Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies in South East Asia. Although activation of the MEK-MAPK is often associated with cellular growth, the role of MEK-MAPK in growth and survival of hepatocarcinoma cells has not been established.

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We previously identified a novel pregnancy-induced growth inhibitory gene, OKL38. To develop a rat model for further characterization of OKL38's role in the initiation and progression of breast and ovarian cancer, we now report the cloning and characterization of three novel rat OKL38 cDNAs that are derived through alternative splicing and differential promoter usage. These three transcripts differ in their 5' untranslated regions but share a common open reading frame that encoded for a 52-kDa protein. OKL38 is mapped to chromosome 19, spanning a region of approximately 15 kb, and contains eight exons. Differential expression of these three rat OKL38 transcripts was observed in liver, kidney, ovary, mammary gland, and uterus. In situ hybridization localized the rat OKL38 transcripts to the luminal epithelial cells of the rat mammary gland and to the granulosa cells in the rat ovary. In vivo studies showed that the RtOKL38-2.0 transcript and protein were regulated by human chorionic gonadotropin in the rat mammary gland and ovary. Importantly, overexpression of RtOKL38-enhanced green fluorescence protein fusion protein in Buffalo rat liver cells resulted in growth inhibition and cell death. Our present findings suggest that OKL38 may function as an effector for human chorionic gonadotropin protection against mammary carcinogenesis, and the availability of the three rat OKL38 cDNAs may help to elucidate the possible role of OKL38 in cellular growth, differentiation, and carcinogenesis.

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Purpose - This research is to empirically explore the differences in apparel consumption among male and female teenagers and college students in Korea and China. By conducting a survey to understand customers' needs and behaviors, fashion businesses will be able to improve their customer satisfaction and avoid redundancy, inventory, and the waste of resources, effort and money. Design/methodology - The research design considers the consumption patterns of male and female high school and college students in Korea and China. To analyze the data, the study employs decision trees, a type of machine learning algorithm. A decision tree model was developed to examine the relationship between the explanatory and response variables, which can be either quantitative or qualitative in nature. Findings - The main findings of this study indicate that there are differences in shopping behavior among different customer segments. The results show that men have a simpler shopping behavior compared to women. Additionally, cultural factors and the difference in fashion needs between students and non-students have a significant impact on the shopping choices of Chinese and Korean individuals. Originality/value - Existing studies often assume that the shopping behavior of high school and university students is similar and that there are no significant differences in clothing purchases between men and women across countries. The results provide valuable insights into the unique shopping behavior of different customer segments, and can inform fashion businesses in their efforts to meet the needs of their customers.