Biomedical subjects

Sun-Mi Lee

Publications and source records attributed to Sun-Mi Lee.

7 recordsLinked to original sources

PubMed2006

Benzo[a]pyrene-induced DNA damage and p53 modulation in human hepatoma HepG2 cells for the identification of potential biomarkers for PAH monitoring and risk assessment.

To identify potential biomarkers for the monitoring and risk assessment of benzo[a]pyrene (BaP), the oxidative stress-related DNA damage and p53 modification were investigated in human hepatoma HepG2 cells. Benzo[a]pyrene exposure induced a decrease in the cell viability, but increased the antioxidant enzyme activity as well as the DNA and lipid damage. The p53 protein activation appeared to have been a downstream response to the benzo[a]pyrene-induced DNA damage, suggesting p53 plays important roles in the defense against benzo[a]pyrene-induced genotoxicity. The response of phosphorylated p53 may be more sensitive towards benzo[a]pyrene exposure than normal p53. Following DNA damage, the activation of p53 acts as a transcriptional regulator of several target genes, including, p21 protein; a gene that encodes the Cdk inhibitor and is induced by exposure to benzo[a]pyrene. The p53 mRNA level was increased after the treatment of cells with benzo[a]pyrene, as well as following the induction of p53 protein, suggesting the benzo[a]pyrene-stimulated p53 accumulation may also be transcriptionally induced. The overall results suggest that benzo[a]pyrene leads to serious DNA damage, which leads to the transcription of the p53 gene; that the subsequent p53 protein accumulation up-regulates the cellular p21 protein. Oxidative DNA damage and p53 accumulation seem to be related to benzo[a]pyrene toxicity; however, their potential as biomarkers in environmental monitoring and risk assessment needs to be validated in the context of their specificity and sensitivity.

Benzo(a)pyrene↗

PubMed2006

[Socioeconomic costs of alcohol drinking in Korea].

OBJECTIVES: We wanted to estimate the annual socioeconomic costs of alcohol drinking in Korea. METHODS: The costs were classified as direct costs, indirect costs and the other costs. The direct costs consisted of direct medical costs, indirect medical costs and subsidiary medical costs. Particularly, the medical costs and population attributable fraction for disease were considered to reflect the calculation of the direct medical costs. The indirect costs were computed by the extent to which the loss of productivity and loss of the workforce might have occurred due to changes in mortality and morbidity according to alcohol drinking. The other costs consisted of property loss, administration costs and costs of alcoholic beverage. RESULTS: The annual costs, which seemed to be attributable to alcohol drinking, were estimated to be 149,352 hundred million won (2.86% of GDP). In case of the latter, the amount includes 9,091 hundred million won for direct costs (6.09%), 62,845 hundred million won for the reduction and loss of productivity (42.08%), 44,691 hundred million won for loss of the workforce (29.92%), and the other costs (21.91%). CONCLUSIONS: Our study confirms that compared with the cases of Japan (1.9% of GNP) and the other advanced countries (1.00-1.42% of GDP), alcohol drinking incurs substantial socioeconomic costs to the Korean society. Therefore, this study provides strong support for government interventions to control alcohol drinking in Korea.

Adult↗

PubMed2006

Data mining approach to model the diagnostic service management.

Korea has National Health Insurance Program operated by the government-owned National Health Insurance Corporation, and diagnostic services are provided every two year for the insured and their family members. Developing a customer relationship management (CRM) system using data mining technology would be useful to improve the performance of diagnostic service programs. Under these circumstances, this study developed a model for diagnostic service management taking into account the characteristics of subjects using a data mining approach. This study could be further used to develop an automated CRM system contributing to the increase in the rate of receiving diagnostic services.

Databases, Factual↗

PubMed2005

Clinical usefulness of telomerase for the detection of colon cancer in ulcerative colitis patients.

