 239 matched DN subjects reported significantly lower patient satisfaction (59.9 vs. 64.5, p = 0.02) and general health (35.3 vs. 39.1, p = 0.04) than the 239 non-DN subjects. Scores decreased in both groups during the 5-year follow-up, and the scores in the work status, sexual function, and role-physical domains were lower among DN patients than non-DN patients, though those differences were not statistically significant. Conclusion Socioeconomic factors of DN were strong risk factors for impaired QoL, as were high platelet, alkaline phosphatase, and clopidogrel and insulin use. Clinicians should keep in mind that the QoL of DN patients might decrease in some domains compared with non-DN CKDs

Psychometric Validation of the Korean Version of Structured Interview for Post-traumatic Stress Disorder (K-SIP)

For diagnosis and management of post-traumatic stress disorder (PTSD), the easily administered assessment tool is essential. Structured Interview for PTSD (SIP) is a validated, 17-item, simple measurement being used widely. We aimed to develop the Korean version of SIP (K-SIP) and investigated its psychometric properties. Ninety-three subjects with PTSD, 73 subjects with mood disorder or anxiety disorder as a psychiatric control group, and 88 subjects as a healthy control group were enrolled in this study. All subjects completed psychometric assessments that included the K-SIP, the Korean versions of the Clinician-Administered PTSD Scale (CAPS) and other assessment tools. The K-SIP presented good internal consistency (Cronbach's α=0.92) and test-retest reliability (r=0.87). K-SIP showed strong correlations with CAPS (r=0.72). Among three groups including PTSD patients, psychiatric controls, and normal controls, there were significant differences in the K-SIP total score. The potential cut-off total score of K-SIP was 20 with highest diagnostic efficiency (91.9%). At this point, the sensitivity and specificity were 95.5% and 88.4%, respectively. Our result showed that K-SIP had good reliability and validity. We expect that K-SIP will be used as a simple but structured instrument for assessment of PTSD

Determination of Malignant and Invasive Predictors in Branch Duct Type Intraductal Papillary Mucinous Neoplasms of the Pancreas: A Suggested Scoring Formula

Prediction of malignancy or invasiveness of branch duct type intraductal papillary mucinous neoplasm (Br-IPMN) is difficult, and proper treatment strategy has not been well established. The authors investigated the characteristics of Br-IPMN and explored its malignancy or invasiveness predicting factors to suggest a scoring formula for predicting pathologic results. From 1994 to 2008, 237 patients who were diagnosed as Br-IPMN at 11 tertiary referral centers in Korea were retrospectively reviewed. The patients' mean age was 63.1 ± 9.2 yr. One hundred ninty-eight (83.5%) patients had nonmalignant IPMN (81 adenoma, 117 borderline atypia), and 39 (16.5%) had malignant IPMN (13 carcinoma in situ, 26 invasive carcinoma). Cyst size and mural nodule were malignancy determining factors by multivariate analysis. Elevated CEA, cyst size and mural nodule were factors determining invasiveness by multivariate analysis. Using the regression coefficient for significant predictors on multivariate analysis, we constructed a malignancy-predicting scoring formula: 22.4 (mural nodule [0 or 1]) + 0.5 (cyst size [mm]). In invasive IPMN, the formula was expressed as invasiveness-predicting score = 36.6 (mural nodule [0 or 1]) + 32.2 (elevated serum CEA [0 or 1]) + 0.6 (cyst size [mm]). Here we present a scoring formula for prediction of malignancy or invasiveness of Br-IPMN which can be used to determine a proper treatment strategy

Characterization of patients in the International Severe Asthma Registry with high steroid exposure who did or did not initiate biologic therapy

Peer reviewedPostprin

Predictive Maintenance System for Wafer Transport Robot Using K-Means Algorithm and Neural Network Model

Maintenance is the technology of continuously monitoring the conditions of equipment and predicting the timing of maintenance for equipment. Particularly in the field of semiconductor manufacturing, where processes are automated, various methods are being tried to minimize the economic losses and maintenance costs caused by equipment failure. A new Predictive Maintenance (PdM) technique, a new method of maintenance, is introduced in this paper to develop an algorithm for predicting the failure of wafer transfer robots in advance. The acceleration sensor data used in the experiment were obtained by installing a sensor onto the wafer transfer robot. To analyze these data, the data preprocessing and FFT process were performed. These data were divided into normal data, first error data, second error data, and third error data (failure data) in stages. By clustering the data using the K-means algorithm, the center point distribution of the clusters was analyzed, and the features of the error data and normal data were extracted. Using these features, an artificial neural network model was designed to predict the point of failure of the robot. Previous research on maintenance systems of the transfer robot used fewer than 50 error data, but 1686 error data were used in this experiment. The reliability of the model is improved by randomly selecting data from a total of 2248 data sets. In addition, it was confirmed that it was possible to classify normal data and error data with an accuracy of 97% and to predict equipment failure by applying neural network modeling