Cellular senescence, a type of cytostasis, is the irreversible inhibition of the natural cell division in proliferating cells, resulting from various cellular stresses, including telomere shortening, DNA damage, mitochondrial dysfunctions, and pro-inflammatory responses. While cellular senescence can facilitate beneficial physiological processes such as tissue repair and wound healing, senescent cells also contribute to pathophysiological processes of agerelated diseases, including fibrotic lung diseases. The cellular senescence model and co-culture system were established to explore the underlying mechanisms associated with cellular senescence and fibrosis. Rutaecarpine is a bioactive alkaloid isolated from Evodia rutaecarpa (Rutaceae), a traditional herbal medicine. Rutaecarpine enhanced the promotor activity of E-cadherin, reduced TGF-β-induced reorganization of the actin cytoskeleton, and finally inhibited epithelialmesenchymal transition. Rutaecarpine also attenuated fibrotic and senescence features in bleomycin-induced lung fibrosis model. Here, we suggest the relevance between senescence and fibrosis, and a potential therapeutic approach of targeting senescence to attenuate lung fibrosis development.

The retraction has been agreed upon due to critical typographical errors throughout the contents from accidents at the manuscript editing step.

Because bioequivalence studies are performed using a crossover design, information on the intra-subject coefficient of variation (intra-CV) for pharmacokinetic measures is needed when determining the sample size. However, calculated intra-CVs based on bioequivalence results of identical generic drugs produce different estimates. In this study, we collected bioequivalence results using public resources from the Ministry of Food and Drug Safety (MFDS) and calculated the intra-CVs of various generics. For the generics with multiple bioequivalence results, pooled intra-CVs were calculated. The estimated intra-CVs of 142 bioequivalence studies were 14.7±8.2% for AUC and 21.7±8.8% for Cmax. Intra-CVs of Cmax were larger than those of area under the concentration-time curve (AUC) in 129 studies (90.8%). For the 26 generics with multiple bioequivalence results, the coefficients of variation of intra-CVs between identical generics (mean±sd (min ~ max)) were 38.0±24.4% (1.9 ~ 105.3%) for AUC and 27.9±18.2% (4.0 ~ 70.1%) for Cmax. These results suggest that substantial variation exists among the bioequivalence results of identical generics. In this study, we presented the intra-CVs of various generics with their pooled intra-CVs. The estimated intra-CVs calculated in this study will provide useful information for planning future bioequivalence studies. (This is republication of the article 'Transl Clin Pharmacol 2017;25:179-182' retracted from critical typographic errors. See the 'Retraction and Republication section of this issue for further information)
Area Under Curve ; Cross-Over Studies ; Drugs, Generic ; Sample Size ; Therapeutic Equivalency

Because bioequivalence studies are performed using a crossover design, information on the intra-subject coefficient of variation (intra-CV) for pharmacokinetic measures is needed when determining the sample size. However, calculated intra-CVs based on bioequivalence results of identical generic drugs produce different estimates. In this study, we collected bioequivalence results using public resources from the Ministry of Food and Drug Safety (MFDS) and calculated the intra-CVs of various generics. For the generics with multiple bioequivalence results, pooled intra-CVs were calculated. The estimated intra-CVs of 142 bioequivalence studies were 14.7±8.2% for AUC and 21.7±8.8% for C(max). Intra-CVs of C(max) were larger than those of area under the concentration-time curve (AUC) in 129 studies (90.8%). For the 26 generics with multiple bioequivalence results, the coefficients of variation of intra-CVs between identical generics (mean±sd (min ~ max)) were 38.0±24.4% (1.9 ~ 105.3%) for AUC and 27.9±18.2 % (4.0 ~ 70.1%) for C(max). These results suggest that substantial variation exists among the bioequivalence results of identical generics. In this study, we presented the intra-CVs of various generics with their pooled intra-CVs. The estimated intra-CVs calculated in this study will provide useful information for planning future bioequivalence studies.
Area Under Curve ; Cross-Over Studies ; Drugs, Generic ; Sample Size* ; Therapeutic Equivalency*