BACKGROUND AND AIM: Colorectal carcinoma (CRC) is a complication of ulcerative colitis (UC). Although stool occult blood and colonoscopy are used to detect CRC in UC, these methods have drawbacks, in that bleeding is associated with UC and the underlying mucosa is irregular, making it difficult to detect dysplasia. Telomerase and its catalytic subunit, telomerase reverse transcriptase (hTERT), are specifically expressed in cancers, making them candidate markers for the early detection of cancer. We previously reported that assays of telomerase in pancreatic juice may be useful for the early detection of pancreatic cancer. The aims of our study were to determine whether assays for telomerase and TERT may be useful in the diagnosis of CRC developed in UC patients. METHODS: Luminal washings and biopsies were collected during colonoscopy in 66 patients; 34 with CRC, 21 with UC, and 11 controls. Telomerase activity was detected by telomeric repeat amplification protocol (TRAP) and hTERT mRNA was assayed by reverse transcription-polymerase chain reaction (RT-PCR). RESULTS: Telomerase activity was detected in biopsies from 33 of 34 (97%) CRC patients, 14 of 21 (67%) UC patients, and three of 11 (27%) normal controls. Expression of hTERT mRNA was observed in biopsies from 32 of 34 (94%) CRC patients, 12 of 21 (57%) UC patients, and five of 11 (45%) controls. In analyses of washing fluid, 21 of 34 (62%) of CRC patients were positive for telomerase, but all UC patients and controls were negative. The sensitivity of telomerase for CRC was 97% in tissues and 62% in washing fluid. The specificity of telomerase in washing fluid was 100%, whereas the specificity of telomerase or hTERT mRNA in tissues was 47% each. CONCLUSIONS: For the detection of CRC arising in UC, telomerase and hTERT in colonic tissue showed high sensitivity, and telomerase in colonoscopic luminal washings revealed a high specificity. These might be candidate markers in clinical decision making for the diagnosis of CRC from UC.

Adult↗

PubMed2004

[Prophylactic effect of Lactobacillus GG in animal colitis and its effect on cytokine secretion and mucin gene expressions].

BACKGROUND/AIMS: Lactobacillus rhamnosus GG (LGG) has been used in acute colitis treatment. However, it is unclear whether the LGG prevents chronic colitis. The aim of this study was to examine the prophylactic effect of LGG on animal colitis, cytokine secretion, and mucin gene expression. METHODS: BALB/c mice (n=64) were exposed to 5% dextran sulfate sodium (DSS) for 7 days followed by 10 days recovery period and repeatedly exposed for 4 days. Then, the mice were devided into three group; group of oral LGG adminstration throughout the recovery and repeated colitis period; PBS group of PBS administration; control group. Colon length, histologic score, tumor necrosis factor-alpha (TNF-alpha), interleukin-10 (IL-10) levels, mucin gene expressions were determined at each period. RESULTS: In acute colitis period, the LGG group showed higher levels of disease activity index (DAI), histologic score, TNF-alpha, IL-10, but shorter colon length, lower levels of mucin gene expressions than the control group. However, in repeated colitis period, the LGG group showed markedly lower levels of DAI and IL-10 but significantly longer colon length than PBS group (p<0.05). There was no difference in the mucin gene expression. CONCLUSIONS: These results suggest that LGG prevents chronic murine colitis. It may be associated with cytokine modulation and competitive inhibition of pathogenic bacteria. However, it may not be related with gene expression.

Animals↗

PubMedDate not supplied

Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers.

The growth of nursing databases necessitates new approaches to data analyses. These databases, which are known to be massive and multidimensional, easily exceed the capabilities of both human cognition and traditional analytical approaches. One innovative approach, knowledge discovery in large databases (KDD), allows investigators to analyze very large data sets more comprehensively in an automatic or a semi-automatic manner. Among KDD techniques, Bayesian networks, a state-of-the art representation of probabilistic knowledge by a graphical diagram, has emerged in recent years as essential for pattern recognition and classification in the healthcare field. Unlike some data mining techniques, Bayesian networks allow investigators to combine domain knowledge with statistical data, enabling nurse researchers to incorporate clinical and theoretical knowledge into the process of knowledge discovery in large datasets. This tailored discussion presents the basic concepts of Bayesian networks and their use as knowledge discovery tools for nurse researchers.

Artificial Intelligence↗

PubMedDate not supplied

Logistic regression and Bayesian networks to study outcomes using large data sets.

BACKGROUND: In nursing research, the interest in using large health care databases to predict nursing sensitive outcomes is growing rapidly. Traditionally, one of the most frequently used methods is logistic regression (LR), which, although powerful and familiar, has several limitations when used in the analysis of large databases. As a result, innovative approaches are required. APPROACH: To (a) introduce an innovative/alternative data analysis approach (Bayesian network), (b) discuss the constraints of LR and the complementary advantages of Bayesian networks (BNs) in working with large and multidimensional health care data, and (c) provide a fundamental understanding of the use of BNs in the nursing/health care domain. RESULTS: Studies have shown that BNs have several advantages over LR in analyzing complex and large data: (a) statistical assumptions, such as linearity and additivity, are relaxed; (b) handling of a larger number of predictors and identification of interactions among predictors is less complex; and (c) the discovery of structure, pattern, and knowledge, for example, of unknown, complex, and nonlinear relationships, in data is facilitated. CONCLUSION: Outcome studies, such as those undertaken by nurse researchers, may benefit from the examination and use of innovative approaches such as BNs to the analysis of very large and complex health care data sets.

Bayes Theorem↗