Corpus overview


MeSH Disease

Human Phenotype


    displaying 1 - 10 records in total 17
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    Quantum Machine Learning for Drug Discovery

    Authors: Kushal Batra; Kimberley M. Zorn; Daniel H. Foil; Eni Minerali; Victor O. Gawriljuk; Thomas R. Lane; sean ekins

    doi:10.26434/chemrxiv.12781232.v1 Date: 2020-08-10 Source: ChemRxiv

    The growing public and private datasets focused on small molecules screened against biological targets or whole organisms 1 provides a wealth of drug discovery relevant data. Increasingly this is used to create machine learning models which can be used for enabling target-based design 2-4, predict on- or off-target effects and create scoring functions 5,6. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large datasets and thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on QC. Here we show how to achieve compression with datasets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands (whole cell screening datasets for plague MESHD and M. tuberculosis MESHD) with SVM and data re-uploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This illustrates a quantum advantage for drug discovery to build upon in future.

    Signatures and mechanisms of efficacious therapeutic ribonucleotides against SARS-CoV-2 revealed by analysis of its replicase using magnetic tweezers

    Authors: Mona Seifert; Subhas C. Bera; Pauline van Nies; Robert N. Kirchdoerfer; Ashleigh Shannon; Thi-Tuyet-Nhung Le; Tyler L. Grove; Flavia S. Papini; Jamie J. Arnold; Steven C. Almo; Bruno Canard; Martin Depken; Craig E. Cameron; David Dulin

    doi:10.1101/2020.08.06.240325 Date: 2020-08-06 Source: bioRxiv

    Coronavirus Disease MESHD 2019 (COVID-19) results from an infection MESHD infection by the severe HP by the severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2), the third coronavirus outbreak to plague MESHD humanity this century. Currently, the most efficacious therapeutic against SARS-CoV-2 infection MESHD is the Remdesivir (RDV), an adenine-like ribonucleotide analogue that is very efficiently incorporated by the SARS-CoV-2 replicase. Understanding why RDV is so well incorporated will facilitate development of even more effective therapeutics. Here, we have applied a high-throughput, single-molecule, magnetic-tweezers platform to study thousands of cycles of nucleotide addition by the SARS-CoV-2 replicase in the absence and presence of RDV, a Favipiravir-related analog (T-1106), and the endogenously produced ddhCTP. Our data are consistent with two parallel catalytic pathways of the replicase: a high-fidelity catalytic (HFC) state and a low-fidelity catalytic (LFC) state, the latter allowing the slow incorporation of both cognate and non-cognate nucleotides. ddhCTP accesses HFC, T-1106 accesses LFC as a non-cognate nucleotide, while RDV efficiently accesses both LFC pathway. In contrast to previous reports, we provide unequivocal evidence against RDV functioning as a chain terminator. We show that RDV incorporation transiently stalls the replicase, only appearing as termination events when traditional, gel-based assays are used. The efficiency of ddhCTP utilization by the SARS-CoV-2 replicase suggests suppression of its synthesis during infection MESHD, inspiring new therapeutic strategies. Use of this experimental paradigm will be essential to the development of therapeutic nucleotide analogs targeting polymerases.

    A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses

    Authors: Claire Donnat; Nina Miolane; Frederick de St Pierre Bunbury; Jack Kreindler

    id:2007.13847v1 Date: 2020-07-27 Source: arXiv

    Computer-Aided Diagnosis has shown stellar performance SERO in providing accurate medical diagnoses across multiple testing modalities (medical images, electrophysiological signals, etc.). While this field has typically focused on fully harvesting the signal provided by a single (and generally extremely reliable) modality, fewer efforts have utilized imprecise data lacking reliable ground truth labels. In this unsupervised, noisy setting, the robustification and quantification of the diagnosis uncertainty become paramount, thus posing a new challenge: how can we combine multiple sources of information -- often themselves with vastly varying levels of precision and uncertainty -- to provide a diagnosis estimate with confidence bounds? Motivated by a concrete application in antibody testing SERO, we devise a Stochastic Expectation-Maximization algorithm that allows the principled integration of heterogeneous, and potentially unreliable, data types. Our Bayesian formalism is essential in (a) flexibly combining these heterogeneous data sources and their corresponding levels of uncertainty, (b) quantifying the degree of confidence associated with a given diagnostic, and (c) dealing with the missing values that typically plague MESHD medical data. We quantify the potential of this approach on simulated data, and showcase its practicality by deploying it on a real COVID-19 immunity study.

