ABSTRACT
The severe acute respiratory syndrome coronavirus-2 (SARSCoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale. We acknowledge funding from NIH P41-GM10460, DOE CSGF (DE-FG02-97ER25308), Exascale Computing Project (17-SC-20-SC) and National Virtual Biotechnology Laboratory.Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.
ABSTRACT
The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100 × to 1000 × improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ∼1011 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine. © 2021 ACM.
ABSTRACT
COVID-19 has claimed more than 2.7 × 106 lives and resulted in over 124 × 106 infections. There is an urgent need to identify drugs that can inhibit SARS-CoV-2. We discuss innovations in computational infrastructure and methods that are accelerating and advancing drug design. Specifically, we describe several methods that integrate artificial intelligence and simulation-based approaches, and the design of computational infrastructure to support these methods at scale. We discuss their implementation, characterize their performance, and highlight science advances that these capabilities have enabled. © 2021 ACM.
ABSTRACT
The starting point of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV- 2) pandemic is a highly debated topic today. Despite the fact that a multitude of hypotheses have been issued in this context, this new strain beta-coronavirus is not only highly contagious, but also rapidly transmitted interpersonally through the perpetually disrupting the homeostasis. It constantly affects the lungs, and also the digestive tract according to the latest reports following its binding to the angiotensin-converting enzyme 2 (ACE2) receptors. Material and methods: In the present study we aimed to explain and describe some gastrointestinal deficiencies exhibited by the positive patients Therefore, the Visual Analogue Scale-IBS ( VAS-IBS) questionnaire was applied to a group of positive patients (n = 22) and compared to their sex-matched controls (n = 22). The confirmation for the SARS- CoV-2 infection was carried out by specific RT-qPCR testing. Results: We observed that the overall scores attributed to the spectrum of Physical symptoms, Mental health and Quality of life are significantly different among the COVID-19 group dependent on sex than in the control group. However, we did not find significant differences in terms of shape and stool consistencies (as evaluated by Bristol scale). Conclusions: In this study, we showed that several gastrointestinal symptoms (as evaluated by VAS-IBS scale) could occur during or following SARS-CoV-2 infection.
ABSTRACT
This paper presents preliminary work on the topic of emotion analysis on Twitter, in the context of the coronavirus pandemic in Portugal. We collected, curated and analyzed covid-related tweets of users in Portugal in order to understand the evolution of the six basic emotions reflected in these tweets. We analyzed tweets written in both English and Portuguese. In this first step of our work we correlate this information with key events of the evolution of the pandemic in Portugal during March, which was the most critical period in Portugal. We do so in an attempt to estimate the online manifestation of the psychological toll that this pandemic has on the overall well-being status of the general population. Our findings show that the sentiment analysis of covid-related tweets is consistent with our hypothesis that negative emotions would intensify as the pandemic progressed. The preliminary results obtained stand as proof of concept that the analysis of real-time tweets or other social media messages through sentiment analysis can be an important tool for behavioural and well-being tracking.
ABSTRACT
Aims The outbreak of the corona-virus disease 2019 (COVID-19) has led to significant changes in endoscopy unitsworldwide, with potential impact on patients' welfare as well as on endoscopy training. We aimed to assess the real-lifeimpact of COVID-19 on the endoscopy unit in a tertiary care center from Romania. Methods We performed a service evaluation using the local endoscopy database. Two time periods were analyzed, namelyfrom the 1 of March to 15 September 2020, during the COVID-19 pandemic and a similar period between 1 of March -15 September 2019. Results There was a 6.2 fold decrease of the number of endoscopic procedures as a result of COVID-19. The mostimportant reduction was found in colonoscopy, from 916 to 42 procedures, P < 0.001, followed by flexible sigmoidoscopy, from 189 to 14 procedures, P = 0.009, upper gastrointestinal endoscopy, from 2269 to 401 procedures, P = 0.006, and ERCP, from 234 to 125 procedures, P < 0.001. The percentage of emergency procedures increased (38.8 % vs 26.2 %, P < 0.001),as well as the rate of endoscopies performed for upper GI bleeding (42.5 % vs 24.4 %, respectively, P < 0.001). Thedetection of cancers was considerably reduced (57 compared to 249, P = 0.001). Surprisingly the rate of complications waslower and the success of the procedures higher (7.6 % vs 19.2 %, P < 0.001, and 94.2 % vs 90.7 %, respectively). Fellowparticipation was also reduced from 90 % before the pandemic to 40.9 % during COVID-19 time (P < 0.001). Conclusions The COVID-19 pandemic has significantly altered the workflow of the endoscopy unit, lowering the number ofprocedures performed and potentially compromising the early detection of cancers.