Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
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    Dysregulated transcriptional responses to SARS-CoV-2 in the periphery support novel diagnostic approaches

    Authors: Micah T McClain; Florica J Constantine; Ricardo Henao; Yiling Liu; Ephraim L Tsalik; Thomas W Burke; Julie M Steinbrink; Elizabeth Petzold; Bradly P Nicholson; Robert Rolfe; Bryan D Kraft; Matthew S Kelly; Gregory D Sempowski; Thomas N Denny; Geoffrey S Ginsburg; Christopher W Woods

    doi:10.1101/2020.07.20.20155507 Date: 2020-07-26 Source: medRxiv

    In order to elucidate novel aspects of the host response to SARS-CoV-2 we performed RNA sequencing on peripheral blood SERO samples across 77 timepoints from 46 subjects with COVID-19 and compared them to subjects with seasonal coronavirus, influenza, bacterial pneumonia MESHD pneumonia HP, and healthy controls. Early SARS-CoV-2 infection MESHD triggers a conserved transcriptomic response in peripheral blood SERO that is heavily interferon-driven but also marked by indicators of early B-cell activation and antibody SERO production. Interferon responses during SARS-CoV-2 infection MESHD demonstrate unique patterns of dysregulated expression compared to other infectious and healthy states. Heterogeneous activation of coagulation and fibrinolytic pathways are present in early COVID-19, as are IL1 and JAK/STAT signaling pathways, that persist into late disease MESHD. Classifiers based on differentially expressed genes accurately distinguished SARS-CoV-2 infection MESHD from other acute illnesses (auROC 0.95). The transcriptome in peripheral blood SERO reveals unique aspects of the immune response in COVID-19 and provides for novel biomarker-based approaches to diagnosis.

    An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis MESHD Detection Using X-Ray Images in Resource Limited Settings

    Authors: Ali H. Al-Timemy; Rami N. Khushaba; Zahraa M. Mosa; Javier Escudero

    id:2007.08223v1 Date: 2020-07-16 Source: arXiv

    Clinicians in the frontline need to assess quickly whether a patient with symptoms indeed has COVID-19 or not. The difficulty of this task is exacerbated in low resource settings that may not have access to biotechnology tests. Furthermore, Tuberculosis MESHD (TB) remains a major health problem in several low- and middle-income countries and its common symptoms include fever MESHD fever HP, cough MESHD cough HP and tiredness, similarly to COVID-19. In order to help in the detection of COVID-19, we propose the extraction of deep features (DF) from chest X-ray images, a technology available in most hospitals, and their subsequent classification using machine learning methods that do not require large computational resources. We compiled a five-class dataset of X-ray chest images including a balanced number of COVID-19, viral pneumonia MESHD pneumonia HP, bacterial pneumonia MESHD pneumonia HP, TB, and healthy cases. We compared the performance SERO of pipelines combining 14 individual state-of-the-art pre-trained deep networks for DF extraction with traditional machine learning classifiers. A pipeline consisting of ResNet-50 for DF computation and ensemble of subspace discriminant classifier was the best performer in the classification of the five classes, achieving a detection accuracy of 91.6+ 2.6% (accuracy + 95% Confidence Interval). Furthermore, the same pipeline achieved accuracies of 98.6+1.4% and 99.9+0.5% in simpler three-class and two-class classification problems focused on distinguishing COVID-19, TB and healthy cases; and COVID-19 and healthy images, respectively. The pipeline was computationally efficient requiring just 0.19 second to extract DF per X-ray image and 2 minutes for training a traditional classifier with more than 2000 images on a CPU machine. The results suggest the potential benefits of using our pipeline in the detection of COVID-19, particularly in resource-limited settings and it can run with limited computational resources.

    Risk of Transmission TRANS of infection MESHD to Healthcare Workers delivering Supportive Care for Coronavirus Pneumonia MESHD Pneumonia HP;A Rapid GRADE Review

    Authors: TK Luqman Arafath; Sandeep S Jubbal; Elakkat D Gireesh; Jyothi Margapuri; Hanumantha Rao Jogu; Hitesh Patni; Tyler Thompson; Arsh Patel; Amirahwaty Abdulla; Suma Menon; Sudheer Penupolu