    System for Control and Management of Data Privacy of Patients with COVID-19

    Authors: Arielle Verri Lucca; Rodrigo Luchtenberg; Leonardo Garcez de Paula Conceicao; Luis Augusto Silva; Raúl García Ovejero; María Navarro-Cáceres; Valderi Reis Quietinho Leithardt

    id:10.20944/preprints202007.0369.v1 Date: 2020-07-17 Source:

    The COVID-19 pandemic plagues MESHD the whole world, bringing numerous challenges which need to be addressed. One of them is the privacy of patient data. There are several problems related to data privacy in IoT environments, the use of applications, devices, and functionalities in hospital processes. Therefore, we have compared works from the literature and developed a taxonomy consisting of the requirements necessary to control patient privacy data in a hospital setting in the current pandemic. Based on the studies, an application was modeled and implemented. According to the tests and comparisons drawn between the variables, the application yielded satisfactory results.

    Scientific comment on "Tail risk of contagious diseases MESHD"

    Authors: Alvaro Corral

    id:2007.06876v1 Date: 2020-07-14 Source: arXiv

    Cirillo and Taleb [Nature Phys. 16, 606-613 (2020)] study the size of major epidemics in human history in terms of the number of fatalities. Using the figures from 72 epidemics, from the plague MESHD of Athens (429 BC) to the COVID-19 (2019-2020), they claim that the resulting fatality distribution is ``extremely fat-tailed'', i.e., asymptotically a power law. This has important consequences for risk, as the mean value of the fatality distribution becomes infinite. Reanalyzing the same data, we find that, although the data may be compatible with a power-law tail, these results are not conclusive, and other distributions, not fat-tailed, could explain the data equally well. Simulation of a log-normally distributed random variable provides synthetic data whose statistics are undistinguishable from the statistics of the empirical data.

    Rapid Systematic Review Exploring Historical and Present Day National and International Governance during Pandemics

    Authors: Elizabeth Lowry; Henock Taddese; Leigh R Bowman

    doi:10.1101/2020.07.07.20148239 Date: 2020-07-08 Source: medRxiv

    Introduction Pandemics have plagued MESHD mankind since records began, and while non- communicable disease MESHD pandemics are more common in high-income nations, infectious disease MESHD pandemics continue to affect all countries worldwide. To mitigate impact, national pandemic preparedness and response policies remain crucial. And in response to emerging pathogens of pandemic potential, public health policies must be both dynamic and adaptive. Yet, this process of policy change and adaptation remains opaque. Accordingly, this rapid systematic review will synthesise and analyse evaluative policy literature to develop a roadmap of policy changes that have occurred after each pandemic event, throughout both the 20th and 21st Century, in order to better inform future policy development. Methods and Analysis A rapid systematic review will be conducted to assimilate and synthesise both peer-reviewed articles and grey literature that document the then current pandemic preparedness policy, and the subsequent changes to that policy, across high-, middle- and low-income countries. The rapid review will follow the PRISMA guidelines, and the literature search will be performed across five relevant databases, as well as various government websites to scan for grey literature. Articles will be screen against pre-agreed inclusion/ exclusion criteria, and data will be extracted using a pre-defined charting table. Ethics and Dissemination All data rely on secondary, publicly available data sources; therefore no ethical clearance is required. Upon completion, the results of this study will be disseminated via the Imperial College London Community and published in an open access, peer-reviewed journal.

    Evaluation of Contemporary Convolutional Neural Network Architectures for Detecting COVID-19 from Chest Radiographs

    Authors: Nikita Albert

    id:2007.01108v1 Date: 2020-06-30 Source: arXiv

    Interpreting chest radiograph, a.ka. chest x-ray, images is a necessary and crucial diagnostic tool used by medical professionals to detect and identify many diseases MESHD that may plague MESHD a patient. Although the images themselves contain a wealth of valuable information, their usefulness may be limited by how well they are interpreted, especially when the reviewing radiologist may be fatigued MESHD fatigued HP or when or an experienced radiologist is unavailable. Research in the use of deep learning models to analyze chest radiographs yielded impressive results where, in some instances, the models outperformed practicing radiologists. Amidst the COVID-19 pandemic, researchers have explored and proposed the use of said deep models to detect COVID-19 infections MESHD from radiographs as a possible way to help ease the strain on medical resources. In this study, we train and evaluate three model architectures, proposed for chest radiograph analysis, under varying conditions, find issues that discount the impressive model performances SERO proposed by contemporary studies on this subject, and propose methodologies to train models that yield more reliable results.. Code, scripts, pre-trained models, and visualizations are available at

