Glioma-associated stem cells: A novel class of tumor-supporting cells able to predict prognosis of human low-grade gliomas.

Background: Translational medicine aims at transferring advances in basic science research into new approaches for diagnosis and treatment of diseases. Low-grade gliomas (LGG) have a heterogeneous clinical behavior that can be only partially predicted employing current state-of-the-art markers, hindering the decision-making process. To deepen our comprehension on tumor heterogeneity, we dissected the mechanism of interaction between tumor cells and relevant components of the neoplastic environment, isolating, from LGG and high-grade gliomas (HGG), proliferating stem cell lines from both the glioma stroma and, where possible, the neoplasm. Methods and Findings: We isolated glioma-associated stem cells (GASC) from LGG (n=40) and HGG (n=73). GASC showed stem cell features, anchorage-independent growth, and supported the malignant properties of both A172 cells and human glioma-stem cells, mainly through the release of exosomes. Finally, starting from GASC obtained from HGG (n=13) and LGG (n=12) we defined a score, based on the expression of 9 GASC surface markers, whose prognostic value was assayed on 40 subsequent LGG-patients. At the multivariate Cox analysis, the GASC-based score was the only independent predictor of overall survival and malignant progression free-survival. Conclusions: The microenvironment of both LGG and HGG hosts non-tumorigenic multipotent stem cells that can increase in vitro the biological aggressiveness of glioma-initiating cells through the release of exosomes. The clinical importance of this finding is supported by the strong prognostic value associated with the characteristics of GASC. This patient-based approach can provide a groundbreaking method to predict prognosis and to exploit novel strategies that target the tumor stroma

A balanced scorecard-based model for evaluating e-learning and conventional pedagogical activities in nursing

The evaluation of e-learning and conventional pedagogical activities in nursing programmes has focused either on a single pedagogical activity or the entire curriculum, and only on students or teachers\u27 perspective. The goal of this study was to design and test a novel approach for evaluation of e-learning and conventional pedagogical activities that considers students\u27, teachers\u27 and managers\u27 perspectives. A case study of the proposed approach was performed at a publicly funded nursing faculty with Slovenian and Italian students from 2009 to 2012. The case study was combined with focus group discussions, interviews, direct observation and survey. The proposed approach allows management to compare the value of different pedagogical activities through the students\u27, teachers\u27 and managers\u27 perspectives. The approach proved useful in the evaluation of pedagogical activities and provided valid arguments for long-term pedagogical process improvemen

The EUPPBench postprocessing benchmark dataset v1.0

Abstract. Statistical Postprocessing of medium-range weather forecasts is an important component of modern forecasting systems. Since the beginning of modern data science, numerous new postprocessing methods have been proposed, complementing an already very diverse field. However, one of the questions that frequently arises when considering different methods in the framework of implementing operational postprocessing is the relative performance of the methods for a given specific task. It is particularly challenging to find or construct a common comprehensive dataset that can be used to perform such comparisons. Here, we introduce the first version of EUPPBench, a dataset of time-aligned forecasts and observations, with the aim to facilitate and standardize this process. This dataset is publicly available at https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark. We provide examples on how to download and use the data, propose a set of evaluation methods, and perform a first benchmark of several methods for the correction of 2-meter temperature forecasts. </jats:p

Glioma-Associated Stem Cells: A Novel Class of Tumor-Supporting Cells Able to Predict Prognosis of Human Low-Grade Gliomas

Background: Translational medicine aims at transferring advances in basic science research into new approaches for diagnosis and treatment of diseases. Low-grade gliomas (LGG) have a heterogeneous clinical behavior that can be only partially predicted employing current state-of-the-art markers, hindering the decision-making process. To deepen our comprehension on tumor heterogeneity, we dissected the mechanism of interaction between tumor cells and relevant components of the neoplastic environment, isolating, from LGG and high-grade gliomas (HGG), proliferating stem cell lines from both the glioma stroma and, where possible, the neoplasm. Methods and Findings: We isolated glioma-associated stem cells (GASC) from LGG (n=40) and HGG (n=73). GASC showed stem cell features, anchorage-independent growth, and supported the malignant properties of both A172 cells and human glioma-stem cells, mainly through the release of exosomes. Finally, starting from GASC obtained from HGG (n=13) and LGG (n=12) we defined a score, based on the expression of 9 GASC surface markers, whose prognostic value was assayed on 40 subsequent LGG-patients. At the multivariate Cox analysis, the GASC-based score was the only independent predictor of overall survival and malignant progression free-survival. Conclusions: The microenvironment of both LGG and HGG hosts non-tumorigenic multipotent stem cells that can increase in vitro the biological aggressiveness of glioma-initiating cells through the release of exosomes. The clinical importance of this finding is supported by the strong prognostic value associated with the characteristics of GASC. This patient-based approach can provide a groundbreaking method to predict prognosis and to exploit novel strategies that target the tumor stroma. Stem Cells 2014;32:1239-125