    doi:10.1101/2020.07.06.20146712 Date: 2020-07-08 Source: medRxiv

    Abstract Background: Avenues of treatment currently implemented for Covid-19 pandemic are largely supportive in nature. Non -availability of an effective antiviral treatment makes supportive care for acute hypoxic respiratory failure HP is the most crucial intervention. Highly contagious nature of Covid-19 had created stress and confusion MESHD confusion HP among front line Health Care Workers (HCWs) regarding infectious risk of supportive interventions and best preventive strategies. Purpose: To analyze and summarize key evidence from published literature exploring the risk of transmission TRANS of Covid-19 related to common supportive care interventions in hospitalized patients and effectiveness of currently used preventive measures in hospital setting. Data Sources: Curated Covid-19 literature from NCBI Computational Biology Branch ,Embase and Ovid till May 20,2020.Longitudinal and reference search till June 28,2020 Study Selection: Studies pertaining to risk of infection TRANS risk of infection TRANS infection MESHD to HCWs providing standard supportive care of hospitalized Covid-19 mainly focusing on respiratory support interventions.Indirect studies from SARS,MERS or other ARDS pathology caused by infectious agents based on reference tracking and snow ball search . Clinical, Healthy volunteer and mechanistic studies were included. Two authors independently screened studies for traditional respiratory supportive-care ( Hypoxia MESHD management, ventilatory support and pulmonary toileting) related transmission TRANS of viral or bacterial pneumonia MESHD pneumonia HP to HCWs. Data Extraction: Two authors (TK and SP) independently screened articles and verified for consensus. Quality of studies and level of evidence was assessed using Oxford Center for Evidence Based Medicine (OCEBM) , Newcastle - Ottawa quality assessment Scale for observational studies and Grading of Recommendations Assessment, Development and Evaluation (GRADE) system for grading evidence. Data Synthesis: 21 studies were eligible for inclusion. In 11 mechanistic studies, 7 were manikin based,1 was in the setting of GNB pneumonia MESHD pneumonia HP ,2 were healthy volunteer study and 1 was heterogenous setting.Out of 10 clinical studies ,5 were case controlled and 6 were cohort studies. Risk of corona virus transmission TRANS was significantly high in HCWs performing or assisting endotracheal intubation or contact with respiratory secretion.(Moderate certainty evidence, GRADE B) Safety of nebulization treatment in corona virus pneumonia MESHD pneumonia HP patients are questionable(Low certainty evidence, GRADE C).Very low certainty evidence exist for risk of transmission TRANS with conventional HFNC (GRADE D) and NIV (GRADE D),CPR (GRADE D),Bag and mask ventilation(GRADE D).Moderate certainty evidence exist for protective effect of wearing a multilayered mask, gown , eye protection and formal training for PPE use (GRADE B).Low certainty evidence exist for transmission risk TRANS with bag and mask ventilation, suctioning before and after intubation and prolonged exposure (GRADE C).Certainty of evidence for wearing gloves,post exposure hand washing and wearing N 95 mask is low(GRADE C). Limitations: This study was limited to articles with English abstract. Highly dynamic nature of body of literature related to Covid-19, frequent updates were necessary even during preparation of manuscript and longitudinal search was continued even after finalizing initial search. Due to the heterogeneity and broad nature of the search protocol, quantitative comparisons regarding the effectiveness of included management strategies could not be performed. Direct evidence was limited due to poor quality and non-comparative nature of available Covid-19 reporting. Conclusions: Major risk factors for transmission TRANS of corona virus infection MESHD were, performing or assisting endotracheal intubation and contact with respiratory secretion. Risk of transmission TRANS with HFNC or NIV can be significantly decreased by helmet interface, modified exhalation circuit or placing a properly fitting face mask over patient interface of HFNC. Evidence for risk of transmission TRANS with CPR, suctioning before or after intubation or bag and mask ventilation of very low certainty. Significant protective factors are Formal training for PPE use, consistently wearing mask, gown and eye protection. Primary Funding Source: None Disclosure: None of the authors have any conflict of interest to disclose.

    Automatic Detection of COVID-19 and Pneumonia MESHD Pneumonia HP from Chest X-Ray using Transfer Learning

    Authors: Sarath Pathari; Rahul U

    doi:10.1101/2020.05.27.20100297 Date: 2020-05-29 Source: medRxiv

    In this study, a dataset of X-ray images from patients with common viral pneumonia MESHD pneumonia HP, bacterial pneumonia MESHD pneumonia HP, confirmed Covid-19 disease MESHD was utilized for the automatic detection of the Coronavirus disease MESHD. The point of the investigation is to assess the exhibition of cutting edge convolutional neural system structures proposed over the ongoing years for clinical picture order. In particular, the system called Transfer Learning was received. With transfer learning, the location of different variations from the norm in little clinical picture datasets is a reachable objective, regularly yielding amazing outcomes. The datasets used in this trial. Firstly, a collection of 24000 X-ray images includes 6000 images for confirmed Covid-19 disease MESHD,6000 confirmed common bacterial pneumonia MESHD pneumonia HP and 6000 images of normal conditions. The information was gathered and expanded from the accessible X-Ray pictures on open clinical stores. The outcomes recommend that Deep Learning with X-Ray imaging may separate noteworthy biomarkers identified with the Covid-19 sickness, while the best precision, affectability, and particularity acquired is 97.83%, 96.81%, and 98.56% individually.

