• Asian Pacific Journal of Cancer Prevention
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• 제16권6호
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• pp.2333-2340
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• 2015

Background: Due to the strong inhibitory effects of $PPAR{\gamma}$ gene on the growth of cancer cells, the role of Pro12Ala polymorphism in $PPAR{\gamma}$ gene has been extensively investigated in cancer recently. However, the results were inconsistent according to cancer type. The aim of this study was to comprehensively evaluate the $PPAR{\gamma}$ Pro12Ala polymorphism and gastric cancer susceptibility. Materials and Methods: Search strategies were conducted in Pubmed, Medline (Ovid), Chinese biomedical database (CBM), China national knowledge infrastructure (CNKI), VIP, and Wanfang database, covering all publications, with the last search up to November 01, 2014. The strength of association between $PPAR{\gamma}$ Pro12Ala polymorphism and gastric cancer risk was assessed by OR with 95%CI. Results: A total of 546 cases and 827 controls in 5 case-control studies were included in this meta-analysis. The results indicated that the variant G allele carriers (CG+GG) had a 2.31 times higher risk for gastric cancer when compared with the homozygote CC (odds ratio (OR)=2.31, 95% confidence interval (CI)=1.67-3.21 for CG+GG vs. CC). In the subgroup analysis by ethnicity, significantly elevated risks were both found in Asians (OR=2.56, 95% CI=1.42-4.64) and Caucasians (OR=2.20, 95% CI=1.48-3.25). Similarly, in the subgroup analysis by H. pylori status, a significantly increased risk was identified in H. pylori (+) populations (OR=3.68, 95%CI=2.07-6.52), but not in H. pylori(-) populations (OR=1.17, 95%CI=0.58-2.39). Conclusions: This pooled analysis suggested that the $PPAR{\gamma}$ Pro12Ala polymorphism could be an independent predictive risk factor for gastric cancer especially in H. pylori infected populations in Asians and Caucasians. Nevertheless, prospectively designed cohort studies are needed to further investigate gene-gene and gene-environment interactions to confirm the combined effects of $PPAR{\gamma}$ Pro12Ala polymorphisms and H. pylori infection on gastric cancer risk.

• Asian Pacific Journal of Cancer Prevention
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• 제15권2호
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• pp.637-642
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• 2014

The pathogenesis of hepatocellular carcinoma (HCC) related to habitual betel quid (BQ) chewing is unclear. Risk of HCCis increased with adverse hepatic fibrosis. This study aimed to assess the impact of chronic viral hepatitis on adverse hepatic fibrosis in HCC related to BQ chewing. This hospital-based case-control study enrolled 200 pairs of age- and gender-matched patients with HCC and unrelated healthy controls. Serologic hepatitis B surface antigen (HBsAg), antibodies to hepatitis C virus (anti-HCV), ${\alpha}$-fetoprotein (AFP), and surrogate markers for significant hepatic fibrosis were measured. Information on substance-use habits was obtained with a questionnaire. By analysis of surrogate markers for hepatic fibrosis, the prevalence of significant hepatic fibrosis in patients chewing BQ was between 45.8% and 91.7%, whereas that for patients without BQ chewing was between 18.4% and 57.9%. The difference was significant (P <0.05 for each surrogate marker). Multivariate analysis indicated that cirrhosis with Child-Pugh C (odds ratio (OR) = 3.28; 95% confidence interval (CI), 1.29-8.37), thrombocytopenia (OR = 3.92, 95% CI, 1.77-8.68), AFP >400 mg/L (OR = 2.21, 95% CI, 1.05-4.66) and male gender (OR = 4.06, 95% CI, 1.29-12.77) were independent factors associated with habitual BQ chewing. In conclusion, adverse hepatic fibrosis and severe liver damage play important roles in the pathogenesis of BQ-related HCC, which could be aggravated by chronic hepatitis B and hepatitis C. BQ-cessation programs and prevention of chronic HBV/HCV infection are needed to prevent HCC related to BQ chewing.

• 한국임상수의학회지
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• 제30권4호
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• pp.258-263
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• 2013

The primary goal of this study was to compute sample sizes required to achieve the each aim of a variety of foot-and-mouth disease (FMD) surveillance programs, using a statistically valid technique that takes the following factors into account: sensitivity (Se) and specificity (Sp) of diagnostic test system, desired minimum detectable prevalence, precision, population size, and desired power of the survey. In addition, sample sizes to detect FMD if the disease is present and also as proof of freedom were computed. The current FMD active surveillance programs consist of clinical, virological, and serological surveillance. For the 2012 serological surveillance, annual sample sizes (n = 265,065) are planned at four separate levels: statistical (n = 60,884) and targeted (n = 115,232) at breeding pig farms and slaughter house, in together with the detection of structural proteins (SP) antibodies against FMD (n = 88,949). Overall, the sample size was not designed taking the specific aims of each surveillance stream into account. The sample sizes for statistical surveillance, assuming stratified two-stage sampling technique, was based to detect at least one FMD-infected case in the general population. The resulting sample size can be used to obtain evidence of freedom from FMD infection, not for detecting animals that have antibodies against FMD virus non-structural proteins (NSP). Additionally, sample sizes for targeted surveillance were not aimed for the population at risk, and also without consideration of statistical point of view. To at least the author's knowledge, sampling plan for targeted, breeding pig farms and slaughter house is not necessary and need to be included in the part of statistical surveillance. Assuming design prevalence of 10% in an infinite population, a total of 29 animals are required to detect at least one positive with probability of 95%, using perfect diagnostic test system (Se = Sp = 100%). A total of 57,211 animals needed to be sampled to give 95% confidence of estimating SP prevalence of 80% at the individual animal-level with a precision of ${\pm}5%$, assuming 800 herds with an average 200 heads per farm, within-farm variance of 0.2, between-farm variance of 0.05, cost ratio of 100:1 of farm against animals. Furthermore, 779,736 animals were required to demonstrate FMD freedom, and the sample size can further be reduced depending on the parameters assumed.

• Korean Journal of Radiology
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• 제22권6호
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• pp.994-1004
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• 2021

Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.