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Independent predictors for good neurologic outcome were age, lower no-flow time and lower serum lactate levels at hospital arrival. CONCLUSION In an urban setting, a significant proportion of OHCA patients with rSR can survive to a good neurologic outcome, despite very long time to ROSC.Hormones have become a useful therapeutic aspect of clinical endocrinology but how to use them to optimize the health benefits and avoid adverse effects is a major challenge. Estrogen is an indispensable hormone for proper biological functioning but is also implicated with the pathology of both the reproductive and non-reproductive tissues. Abnormal estrogen receptor signaling may increase the risk of development of a variety of diseases including colorectal cancer (CRC). Estrogen receptor beta (ERβ) is the predominant subtype in the colonic epithelium and confers the anti-tumor effect through various mechanisms. Many investigators have embarked on the search for the biological mechanisms by which estrogen and estrogen-like compounds may influence the pathogenesis of CRC. check details This review explores the recent findings on the therapeutic role of ERβ in the colonic epithelium as a prospective candidate for targeted endocrine therapy in CRC.One of the major barriers in cancer therapy is the resistance to conventional therapies and cancer stem cells (CSCs) are among the main causes of this problem. CD133 as a CSC marker displays stem cell-like properties, tumorigenic capacity, and drug resistance in various cancers. However, the molecular mechanism behind CD133 function in prostate cancer (PC) still remains unclear. This research aimed to illustrate the probabilistic mechanism of CD133-siRNA and paclitaxel in the reduction of chemoresistance in PC cells. To measure the cell viability, migratory capacity, CSCs properties, invasive potential, apoptosis and cell cycle progression of the cells, the MTT, wound healing, spheroid assay, colony formation assay, DAPI staining and flow cytometry assays were applied in the LNCaP cell line, respectively. Also, quantitative real-time PCR (qRT-PCR) and western blot method were used for measuring the expression of CD133 and the effects of CD133 silencing on the AKT/mTOR/c-myc axis and pro-metastatic genes expression. We showed that the CD133-siRNA considerably decreased the CD133 expression. Moreover, CD133-siRNA and paclitaxel treatment significantly decreased cell proliferation and also inhibited the ability of cell migration and invasion and reduced pro-metastatic genes expression. Additionally, we found that the simultaneous use of CD133-siRNA and paclitaxel increased the paclitaxel-induced apoptosis. Our results confirmed that CD133 silencing combined with paclitaxel synergistically could suppress cell migration, invasion, and proliferation and enhance the chemosensitivity compared with mono treatment. Therefore, CD133 silencing therapy could be viewed as a promising and efficient strategy in PC targeted therapies.Deinstitutionalization is often described as an organizational shift of moving care from the psychiatric hospital towards the community. This paper analyses deinstitutionalization as a daily care practice by adopting an empirical ethics approach instead. Deinstitutionalization of mental healthcare is seen as an important way of improving the quality of lives of people suffering from severe mental illness. But how is this done in practice and which different goods are strived for by those involved? We examine these questions by giving an ethnographic description of community mental health care in Trieste, a city that underwent a radical process of deinstitutionalization in the 1970s. We show that paying attention to the spatial metaphors used in daily care direct us to different notions of good care in which relationships are central. Addressing the question of how daily care practices of mental healthcare outside the hospital may be constituted and the importance of spatial metaphors used may inform other practices that want to shape community mental health care.The idea of artificial intelligence for social good (henceforth AI4SG) is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good.Functional connectivity analyses for task-based fMRI data are generally preceded by methods for identification of network nodes. As there is no general canonical approach to identifying network nodes, different identification techniques may exert different effects on inferences drawn regarding functional network properties. Here, we compared the impact of two different node identification techniques on estimates of local node importance (based on Degree Centrality, DC) in two working memory domains verbal and visual. The two techniques compared were the commonly used Activation Likelihood Estimate (ALE) technique (with node locations based on data aggregation), against a hybrid technique, Experimentally Derived Estimation (EDE). In the latter, ALE was first used to isolate regions of interest; then participant-specific nodes were identified based on individual-participant local maxima. Time series were extracted at each node for each dataset and subsequently used in functional connectivity analysis to (1) assess the impact of choice of technique on estimates of DC, and (2) assess the difference between the techniques in the ranking of nodes (based on DC) in the networks they produced. In both domains, we found a significant Technique by Node interaction, signifying that the two techniques yielded networks with different DC estimates. Moreover, for the majority of participants, node rankings were uncorrelated between the two techniques (85% for the verbal working memory task and 92% for the visual working memory task). The latter effect is direct evidence that the identification techniques produced different rankings at the level of individual participants. These results indicate that node choice in task-based fMRI data exerts downstream effects that will impact interpretation and reverse inference regarding brain function.