Epigenetic and genetic landscape of uterine leiomyomas: a current view over a common gynecological disease

PURPOSE: Despite the numerous studies on the factors involved in the genesis and growth of uterine leiomyomas, the pathogenesis of these tumors remains unknown. Intrinsic abnormalities of the myometrium, abnormal myometrial receptors for estrogen, and hormonal changes or altered responses to ischemic damage during the menstrual period may be responsible for the initiation of (epi)genetic changes found in these tumors. Considering these elements, we aimed to offer an overview about epigenetic and genetic landscape of uterine leiomyomas. METHODS: Narrative overview, synthesizing the findings of literature retrieved from searches of computerized databases. RESULTS: Several studies showed that leiomyomas have a monoclonal origin. Accumulating evidence converges on the risk factors and mechanisms of tumorigenesis: the translocation t (12;14) and deletion of 7q were found in the highest percentages of recurrence; dysregulation of the HMGA2 gene has been mapped within the critical 12q14-q15 locus. Estrogen and progesterone are recognized as promoters of tumor growth, and the potential role of environmental estrogens has been poorly explored. The growth factors with mitogenic activity, such as transforming growth factor-β3, fibroblast growth factor, epidermal growth factor, and insulin-like growth factor-I are elevated in fibroids and may have a role as effectors of the tumor promotion. CONCLUSION: The new clues on genetics and epigenetics, as well as about the growth factors that control normal and pathological myometrial cellular biology may be of great help for the development of new effective and less invasive therapeutic strategies in the near future

Big data analytics: does organizational factor matters impact technology acceptance?

Abstract Ever since the emergence of big data concept, researchers have started applying the concept to various fields and tried to assess the level of acceptance of it with renown models like technology acceptance model (TAM) and it variations. In this regard, this paper tries to look at the factors that associated with the usage of big data analytics, by synchronizing TAM with organizational learning capabilities (OLC) framework. These models are applied on the construct, intended usage of big data and also the mediation effect of the OLC constructs is assessed. The data for the study is collected from the students pertaining to information technology disciplines at University of Liverpool, online programme. Though, invitation to participate e-mails are sent to 1035 students, only 359 members responded back with filled questionnaires. This study uses structural equation modelling and multivariate regression using ordinary least squares estimation to test the proposed hypotheses using the latest statistical software R. It is proved from the analysis that compared to other models, model 4 (which is constructed by using the constructs of OLC and TAM frameworks) is able to explain 44% variation in the usage pattern of big data. In addition to this, the mediation test performed revealed that the interaction between OLC dimensions and TAM dimensions on intended usage of big data has no mediation effect. Thus, this work provided inputs to the research community to look into the relation between the constructs of OLC framework and the selection of big data technology