    Differentiating COVID-19 from other types of pneumonia MESHD pneumonia HP with convolutional neural networks

    Authors: Ilker Ozsahin; Confidence Onyebuchi; Boran Sekeroglu

    doi:10.1101/2020.05.26.20113761 Date: 2020-05-27 Source: medRxiv

    INTRODUCTION A widely-used method for diagnosing COVID-19 is the nucleic acid test based on real-time reverse transcriptase-polymerase chain reaction (RT-PCR). However, the sensitivity SERO of real time RT-PCR tests is low and it can take up to 8 hours to receive the test results. Radiologic methods can provide higher sensitivity SERO. The aim of this study is to investigate the use of X-ray and convolutional neural networks for the diagnosis of COVID-19 and to differentiate it from viral and/or bacterial pneumonia MESHD pneumonia HP, as 2-class ( bacterial pneumonia MESHD pneumonia HP vs COVID-19 and viral pneumonia MESHD pneumonia HP vs COVID-19) and 3-class ( bacterial pneumonia MESHD pneumonia HP, COVID-19, and healthy group (BCH), and among viral pneumonia MESHD pneumonia HP, COVID-19, and healthy group (VCH)) experiments. METHODS 225 COVID-19, 1,583 healthy control, 2,780 bacterial pneumonia MESHD pneumonia HP, and 1,493 viral pneumonia MESHD pneumonia HP chest X-ray images were used. 2-class- and 3-class-experiments were performed with different convolutional neural network (ConvNet) architectures, with different variations of convolutional layers and fully-connected layers. RESULTS The results showed that bacterial pneumonia MESHD pneumonia HP vs COVID-19 and viral pneumonia MESHD pneumonia HP vs COVID-19 reached a mean ROC AUC of 97.32% and 96.80%, respectively. In the 3-class-experiments, macro-average F1 scores of 95.79% and 94.59% were obtained in terms of detecting COVID-19 among BCH and VCH, respectively. CONCLUSIONS The ConvNet was able to distinguish the COVID-19 images among non-COVID-19 images, namely bacterial and viral pneumonia MESHD pneumonia HP as well as normal X-ray images.

    COVID-19 detection from chest X-Ray images using Deep Learning and Convolutional Neural Networks

    Authors: Antonios Makris; Ioannis Kontopoulos; Konstantinos Tserpes

    doi:10.1101/2020.05.22.20110817 Date: 2020-05-24 Source: medRxiv

    The COVID-19 pandemic in 2020 has highlighted the need to pull all available resources towards the mitigation of the devastating effects of such "Black Swan" events. Towards that end, we investigated the option to employ technology in order to assist the diagnosis of patients infected by the virus. As such, several state-of-the-art pre-trained convolutional neural networks were evaluated as of their ability to detect infected patients from chest X-Ray images. A dataset was created as a mix of publicly available X-ray images from patients with confirmed COVID-19 disease MESHD, common bacterial pneumonia MESHD pneumonia HP and healthy individuals. To mitigate the small number of samples, we employed transfer learning, which transfers knowledge extracted by pre-trained models to the model to be trained. The experimental results demonstrate that the classification performance SERO can reach an accuracy of 95% for the best two models.

    IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images

    Authors: Juliana Carneiro Gomes; Valter Augusto de Freitas Barbosa; Maíra Araújo de Santana; Jonathan Bandeira; Mêuser Jorge Silva Valença; Ricardo Emmanuel de Souza; Aras Masood Ismael; Wellington Pinheiro dos Santos

    doi:10.21203/rs.3.rs-28716/v1 Date: 2020-05-13 Source: ResearchSquare

    In late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia MESHD pneumonia HP. There is still no specific treatment and diagnosis for the disease MESHD. The standard diagnostic method for pneumonia MESHD pneumonia HP is chest X-ray image. There are many advantages to using Covid-19 diagnostic X-rays: low cost, fast and widely available. We propose an intelligent system to support diagnosis by X-ray images.We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Support vector machines stood out, reaching an average accuracy of 89.78%, average recall SERO and sensitivity SERO of 0.8979, and average precision and specificity of 0.8985 and 0.9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia MESHD pneumonia HP, with low computational cost.

