In silico and in vitro analysis of lncRNA XIST reveals a panel of possible lung cancer regulators and a five-gene diagnostic signature

© 2020 by the authors. Long non-coding RNAs (lncRNAs) perform a wide functional repertoire of roles in cell biology, ranging from RNA editing to gene regulation, as well as tumour genesis and tumour progression. The lncRNA X-inactive specific transcript (XIST) is involved in the aetiopathogenesis of non-small cell lung cancer (NSCLC). However, its role at the molecular level is not fully elucidated. The expression of XIST and co-regulated genes TSIX, hnRNPu, Bcl-2, and BRCA1 analyses in lung cancer (LC) and controls were performed in silico. Differentially expressed genes (DEGs) were determined using RNA-seq in H1975 and A549 NSCLC cell lines following siRNA for XIST. XIST exhibited sexual dimorphism, being up-regulated in females compared to males in both control and LC patient cohorts. RNA-seq revealed 944 and 751 DEGs for A549 and H1975 cell lines, respectively. These DEGs are involved in signal transduction, cell communication, energy pathways, and nucleic acid metabolism. XIST expression associated with TSIX, hnRNPu, Bcl-2, and BRCA1 provided a strong collective feature to discriminate between controls and LC, implying a diagnostic potential. There is a much more complex role for XIST in lung cancer. Further studies should concentrate on sex-specific changes and investigate the signalling pathways of the DEGs following silencing of this lncRNA

LogicWiSARD: Memoryless synthesis of weightless neural networks

Weightless neural networks (WNNs) are an alternative pattern recognition technique where RAM nodes function as neurons. As both training and inference require mostly table lookups, few additions, and no multiplications, WNNs are suitable for high-performance and low-power embedded applications. This work introduces a novel approach to implement WiSARD, the leading WNN state-of-the-art architecture, completely eliminating memories and arithmetic circuits and utilizing only logic functions. The approach creates compressed minimized implementations by converting trained WNN nodes from lookup tables to logic functions. The proposed LogicWiSARD is implemented in FPGA and ASIC technologies to illustrate its suitability for edge inference. Experimental results show more than 80% reduction in energy consumption when the proposed LogicWiSARD model is compared with a multilayer perceptron network (MLP) of equivalent accuracy. Compared to previous work on FPGA implementations for WNNs, convolutional neural networks, and binary neural networks, the energy savings of LogicWiSARD range between 32.2% and 99.6%.info:eu-repo/semantics/acceptedVersio

Protein expression of transmembrane protease serine 4 in the gastrointestinal tract and in healthy, cancer, and SARS‑CoV‑2 infected lungs

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.Copyright: © Kerslake et al. In addition to the angiotensin‑converting enzyme 2 (ACE2), a number of host cell entry mediators have been identified for severe acute respiratory syndrome coronavirus‑2 (SARS‑CoV‑2), including transmembrane protease serine 4 (TMPRSS4). The authors have recently demonstrated the upregulation of TMPRSS4 in 11 different cancers, as well as its specific expression within the central nervous system using in silico tools. The present study aimed to expand the initial observations and, using immunohistochemistry, TMPRSS4 protein expression in the gastrointestinal (GI) tract and lungs was further mapped. Immunohistochemistry was performed on tissue arrays and lung tissues of patients with non‑small cell lung cancer with concurrent coronavirus disease 2019 (COVID‑19) infection using TMPRSS4 antibody. The results revealed that TMPRSS4 was abundantly expressed in the oesophagus, stomach, small intestine, jejunum, ileum, colon, liver and pancreas. Moreover, the extensive TMPRSS4 protein expression in the lungs of a deceased patient with COVID‑19 with chronic obstructive pulmonary disease and bronchial carcinoma, as well in the adjacent normal tissue, was demonstrated for the first time, at least to the best of our knowledge. On the whole, the immunohistochemistry data of the present study suggest that TMPRSS4 may be implicated in the broader (pulmonary and extra‑pulmonary) COVID‑19 symptomatology; thus, it may be responsible for the tropism of this coronavirus both in the GI tract and lungs.Cancer Treatment and Research Trust; University Hospitals Coventry and Warwickshire NHS Trust (grant no. 12899)

ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks

The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches to enabling deep neural networks (DNNs) in this domain, including pruning, quantization, compression, and binary neural networks (BNNs), but with the emergence of the "extreme edge", there is now a demand for even more efficient models. In order to meet the constraints of ultra-low-energy devices, we propose ULEEN, a model architecture based on weightless neural networks. Weightless neural networks (WNNs) are a class of neural model which use table lookups, not arithmetic, to perform computation. The elimination of energy-intensive arithmetic operations makes WNNs theoretically well suited for edge inference; however, they have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by BNNs to make significant strides in improving accuracy and reducing model size. We compare FPGA and ASIC implementations of an inference accelerator for ULEEN against edge-optimized DNN and BNN devices. On a Xilinx Zynq Z-7045 FPGA, we demonstrate classification on the MNIST dataset at 14.3 million inferences per second (13 million inferences/Joule) with 0.21 $\mu$s latency and 96.2% accuracy, while Xilinx FINN achieves 12.3 million inferences per second (1.69 million inferences/Joule) with 0.31 $\mu$s latency and 95.83% accuracy. In a 45nm ASIC, we achieve 5.1 million inferences/Joule and 38.5 million inferences/second at 98.46% accuracy, while a quantized Bit Fusion model achieves 9230 inferences/Joule and 19,100 inferences/second at 99.35% accuracy. In our search for ever more efficient edge devices, ULEEN shows that WNNs are deserving of consideration.Comment: 14 pages, 14 figures Portions of this article draw heavily from arXiv:2203.01479, most notably sections 5E and 5F.

Failure to complete adjuvant chemotherapy is associated with adverse survival in stage III colon cancer patients

Two recent North American studies have shown that completion of 5-fluorouracil (5FU)-based adjuvant chemotherapy is a major prognostic factor for the survival of elderly stage III colon cancer patients. The aim of the present study was to confirm this finding in a population-based series from Australia. The study cohort comprised 851 stage III colon cancer patients treated by surgery alone and 461 who initiated the Mayo chemotherapy regime. One-third of patients who initiated chemotherapy failed to complete more than three cycles of treatment. Independent predictors for failure to complete were treatment in district or rural hospitals, low socioeconomic index and treatment by a low-volume surgeon. Patients who failed to complete chemotherapy showed worse cancer-specific survival compared not only to those who completed treatment (HR=2.24; 95% confidence interval (CI) (1.66–3.03), P<0.001) but also to those treated by surgery alone (HR=1.37; 95% CI (1.09–1.72), P=0.008). The current and previous studies demonstrate the importance of completing adjuvant 5-FU-based chemotherapy for colon cancer. Further prospective studies are required to identify better the physiological and socioeconomic factors responsible for failure to complete chemotherapy so that appropriate improvements in health service delivery can be made

Fusidic acid and clindamycin resistance in community-associated, methicillin-resistant Staphylococcus aureus infections in children of Central Greece

<p>Abstract</p> <p>Introduction</p> <p>In Greece, fusidic acid and clindamycin are commonly used for the empiric therapy of suspected staphylococcal infections.</p> <p>Methods</p> <p>The medical records of children examined at the outpatient clinics or admitted to the pediatric wards of the University General Hospital of Larissa, Central Greece, with community-associated staphylococcal infections from January 2003 to December 2009 were reviewed.</p> <p>Results</p> <p>Of 309 children (0-14 years old), 21 (6.8%) had invasive infections and 288 (93.2%) skin and soft tissue infections (SSTIs). Thirty-five patients were ≤30 days of age. The proportion of staphylococcal infections caused by a community-associated methicillin-resistant <it>Staphylococcus aureus </it>(CA-MRSA) isolate increased from 51.5% (69 of 134) in 2003-2006 to 63.4% (111 of 175) in 2007-2009 (<it>P </it>= 0.037). Among the CA-MRSA isolates, 88.9% were resistant to fusidic acid, 77.6% to tetracycline, and 21.1% to clindamycin. Clindamycin resistance increased from 0% (2003) to 31.2% (2009) among the CA-MRSA isolates (<it>P </it>= 0.011). Over the 7-year period, an increase in multidrug-resistant CA-MRSA isolates was observed (<it>P </it>= 0.004). One hundred and thirty-one (93.6%) of the 140 tested MRSA isolates were Panton-Valentine leukocidin-positive. Multilocus sequence typing of 72 CA-MRSA isolates revealed that they belonged to ST80 (n = 61), ST30 (n = 6), ST377 (n = 3), ST22 (n = 1), and ST152 (n = 1). Resistance to fusidic acid was observed in ST80 (58/61), ST30 (1/6), and ST22 (1/1) isolates.</p> <p>Conclusion</p> <p>In areas with high rate of infections caused by multidrug-resistant CA-MRSA isolates, predominantly belonging to the European ST80 clone, fusidic acid and clindamycin should be used cautiously as empiric therapy in patients with suspected severe staphylococcal infections.</p