Background The benefit of statin use after acute ST-segment elevation myocardial infarction (STEMI) has been well established, however, the influence of the timing of statin administration has not been elucidated. The objective of this study focused on early clinical outcomes after percutaneous coronary intervention (PCI). Methods This analysis of the Korea Working Group on Myocardial Infarction registry (KorMI) study included 3,584 STEMI patients (mean age, 63 ±13 years;male, 2,684, 74.9%) undergoing PCI from January 2008 to June 2009. Rates of major adverse cardiac events (MACE:all-cause death, recurrent MI, and target lesion revascularization) were compared among patients grouped according to statin therapy timing:I, both during and after hospitalization (n=2,653, 74%);II, only during hospita-lization (n=309, 8.6%);III, only after discharge (n=157, 4.4%);and IV, no statin therapy (n=465, 13%). Mean follow-up duration was 234 ± 113 days. Results Multivariate factors of statin use during hospitalization included prior statin use, multiple diseased vessels, final thrombolysis in myocardial infarction flow grade III, and low-density lipoprotein cholesterol level. At 6-month follow-up, groups III and IV had the highest MACE rates (2.3%, 3.9%, 5.1%, and 4.9%for groups I-IV, respectively, P=0.004). After adjusting for confounders, groups II-IV had a higher MACE risk than group I [hazard ratio (HR):3.20, 95%confidence interval (95%CI):1.31-7.86, P=0.011;HR:3.84, 95%CI:1.47-10.02, P=0.006;and HR:3.17, 95%CI:1.59-6.40, P=0.001;respectively]. Conclusions This study, based on the national registry database, shows early and continuous statin therapy improvs early outcomes of STEMI patients after PCI in real-world clinical prac-tice.

Objective Multidector computed tomography (MDCT) is now commonly used for the evaluation of coronary artery disease. Because MDCT images include many non-cardiac organs and the patient population evaluated is highly susceptible to extracardiac diseases, this study was designed to evaluate the prevalence of extracardiac findings in the MDCT evaluation of ischemic heart disease. Methods From March 2007 to March 2008, a total of six-hundred twenty patients, who underwent 64-slice MDCT evaluations for chest pain, or dyspnea, were enrolled in this study. Cardiac and non-cardiac findings were comprehensively evaluated by a radiologist. Results Enrolled patients included 306 men (49.4%), with a mean age of 66 years. Significant coronary artery stenosis was found in 41.6%of the patients. A total of 158 extracardiac findings were observed in 110 (17.7%) patients. Commonly involved extracardiac organs were lung (36.7%), hepatobiliary system (21.5%), thyroid (19.6%), kidney (10.8%), spine (9.7%) and breast (0.6%). Of those 110 patients, 50 (45.5%) patients underwent further diagnostic investigations. Malignant disease was detected in three (2.7%) patients (lung cancer, pancreatic cancer, and thyroid cancer). Conclusions Extracardiac findings are frequently present and should be a concern in the MDCT evaluation of chest pain syndrome.

OBJECTIVES: A common problem with analyzing survey data involves incomplete data with either a nonresponse or missing data. The mail questionnaire survey conducted for collecting lifestyle variables on the members of the Korean Elderly Phamacoepidemiologic Cohort(KEPEC) in 1996 contains some nonresponse or missing data. The proper statistical method was applied to evaluate the missing pattern of a specific KEPEC data, which had no missing data in the independent variable and missing data in the response variable, BMI. METHODS: The number of study subjects was 8,689 elderly people. Initially, the BMI and significant variables that influenced the BMI were categorized. After fitting the log-linear model, the probabilities of the people on each category were estimated. The EM algorithm was implemented using a log-linear model to determine the missing mechanism causing the nonresponse. RESULTS: Age, smoking status, and a preference of spicy hot food were chosen as variables that influenced the BMI. As a result of fitting the nonignorable and ignorable nonresponse log-linear model considering these variables, the difference in the deviance in these two models was 0.0034(df=1). CONCLUSION: There is a lot of risk if an inference regarding the variables and large samples is made without considering the pattern of missing data. On the basis of these results, the missing data occurring in the BMI is the ignorable nonresponse. Therefore, when analyzing the BMI in KEPEC data, the inference can be made about the data without considering the missing data.
Aged ; Body Mass Index ; Humans ; Life Style ; Linear Models ; Postal Service ; Surveys and Questionnaires ; Smoke ; Smoking

