Real-time coronary artery stenosis detection based on modern neural networks

Invasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may facilitate the diagnosis. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our study is aimed at confirming the feasibility of real-time coronary artery stenosis detection using deep learning methods. To reach this goal we trained and tested eight promising detectors based on different neural network architectures (MobileNet, ResNet-50, ResNet-101, Inception ResNet, NASNet) using clinical angiography data of 100 patients. Three neural networks have demonstrated superior results. The network based on Faster-RCNN Inception ResNet V2 is the most accurate and it achieved the mean Average Precision of 0.95, F1-score 0.96 and the slowest prediction rate of 3 fps on the validation subset. The relatively lightweight SSD MobileNet V2 network proved itself as the fastest one with a low mAP of 0.83, F1-score of 0.80 and a mean prediction rate of 38 fps. The model based on RFCN ResNet-101 V2 has demonstrated an optimal accuracy-to-speed ratio. Its mAP makes up 0.94, F1-score 0.96 while the prediction speed is 10 fps. The resultant performance-accuracy balance of the modern neural networks has confirmed the feasibility of real-time coronary artery stenosis detection supporting the decision-making process of the Heart Team interpreting coronary angiography findings

Analysis of opo cis-regulatory landscape uncovers Vsx2 requirement in early eye morphogenesis

The self-organized morphogenesis of the vertebrate optic cup entails coupling the activation of the retinal gene regulatory network to the constriction-driven infolding of the retinal epithelium. Yet the genetic mechanisms underlying this coordination remain largely unexplored. Through phylogenetic footprinting and transgenesis in zebrafish, here we examine the cis-regulatory landscape of opo, an endocytosis regulator essential for eye morphogenesis. Among the different conserved enhancers identified, we isolate a single retina-specific element (H6_10137) and show that its activity depends on binding sites for the retinal determinant Vsx2. Gain- and loss-of-function experiments and ChIP analyses reveal that Vsx2 regulates opo expression through direct binding to this retinal enhancer. Furthermore, we show that vsx2 knockdown impairs the primary optic cup folding. These data support a model by which vsx2, operating through the effector gene opo, acts as a central transcriptional node that coordinates neural retina patterning and optic cup invagination in zebrafish.info:eu-repo/semantics/publishedVersio

Human GLI3 Intragenic Conserved Non-Coding Sequences Are Tissue-Specific Enhancers

The zinc-finger transcription factor GLI3 is a key regulator of development, acting as a primary transducer of Sonic hedgehog (SHH) signaling in a combinatorial context dependent fashion controlling multiple patterning steps in different tissues/organs. A tight temporal and spatial control of gene expression is indispensable, however, cis-acting sequence elements regulating GLI3 expression have not yet been reported. We show that 11 ancient genomic DNA signatures, conserved from the pufferfish Takifugu (Fugu) rubripes to man, are distributed throughout the introns of human GLI3. They map within larger conserved non-coding elements (CNEs) that are found in the tetrapod lineage. Full length CNEs transiently transfected into human cell cultures acted as cell type specific enhancers of gene transcription. The regulatory potential of these elements is conserved and was exploited to direct tissue specific expression of a reporter gene in zebrafish embryos. Assays of deletion constructs revealed that the human-Fugu conserved sequences within the GLI3 intronic CNEs were essential but not sufficient for full-scale transcriptional activation. The enhancer activity of the CNEs is determined by a combinatorial effect of a core sequence conserved between human and teleosts (Fugu) and flanking tetrapod-specific sequences, suggesting that successive clustering of sequences with regulatory potential around an ancient, highly conserved nucleus might be a possible mechanism for the evolution of cis-acting regulatory elements

MicroRNA-mediated drug resistance in breast cancer

Chemoresistance is one of the major hurdles to overcome for the successful treatment of breast cancer. At present, there are several mechanisms proposed to explain drug resistance to chemotherapeutic agents, including decreased intracellular drug concentrations, mediated by drug transporters and metabolic enzymes; impaired cellular responses that affect cell cycle arrest, apoptosis, and DNA repair; the induction of signaling pathways that promote the progression of cancer cell populations; perturbations in DNA methylation and histone modifications; and alterations in the availability of drug targets. Both genetic and epigenetic theories have been put forward to explain the mechanisms of drug resistance. Recently, a small non-coding class of RNAs, known as microRNAs, has been identified as master regulators of key genes implicated in mechanisms of chemoresistance. This article reviews the role of microRNAs in regulating chemoresistance and highlights potential therapeutic targets for reversing miRNA-mediated drug resistance. In the future, microRNA-based treatments, in combination with traditional chemotherapy, may be a new strategy for the clinical management of drug-resistant breast cancers

Analysis of Deep Neural Networks for Detection of Coronary Artery Stenosis

This paper describes an approach based on machine learning technology that is of particular interest for the localization and characterization of both single focal stenoses and multivessel multifocal lesions. Due to the complexity of analyzing large amounts of data for the cardiac surgeon, we pay special attention to the analysis, training, and comparison of popular neural networks that classify and localize foci of stenosis on coronary angiography data. From the complete coronarography dataset collected at the Research Institute for Complex Issues of Cardiovascular Diseases, we retrospectively select data of 100 patients. For the automated analysis of the medical data, the paper considers in detail three models (SSD MobileNet V1, Faster-RCNN ResNet-50 V1, and Faster-RCNN NASNet), which differ in their architecture, complexity, and the number of weights. The models are compared in terms of their basic efficiency characteristics: accuracy, training time, and prediction time. The test results show that the training and prediction times are directly proportional to the complexity of the models. In this regard, Faster-RCNN NASNet exhibits the lowest prediction time (the average processing time for one image is 880 ms), while Faster-RCNN ResNet-50 V1 has the highest prediction accuracy. The latter model reaches the mean average precision (mAP) level of 0.92 on the validation dataset. On the other hand, SSD MobileNet V1 is the fastest model, capable of making predictions with a prediction rate of 23 fps

Messenger RNA Sequence Rather than Protein Sequence Determines the Level of Self-synthesis and Antigen Presentation of the EBV-encoded Antigen, EBNA1

peer-reviewedViruses establishing persistent latent infections have evolved various mechanisms to avoid immune surveillance. The Epstein-Barr virus-encoded nuclear antigen, EBNA1, expressed in all EBV-associated malignancies, modulates its own protein levels at quantities sufficient to maintain viral infection but low enough so as to minimize an immune response by the infected host cell. This evasion mechanism is regulated through an internal purine-rich mRNA repeat sequence encoding glycine and alanine residues. In this study we assess the impact of the repeat's nucleotide versus peptide sequence on inhibiting EBNA1 self-synthesis and antigen presentation. We demonstrate that altered peptide sequences resulting from frameshift mutations within the repeat do not alleviate the immune-evasive function of EBNA1, suggesting that the repetitive purine-rich mRNA sequence itself is responsible for inhibiting EBNA1 synthesis and subsequent poor immunogenicity. Our comparative analysis of the mRNA sequences of the corresponding repeat regions of different gammaherpesvirus maintenance homologues to EBNA1 highlights the high degree of identity between the nucleotide sequences despite very little homology in the encoded amino acid sequences. These studies demonstrate the importance of gammaherpesvirus purine-rich mRNA repeat sequences on antigenic epitope generation and evasion from T-cell mediated immune control, suggesting novel approaches to prevention and treatment of latent infection by this class of virus.National Health & Medical Research Council (NH&MRC) Canberra, Australia (#496684 APP1005091); NH&MRC Career Development Award Research Fellowship (#496712