    The κ-statistics approach to epidemiology

    Authors: Giorgio Kaniadakis; Mauro M. Baldi; Thomas S. Deisboeck; Giulia Grisolia; Dionissios T. Hristopulos; Antonio M. Scarfone; Amelia Sparavigna; Tatsuaki Wada; Umberto Lucia

    doi:10.21203/ Date: 2020-06-13 Source: ResearchSquare

    A great variety of complex physical, natural and artificial systems are governed by statistical distributions, which often follow a standard exponential function in the bulk, while their tail obeys the Pareto power law. The recently introduced k-statistics framework predicts distribution functions with this feature. A growing number of applications in different fields of investigation are beginning to prove the relevance and effectiveness of k-statistics in fitting empirical data. In this paper, we use k-statistics to formulate a statistical approach for epidemiological analysis. We validate the theoretical results by fitting the derived k-Weibull distributions with data from the plague MESHD pandemic of 1417 in Florence as well as partial data (until April 16, 2020) from the COVID-19 pandemic in China. The fact that both the approximate dataset of the Florence plague MESHD and the partial data of the Covid-19 pandemic in China are well described by means of the proposed model suggests that the k-deformed Weibull model is relevant and that both datasets faithfully represent the spreading of the epidemics.

    Oscillations in USA COVID-19 Incidence and Mortality Data reflect societal factors

    Authors: Aviv Bergman; Yehonatan Sella; Peter Agre; Arturo Casadevall

    doi:10.1101/2020.06.08.20123786 Date: 2020-06-12 Source: medRxiv

    The COVID-19 pandemic currently in process differs from other infectious disease MESHD calamities that have previously plagued MESHD humanity in the vast amount of information that is produces each day, which includes daily estimates of the disease MESHD incidence and mortality data. Apart from providing actionable information to public health authorities on the trend of the pandemic, the daily incidence reflects the process of disease MESHD in a susceptible population and thus reflects the pathogenesis of COVID-19, the public health response and diagnosis and reporting. Both daily new cases and daily mortality data in the US exhibit periodic oscillatory patterns. By analyzing NYC and LA testing data, we demonstrate that this oscillation in the number of cases can be strongly explained by the daily variation in testing. This seems to rule out alternative hypotheses such as increased infections MESHD on certain days of the week as driving this oscillation. Similarly, we show that the apparent oscillation in mortality in the US data is mostly an artifact of reporting, which disappears in datasets that record death MESHD by episode date, such as the NYC and LA datasets. Periodic oscillations in COVID-19 incidence and mortality data reflect testing and reporting practices and contingencies. Thus, these contingencies should be considered first prior to suggesting social or biological mechanisms.

    COVID-19 and HIV co- infection MESHD: a living systematic evidence map of current research

    Authors: Gwinyai Masukume; Witness Mapanga; Doreen Sindisiwe van Zyl

    doi:10.1101/2020.06.04.20122606 Date: 2020-06-07 Source: medRxiv

    Abstract The world currently faces two ongoing devastating pandemics. These are the new severe acute respiratory syndrome MESHD coronavirus 2/ coronavirus disease MESHD 2019 (SARS-CoV-2/COVID-19) and the prior human immunodeficiency HP virus/acquired immune deficiency syndrome MESHD (HIV/AIDS) pandemics. The literature regarding the confluence of these global plagues MESHD expands at pace. A systematic search of the literature considering COVID-19 and HIV co- infection MESHD was performed. After five months, from the beginning of the COVID-19 pandemic, there were at least thirty-five studies reported from thirteen countries. These ranged from individual case reports and series to cohort studies. Based on studies that could be extrapolated to the general population, co-infected individuals with suppressed HIV viral loads did not have disproportionate COVID-19 sickness and death MESHD. At least four patients, newly diagnosed with HIV recovered from COVID-19. Current evidence suggests that co-infected patients should be treated like the general population. This ongoing living systematic evidence map of contemporary primary SARS-CoV-2 and HIV co- infection MESHD research provides a platform for researchers, policy makers, clinicians and others to more quickly discover and build relevant insights.

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MeSH Disease
Human Phenotype

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