OBJECTIVES: In large-scale field trials, randomization by cluster is frequently used because of the administrative convenience, a desire to reduce the effect of treatment contamination, and the need to avoid ethical issues that might otherwise arise. Cluster randomization trials are experiments in which intact social unit, e.g., families, schools, cities, rather than independent individuals are randomly allocated to intervention groups. The positive correlation among responses of subjects from the same cluster is in matter in cluster randomization. This thesis is to compare the results of three randomization methods by standard error of estimator of treatment effect. METHODS: We simulated cholesterol data varing the size of the cluster and the level of the correlation in clusters and analyzed the effect of cholesterol-lowering agent. RESULTS: In intra-cluster randomization the standard error of the estimator of treatment effect is smallest relative to that in inter-cluster randomization and that in individual randomization. CONCLUSIONS: Intra-cluster randomization is the most efficient in its standard error of estimator of treatment effect but other factor should be considered when selecting a specific randomization method.
Cholesterol ; Ethics ; Humans ; Random Allocation*

OBJECTIVES: To establish prevalence of hypertension in rural area of Korea, we surveyed adult residents older than 30 years, based on the recommendation and classification of JNC-5(Joint National Committee on Detection, Evaluation, and Treatment of High Blood Pressure). METHODS: From December 1996 to February 1997, we studied 4,209 persons in 41 rural areas purposely sampled nationwide. Blood pressure was checked twice at the time of the first visit and again checked twice after one week later for the person fell under hypertension criteria at the first visit. Persons fell under hypertension criteria at the first visit without second visit for recheck were categorized as suspected hypertension. For the suspected hypertension, we predicted whether fell under criteria by logistic regression model. RESULTS: 1) The distributions of blood pressures show unimodal curve, skewed to the right. The peaks of the systolic blood pressure was between 120~129mmHg, tending to move to the right for the age of 50-and-over in male, 70-and-over in female. But peaks of the diastolic blood pressure were consistent between 80~84mmHg in both sexes. The distributions of blood pressures for male were slightly deviated to the right compared with those of the female. 2) The crude prevalence rate of hypertension, defined as systolic blood pressure > or =140mmHg or diastolic blood pressure > or =90mmHg or taking anti-hypertensive medication, was 25.94%. And the prevalence rate of suspected hypertension was 5.54%. Through the logistic regression model, the prevalence of hypertension was estimated as 29.94%. Age-sex-adjusted prevalence rate for the rural area-Myon regions- was 25.94%, if adjusted to the age-sex composition of the 1995 national census population. 3) Prevalence rate was 27.76% in male and 30.03% in female, if adjusted to the age-sex composition of the base population of this study. Prevalence rate progressively increased with age, higher in men than women before about age 60. 4) Prevalence rates among eight Provinces(Do) was different. Unadjusted rates for Kyonggi Province was 24.74%, and rates for Chonnam Province was 34.18%. But there was no significant differences of the prevalence rate between inland and seaside. 5) By logistic regression model, 65.39% of stage 1 hypertension and 75.51% of stage 2 hypertension at the first visit were estimated as to be included in hypertension criteria. 6) By the JNC-5 classification, only 22.33% of the patients taking anti-hypertensive medication was being controlled. CONCLUSION: The prevalence rate of hypertension by classification of JNC-5 at rural area was 25.94%. We could not find significant differences of prevalence rate between inland and seaside. Follow-up measurement of blood pressures will be needed to establish more valid prevalence rates of hypertension.
Adult ; Blood Pressure ; Censuses ; Classification ; Female ; Follow-Up Studies ; Gyeonggi-do ; Humans ; Hypertension* ; Jeollanam-do ; Korea* ; Logistic Models ; Male ; Prevalence* ; Rural Population

Standardized incidence rates have been widely used for comparing incidence patterns between populations, adjusting for differences in demographic structure. These rates can compare overall incidence levels, but to fully understand incidence patterns, an index which links incidence with age is also needed. The authors proposed a statistical method for estimating population-adjusted mean age of incidence (PAMA), based on Poisson distribution and Fieller's theorem. The index was applied with several modifications to data relating to the incidence of breast cancer among Caucasian women living in Los Angeles.
Breast Neoplasms ; Epidemiologic Methods ; Female ; Humans ; Incidence*