Signal2Image Modules in Deep Neural Networks for EEG Classification

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such as graphical processing units. The vast majority of deep learning research is conducted using images as training data, however the biomedical domain is rich in physiological signals that are used for diagnosis and prediction problems. It is still an open research question how to best utilize signals to train deep neural networks. In this paper we define the term Signal2Image (S2Is) as trainable or non-trainable prefix modules that convert signals, such as Electroencephalography (EEG), to image-like representations making them suitable for training image-based deep neural networks defined as `base models'. We compare the accuracy and time performance of four S2Is (`signal as image', spectrogram, one and two layer Convolutional Neural Networks (CNNs)) combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet) along with the depth-wise and 1D variations of the latter. We also provide empirical evidence that the one layer CNN S2I performs better in eleven out of fifteen tested models than non-trainable S2Is for classifying EEG signals and we present visual comparisons of the outputs of the S2Is.Comment: 4 pages, 2 figures, 1 table, EMBC 201

Co-Deregulated miRNA Signatures in Childhood Central Nervous System Tumors: In Search for Common Tumor miRNA-Related Mechanics

Despite extensive experimentation on pediatric tumors of the central nervous system (CNS), related to both prognosis, diagnosis and treatment, the understanding of pathogenesis and etiology of the disease remains scarce. MicroRNAs are known to be involved in CNS tumor oncogenesis. We hypothesized that CNS tumors possess commonly deregulated miRNAs across different CNS tumor types. Aim: The current study aims to reveal the co-deregulated miRNAs across different types of pediatric CNS tumors. Materials: A total of 439 CNS tumor samples were collected from both in-house microarray experiments as well as data available in public databases. Diagnoses included medulloblastoma, astrocytoma, ependydoma, cortical dysplasia, glioblastoma, ATRT, germinoma, teratoma, yoc sac tumors, ocular tumors and retinoblastoma. Results: We found miRNAs that were globally up- or down-regulated in the majority of the CNS tumor samples. MiR-376B and miR-372 were co-upregulated, whereas miR-149, miR-214, miR-574, miR-595 and miR-765 among others, were co-downregulated across all CNS tumors. Receiver-operator curve analysis showed that miR-149, miR-214, miR-574, miR-595 and miR765 could distinguish between CNS tumors and normal brain tissue. Conclusions: Our approach could prove significant in the search for global miRNA targets for tumor diagnosis and therapy. To the best of our knowledge, there are no previous reports concerning the present approach.Peer reviewe

Fractal Dimensions of In Vitro

Biological systems are characterized by their potential for dynamic adaptation. One of the challenges for systems biology approaches is their contribution towards the understanding of the dynamics of a growing cell population. Conceptualizing these dynamics in tumor models could help us understand the steps leading to the initiation of the disease and its progression. In vitro models are useful in answering this question by providing information over the spatiotemporal nature of such dynamics. In the present work, we used physical quantities such as growth rate, velocity, and acceleration for the cellular proliferation and identified the fractal structures in tumor cell proliferation dynamics. We provide evidence that the rate of cellular proliferation is of nonlinear nature and exhibits oscillatory behavior. We also calculated the fractal dimensions of our cellular system. Our results show that the temporal transitions from one state to the other also follow nonlinear dynamics. Furthermore, we calculated self-similarity in cellular proliferation, providing the basis for further investigation in this topic. Such systems biology approaches are very useful in understanding the nature of cellular proliferation and growth. From a clinical point of view, our results may be applicable not only to primary tumors but also to tumor metastases

MYCN in Neuroblastoma: “Old Wine into New Wineskins”

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/)MYCN Proto-Oncogene, BHLH Transcription Factor (MYCN) has been one of the most studied genes in neuroblastoma. It is known for its oncogenetic mechanisms, as well as its role in the prognosis of the disease and it is considered one of the prominent targets for neuroblastoma therapy. In the present work, we attempted to review the literature, on the relation between MYCN and neuroblastoma from all possible mechanistic sites. We have searched the literature for the role of MYCN in neuroblastoma based on the following topics: the references of MYCN in the literature, the gene’s anatomy, along with its transcripts, the protein’s anatomy, the epigenetic mechanisms regulating MYCN expression and function, as well as MYCN amplification. MYCN plays a significant role in neuroblastoma biology. Its functions and properties range from the forming of G-quadraplexes, to the interaction with miRNAs, as well as the regulation of gene methylation and histone acetylation and deacetylation. Although MYCN is one of the most primary genes studied in neuroblastoma, there is still a lot to be learned. Our knowledge on the exact mechanisms of MYCN amplification, etiology and potential interventions is still limited. The knowledge on the molecular mechanisms of MYCN in neuroblastoma, could have potential prognostic and therapeutic advantages.Peer reviewe

Dysregulated placental microRNAs in Early and Late onset Preeclampsia

Copyright © 2017. Published by Elsevier Ltd.INTRODUCTION: To determine the miRNA expression profile in placentas complicated by Preeclampsia (PE) and compare it to uncomplicated pregnancies. METHODS: Sixteen placentas from women with PE, [11 with early onset PE (EOPE) and 5 with late onset PE (LOPE)], as well as 8 placentas from uncomplicated pregnancies were analyzed using miRNA microarrays. For statistical analyses the MATLAB® simulation environment was applied. The over-expression of miR-518a-5p was verified using Quantitative Real-Time Polymerase Chain Reaction. RESULTS: Forty four miRNAs were found dysregulated in PE complicated placentas. Statistical analysis revealed that miR-431, miR-518a-5p and miR-124* were over-expressed in EOPE complicated placentas as compared to controls, whereas miR-544 and miR-3942 were down-regulated in EOPE. When comparing the miRNA expression profile in cases with PE and PE-growth restricted fetuses (FGR), miR-431 and miR-518a-5p were found over-expressed in pregnancies complicated by FGR. DISCUSSION: Since specific miRNAs can differentiate EOPE and LOPE from uncomplicated placentas, they may be considered as putative PE-specific biomarkers. MiR-518a-5p emerged as a potential diagnostic indicator for EOPE cases as well as for PE-FGR complicated placentas, indicating a potential link to the severity of the disease.Peer reviewe

