Hematopoietic stem cell transplantation with reduced toxicity conditioning regimen in mitochondrial neurogastrointestinal encephalopathy syndrome

Background Mitochondrial neurogastrointestinal encephalomyopathy (MNGIE) is a rare autosomal recessive disorder due to mutations in the TYMP gene. Clinical findings are characterized by neurologic manifestations and severe gastrointestinal dysfunction. The syndrome is usually fatal, the most effective treatment appears to be hematopoietic stem cell transplantation (HSCT). Procedure In this retrospective study, we evaluated HSCT that was performed using a reduced toxicity myeloablative conditioning regimen in patients with MNGIE at our center. Results A total of six allogeneic transplant procedures were performed in four patients. Three patients had fully matched donors, and one patient had a haploidentical donor. Treosulfan-based myeloablative conditioning regimen was applied in five of six transplants. Bone marrow was used as a stem cell source. One patient is being followed up in the 4th year of posttransplant with full chimeric and without graft versus host disease (GVHD). One patient died of acute stage IV gastrointestinal system GVHD. Two patients underwent second transplantation due to engraftment failure, one of which was the patient who had a haploidentical transplant. Conclusions Treosulfan-based regimen is well tolerated, although engraftment failure with this conditioning regimen can be a significant problem. We share our haploidentical transplant experience, which will be the first reported case in the literature

Data mining and machine learning in textile industry

Data mining has been proven useful for knowledge discovery in many areas, ranging from marketing to medical and from banking to education. This study focuses on data mining and machine learning in textile industry as applying them to textile data is considered an emerging interdisciplinary research field. Thus, data mining studies, including classification and clustering techniques and machine learning algorithms, implemented in textile industry were presented and explained in detail in this study to provide an overview of how clustering and classification techniques can be applied in the textile industry to deal with different problems where traditional methods are not useful. This article clearly shows that a classification technique has higher interest than a clustering technique in the textile industry. It also shows that the most commonly applied classification methods are artificial neural networks and support vector machines, and they generally provide high accuracy rates in the textile applications. For the clustering task of data mining, a K-means algorithm was generally implemented in textile studies among the others that were investigated in this article. We conclude with some remarks on the strength of the data mining techniques for textile industry, ways to overcome certain challenges, and offer some possible further research directions. (C) 2017 Wiley Periodicals, Inc

Apoptotic activities of Mediterranean plants

Medicinal plants from the Mediterranean region show great potential in the improvement of health and in the prevention of disease. Epidemiological studies indicate that some of these plants reduce the incidence of inflammatory diseases and cancer by inducing programmed cell death, thus arresting proliferation. These medicinal properties are related to the presence of compounds such as phenolics with antioxidant properties or nonphenolic compounds, including essential oils, terpenoids, or saponins. Our aim is to review role of different medicinal plants and phytochemicals in apoptosis and disease prevention and the mechanism of action implicated in these beneficial effects.Sin financiaciónNo data WoSNo data ScopusSPI 2020UE