    Outcomes among HIV-positive patients hospitalized with COVID-19

    Authors: Savannah Karmen-Tuohy; Philip M. Carlucci; Ioannis M. Zacharioudakis; Fainareti N. Zervou; Gabriel Rebick; Elizabeth Klein; Jenna Reich; Simon Jones; Joseph Rahimian

    doi:10.1101/2020.05.07.20094797 Date: 2020-05-12 Source: medRxiv

    Background: SARS-CoV-2 infection MESHD continues to cause significant morbidity and mortality worldwide. Preliminary data on SARS-CoV-2 infection MESHD suggests that some immunocompromised hosts experience worse outcomes. We performed a retrospective matched cohort study to characterize outcomes in HIV-positive patients with SARS-CoV-2 infection MESHD. Methods: Leveraging data collected from electronic medical records for all patients hospitalized at NYU Langone Health with COVID-19 between March 2, 2020 and April 23, 2020, we matched 21 HIV-positive patients to 42 non-HIV patients using a greedy nearest neighbor algorithm. Admission characteristics, laboratory results, and hospital outcomes were recorded and compared between the two groups. Results: While there was a trend toward increased rates of ICU admission, mechanical ventilation, and mortality in HIV-positive patients, these differences were not statistically significant. Rates for these outcomes in our cohort are similar to those previously published for all patients hospitalized with COVID-19. HIV-positive patients had significantly higher admission and peak CRP values. Other inflammatory markers did not differ significantly between groups, though HIV-positive patients tended to have higher peak values during their clinical course. Three HIV-positive patients had superimposed bacterial pneumonia MESHD pneumonia HP with positive sputum cultures, and all three patients expired during hospitalization. There was no difference in frequency of thrombotic events or myocardial infarction MESHD myocardial infarction HP between these groups. Conclusion: This study provides evidence that HIV coinfection MESHD does not significantly impact presentation, hospital course, or outcomes of patients infected with SARS-CoV-2, when compared to matched non-HIV patients. A larger study is required to determine if the trends we observed apply to all HIV-positive patients.

    IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images

    Authors: Juliana Carneiro Gomes; Valter Augusto de Freitas Barbosa; Maira Araujo de Santana; Jonathan Bandeira; Meuser Jorge Silva Valenca; Ricardo Emmanuel de Souza; Aras Masood Ismael; Wellington Pinheiro dos Santos

    doi:10.1101/2020.05.05.20092346 Date: 2020-05-09 Source: medRxiv

    In late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia MESHD pneumonia HP. There is still no specific treatment and diagnosis for the disease MESHD. The standard diagnostic method for pneumonia MESHD pneumonia HP is chest X-ray image. There are many advantages to using Covid-19 diagnostic X-rays: low cost, fast and widely available. We propose an intelligent system to support diagnosis by X-ray images.We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Support vector machines stood out, reaching an average accuracy of 89:78%, average recall SERO and sensitivity SERO of 0:8979, and average precision and specificity of 0:8985 and 0:9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia MESHD pneumonia HP, with low computational cost.

    Patient-Led COVID-19 Triage Systems and Case Fatality Rates: A Comparative Study Between Singapore, Japan, Norway, the USA and the UK.

    Authors: Fatma Mansab; Sohail Bhatti; Daniel Goyal

    doi:10.1101/2020.04.28.20084079 Date: 2020-05-05 Source: medRxiv

    Introduction: The case fatality rate from COVID-19 differs markedly around the world. There are likely a number of factors one can attribute to such disparity, not least of which is differing healthcare models and approaches. Here, we examine the COVID-19 related health advice issued by six different countries, specifically examining the patient-led triage pathways in each country. Methods: A simulation study was conducted on current, nationwide, patient-led triage systems from three countries with low case fatality rates (Singapore, Norway and Japan) and two countries with high case fatality rates (the USA and the UK). Cases were simulated by clinicians to approximate typical COVID-19 presentations (mild, severe and critical), and COVID-19 mimickers ( sepsis MESHD sepsis HP, bacterial pneumonia MESHD pneumonia HP). The same simulations were applied to each of the five countrys patient-led triage systems, and the recommendations to refer on for medical care or to advise to stay at home were recorded and compared. Statistical analysis examined the potential correlation between triage outcomes and case fatality rates. Results: Patient-led triage systems from Singapore, Japan and Norway maintained a low threshold for advising clinical contact for patients with possible COVID-19 (88 to 100% of cases were referred). Patient-led triage systems from the USA and the UK maintained high thresholds for advising contact with either call centre support or clinical contact (28 and 33% of cases were referred, respectively), and triaged the majority of cases home with no further healthcare input. There was a strong inverse correlation between percentage of cases referred and the nations case fatality rate (Pearsons Correlation = -0.642, p = 0.01). Conclusion: In this simulation study, the triage algorithms of Singapore, Norway and Japan successfully identified severe COVID-19 and triaged such cases to medical care. The USA triage system and the UKs triage system performed poorly, failing to identify severe COVID-19 infection MESHD and sepsis MESHD sepsis HP. In this study, the performance SERO of national COVID-19 triage systems strongly correlate with case fatality rates.

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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