Differential and Common Signatures of miRNA Expression and Methylation in Childhood Central Nervous System Malignancies: An Experimental and Computational Approach

© 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License. https://creativecommons.org/licenses/by/4.0/Epigenetic modifications are considered of utmost significance for tumor ontogenesis and progression. Especially, it has been found that miRNA expression, as well as DNA methylation plays a significant role in central nervous system tumors during childhood. A total of 49 resected brain tumors from children were used for further analysis. DNA methylation was identified with methylation-specific MLPA and, in particular, for the tumor suppressor genes CASP8, RASSF1, MGMT, MSH6, GATA5, ATM1, TP53, and CADM1. miRNAs were identified with microarray screening, as well as selected samples, were tested for their mRNA expression levels. CASP8, RASSF1 were the most frequently methylated genes in all tumor samples. Simultaneous methylation of genes manifested significant results with respect to tumor staging, tumor type, and the differentiation of tumor and control samples. There was no significant dependence observed with the methylation of one gene promoter, rather with the simultaneous presence of all detected methylated genes’ promoters. miRNA expression was found to be correlated to gene methylation. Epigenetic regulation appears to be of major importance in tumor progression and pathophysiology, making it an imperative field of study.Peer reviewe

Analysis of SIGLEC12 expression, IMMUNOMODULATION and prognostic value in RENAL cancer using multiomic databases

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Siglecs belong to a family of immune regulatory receptors predominantly found on hematopoietic cells. They interact with Sia, resulting in the activation or inhibition of the immune response. Previous reports have suggested that the SIGLEC12 gene, which encodes the Siglec-XII protein, is expressed in the epithelial tissues and upregulated in carcinomas. However, studies deciphering the role of Siglec-XII in renal cancer (RC) are still unavailable, and here we provide insights on this question. We conducted expression analysis using the Human Protein Atlas and UALCAN databases. The impact of SIGLEC12 on RC prognosis was determined using the KM plotter, and an assessment of immune infiltration with SIGLEC12 was performed using the TIMER database. GSEA was conducted to identify the pathways affected by SIGLEC12. Finally, using GeneMania, we identified Siglec-XII interacting proteins. Our findings indicated that macrophages express SIGLEC12 in the kidney. Furthermore, we hypothesize that Siglec-XII expression might be involved in the increase of primary RC, but this effect may not be dependent on the age of the patient. In the tumour microenvironment, oncogenic pathways appeared to be upregulated by SIGLEC12. Similarly, our analysis suggested that SIGLEC12-related kidney renal papillary cell carcinomas may be more suitable for targeted immunotherapy, such as CTLA-4 and PD-1/PD-L1 inhibitors. These preliminary results suggested that high expression of SIGLEC12 is associated with poor prognosis for RC. Future studies to assess its clinical utility are necessitated.Peer reviewe

Identification of Co-Deregulated Genes in Urinary Bladder Cancer Using High-Throughput Methodologies

Urinary bladder cancer (UBC) is the second most common urogenital solid tumor and the eleventh in the rank among all types of solid tumors. Although several oncogenes and tumor suppressors are known to be implicated in the disease, the list of candidate prognostic markers has recently expanded, as a result of the power of new high-throughput methodologies. The prognosis and therapy of UBC have progressed greatly during the last years. However, a majority of the different tumor subtypes still relapses, manifesting poor prognosis. Here, we identified gene expression patterns being common across different histological phenotypes of UBC. Such an approach could be useful in the discovery of prognostic and therapeutic targets able to be applied in the majority of the tumor’s subtypes

Information, Thermodynamics and Life: A Narrative Review

Information is probably one of the most difficult physical quantities to comprehend. This applies not only to the very definition of information, but also to the physical entity of information, meaning how can it be quantified and measured. In recent years, information theory and its function in systems has been an intense field of study, due to the large increase of available information technology, where the notion of bit dominated the information discipline. Information theory also expanded from the “simple” “bit” to the quantal “qubit”, which added more variables for consideration. One of the main applications of information theory could be considered the field of “autonomy”, which is the main characteristic of living organisms in nature since they all have self-sustainability, motion and self-protection. These traits, along with the ability to be aware of existence, make it difficult and complex to simulate in artificial constructs. There are many approaches to the concept of simulating autonomous behavior, yet there is no conclusive approach to a definite solution to this problem. Recent experimental results have shown that the interaction between machines and neural cells is possible and it consists of a significant tool for the study of complex systems. The present work tries to review the question on the interactions between information and life. It attempts to build a connection between information and thermodynamics in terms of energy consumption and work production, as well as present some possible applications of these physical quantities

Gravitational Influence on Human Living Systems and the Evolution of Species on Earth

Gravity constituted the only constant environmental parameter, during the evolutionary period of living matter on Earth. However, whether gravity has affected the evolution of species, and its impact is still ongoing. The topic has not been investigated in depth, as this would require frequent and long-term experimentations in space or an environment of altered gravity. In addition, each organism should be studied throughout numerous generations to determine the profound biological changes in evolution. Here, we review the significant abnormalities presented in the cardiovascular, immune, vestibular and musculoskeletal systems, due to altered gravity conditions. We also review the impact that gravity played in the anatomy of snakes and amphibians, during their evolution. Overall, it appears that gravity does not only curve the space–time continuum but the biological continuum, as well