A pilot study of implication of machine learning for relapse prediction after allogeneic stem cell transplantation in adults with Ph-positive acute lymphoblastic leukemia.

The posttransplant relapse in Ph-positive ALL increases the risk of death. There is an unmet need for instruments to predict the risk of relapse and plan prophylaxis. In this study, we analyzed posttransplant data by machine learning algorithms.

High mutation burden in the checkpoint and micro-RNA processing genes in myelodysplastic syndrome.

A number of sequencing studies identified the prognostic impact of somatic mutations in myelodysplastic syndrome (MDS). However the majority of them focused on methylation regulation, apoptosis and proliferation genes. Despite the number of experimental studies published on the role of micro-RNA processing and checkpoint genes in the development of MDS, the clinical data about mutational landscape in these genes is limited.

Identification and Characterization of a Novel Variant Associated with Osteopetrosis.

Osteopetrosis is a group of rare inheritable disorders of the skeleton characterized by increased bone density. The disease is remarkably heterogeneous in clinical presentation and often misdiagnosed. Therefore, genetic testing and molecular pathogenicity analysis are essential for precise diagnosis and new targets for preventive pharmacotherapy.

[Differential expression of genes that encode glycolysis enzymes in kidney and lung cancer in humans].

Glycolysis is a main catabolic pathway of glucose metabolism, accompanied by ATP synthesis. More than 30 enzymes are involved in glycolysis, and genes that encode them can be considered housekeeping genes due to the high conservatism and evolutionary antiquity of the process. We studied the expression of these genes in kidney papillary cancer and planocellular lung cancer via the bioinformatic analysis of transcriptome database and method of quantitative real time PCR.