The usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work. The database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology “Mehmedbasic” for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound. The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman’s) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%. The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.

Cell-to-cell communication is a fundamental process in every multicellular organism. In addition to membrane-bound and released factors, the sharing of cytosolic components represents a new, poorly explored signaling route. An extraordinary example of this communication channel is the direct transport of mitochondria between cells. In this review, we discuss how intercellular mitochondrial transfer can be used by cancer cells to sustain their high metabolic requirements and promote drug resistance and describe relevant molecular players in the context of current and future cancer therapy.

Acute erythroleukemia (AEL or acute myeloid leukemia [AML]-M6) is a rare but aggressive hematologic malignancy. Previous studies showed that AEL leukemic cells often carry complex karyotypes and mutations in known AML-associated oncogenes. To better define the underlying molecular mechanisms driving the erythroid phenotype, we studied a series of 33 AEL samples representing 3 genetic AEL subgroups including TP53-mutated, epigenetic regulator-mutated (eg, DNMT3A, TET2, or IDH2), and undefined cases with low mutational burden. We established an erythroid vs myeloid transcriptome-based space in which, independently of the molecular subgroup, the majority of the AEL samples exhibited a unique mapping different from both non-M6 AML and myelodysplastic syndrome samples. Notably, >25% of AEL patients, including in the genetically undefined subgroup, showed aberrant expression of key transcriptional regulators, including SKI, ERG, and ETO2. Ectopic expression of these factors in murine erythroid progenitors blocked in vitro erythroid differentiation and led to immortalization associated with decreased chromatin accessibility at GATA1-binding sites and functional interference with GATA1 activity. In vivo models showed development of lethal erythroid, mixed erythroid/myeloid, or other malignancies depending on the cell population in which AEL-associated alterations were expressed. Collectively, our data indicate that AEL is a molecularly heterogeneous disease with an erythroid identity that results in part from the aberrant activity of key erythroid transcription factors in hematopoietic stem or progenitor cells.