PURPOSE: This study was conducted to evaluate the association between H. pylori infection and socioeconomic status and to determine the current prevalence of H. pylori infection in Korean adolescents. METHODS: A structured questionnaire was sent to the children's parents to obtain demographic information on the parents and environmental information. Of the 532 questionnaires sent out, 375 (70.5%; 170girls and 205boys) were returned. Their ages ranged from 10 to 15 years (mean, 12.9 years). After collecting blood samples, we measured serum IgG antibody to H. pylori using ELISA method. The association of risk factors such as age, sex, socioeconomic class, type of house, and crowding index with H. pylori infection were analyzed by multiple regression analysis. Socioeconomic status was estimated from the parents'education and occupation using a modified Hollingshead index. RESULTS: The prevalence rate of H. pylori infection was 16.8% (63/375). It increased with age (10.3% at 10~11 years, 15.9% at 12~13 years, and 20.7% at 14~15 years). The H. pylori infection was inversely related to the socioeconomic class (6.3% for the upper class, 16.0% for the middle class, and 20.0% for the lower class). Crowding condition and type of house did not affect significantly on seroprevalence of H. pylori infection. After logistic regression, we found that the odds ratio for age was 2.2 (95% confidence interval 0.9~5.4), and for socioeconomic status, 3.6 (95% confidence interval 0.5~28.9). CONCLUSION: The prevalence of H. pylori infection in Korean adolescents was 16.8%. It related inversely to socioeconomic status but was not statistically significant. Socioeconomic status based on parents' education and occupation seemed to affect more on H. pylori seroprevalence than crowding or type of house did.
Adolescent* ; Crowding ; Education ; Enzyme-Linked Immunosorbent Assay ; Helicobacter pylori* ; Helicobacter* ; Humans ; Immunoglobulin G ; Logistic Models ; Occupations ; Odds Ratio ; Parents ; Prevalence ; Surveys and Questionnaires ; Risk Factors ; Seroepidemiologic Studies ; Social Class*

BACKGROUND AND OBJECTIVES: Cancer of the thyroid is the sixth common cancer in Korea, and fourth common among the Korean women, in particular. Aming the prevalent carcinomas of thyroid, the papillary thyroid carcinoma is the most frequent type. Genomic instability is the characteristic of nearly all tumors as well as thyroid cancers. However, despite the high frequency of papillary thyroid carcinomas, their chromosomal alterations are poorly characterized in Korea. Comparative genomic hybridization (CGH) is a new fluorescence in situ hybridization (FISH) technique to identify genomic imbalances in cancers. In this study, CGH was carried out with the aim of analyzing non-random chromosomal aberrations involved in papillary thyroid carcinomas. MATERIALS AND METHOD: CGH was carried out. Biotin-labeled tumor DNA and digoxigenin-labeled normal DNA were co-hybridized to normal metaphase cells. Then, the ratio of fluorescence was analyzed by an image analyzer. In array-CGH, Cy3 labeled tumor DNA and Cy5 labeled normal DNA were hybridized to microarray template, and then image analysis was performed by microarray image analyzer. RESULTS: Gains of 22q13, 6p24, 7p13, 7q21, 7q31, 8q24, 17q24 and 19p13.3 were found frequently. CONCLUSION: Non-random aberrations which were disclosed in this study might be candidate regions for the abnormal genes involved in papillary thyroid cancer.
Carcinoma, Papillary* ; Chromosome Aberrations* ; Comparative Genomic Hybridization* ; DNA ; Female ; Fluorescence ; Genomic Instability ; Humans ; Hybridization, Genetic ; In Situ Hybridization ; Korea ; Metaphase ; Thyroid Gland* ; Thyroid Neoplasms

PURPOSE: The aim of this study was to evaluate the applicability of machine learning methods that combine data on age and prostate-specific antigen (PSA) levels for predicting prostate cancer. MATERIALS AND METHODS: We analyzed 943 patients who underwent transrectal ultrasonography (TRUS)-guided prostate biopsy at Chungnam National University Hospital between 2014 and 2018 because of elevated PSA levels and/or abnormal digital rectal examination and/or TRUS findings. We retrospectively reviewed the patients’ medical records, analyzed the prediction rate of prostate cancer, and identified 20 feature importances that could be compared with biopsy results using 5 different algorithms, viz., logistic regression (LR), support vector machine, random forest (RF), extreme gradient boosting, and light gradient boosting machine. RESULTS: Overall, the cancer detection rate was 41.8%. In patients younger than 75 years and with a PSA level less than 20 ng/mL, the best prediction model for prostate cancer detection was RF among the machine learning methods based on LR analysis. The PSA density was the highest scored feature importances in the same patient group. CONCLUSIONS: These results suggest that the prediction rate of prostate cancer using machine learning methods not inferior to that using LR and that these methods may increase the detection rate for prostate cancer and reduce unnecessary prostate biopsy, as they take into consideration feature importances affecting the prediction rate for prostate cancer.
Biopsy ; Chungcheongnam-do ; Digital Rectal Examination ; Forests ; Humans ; Logistic Models ; Machine Learning ; Medical Records ; Prostate ; Prostate-Specific Antigen ; Prostatic Neoplasms ; Retrospective Studies ; Support Vector Machine ; Ultrasonography