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MeSH Disease


    displaying 701 - 710 records in total 2150
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    Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates

    Authors: Tongtong Huang; Yan Chu; Shayan Shams; Yejin Kim; Genevera Allen; Ananth V Annapragada; Devika Subramanian; Ioannis Kakadiaris; Assaf Gottlieb; Xiaoqian Jiang

    id:2008.05909v1 Date: 2020-08-10 Source: arXiv

    Objective: We study the influence of local reopening policies on the composition of the infectious population and their impact on future hospitalization and mortality rates. Materials and Methods: We collected datasets of daily reported hospitalization and cumulative morality of COVID 19 in Houston, Texas, from May 1, 2020 until June 29, 2020. These datasets are from multiple sources (USA FACTS, Southeast Texas Regional Advisory Council COVID 19 report, TMC daily news, and New York Times county level mortality reporting). Our model, risk stratified SIR HCD MESHD uses separate variables to model the dynamics of local contact (e.g., work from home) and high contact (e.g., work on site) subpopulations while sharing parameters to control their respective $ R_0 TRANS(t)$ over time. Results: We evaluated our models forecasting performance SERO in Harris County, TX (the most populated county in the Greater Houston area) during the Phase I and Phase II reopening. Not only did our model outperform other competing models, it also supports counterfactual analysis to simulate the impact of future policies in a local setting, which is unique among existing approaches. Discussion: Local mortality and hospitalization are significantly impacted by quarantine and reopening policies. No existing model has directly accounted for the effect of these policies on local trends in infections, hospitalizations, and deaths in an explicit and explainable manner. Our work is an attempt to close this important technical gap to support decision making. Conclusion: Despite several limitations, we think it is a timely effort to rethink about how to best model the dynamics of pandemics under the influence of reopening policies.

    Automated COVID-19 MESHD Detection from Chest X-Ray Images : A High Resolution Network (HRNet) Approach 

    Authors: Sifat Ahmed; Tonmoy Hossain; Oishee Bintey Hoque; Sujan Sarker; Sejuti Rahman; Faisal Muhammad Shah

    doi:10.21203/ Date: 2020-08-09 Source: ResearchSquare

    Background/ introduction: The pandemic, originated by novel coronavirus 2019 ( COVID-19 MESHD), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 MESHD in the human body to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. Our intensive background studies show that, the works till now are not efficient to produce an unbiased detection result.Method: In this work, we propose an automated COVID-19 MESHD classification method, utilizing available COVID and non-COVID X-Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes.Results: To evaluate the proposed method, several baseline experiments have been performed employing n umerous deep learning architectures. MESHD With extensive experiment, we got a significant result of 99.26% accuracy, 98.53% sensitivity SERO, and 98.82% specificity with HRNet which surpasses the performances SERO of the existing models.Conclusions: Finally, we conclude that our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

    A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus ( COVID-19 MESHD)

    Authors: Md. Milon Islam; Fakhri Karray; Reda Alhajj; Jia Zeng

    id:2008.04815v1 Date: 2020-08-09 Source: arXiv

    Novel coronavirus ( COVID-19 MESHD) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence SERO rate of COVID-19 MESHD is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19 MESHD. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 MESHD diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance SERO measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 MESHD detection and probable future trends in this research area. This paper is intended to provide experts (medical or otherwise) and technicians with new insights into the ways deep learning techniques are used in this regard and how they potentially further works in combatting the outbreak of COVID-19 MESHD.

    Enhancement of SARS-CoV-2 Receptor Binding Domain -CR3022 Human Antibody SERO Binding Affinity via in Silico Engineering Approach

    Authors: fateme sefid; zahra payandeh; Ghasem Azamirad; Behzad Mansoori; Behzad Baradaran; Mohammad Reza Sadeghi

    doi:10.21203/ Date: 2020-08-09 Source: ResearchSquare

    Background: The nCoV-2019 is a cause of COVID-19 MESHD disease. The surface spike glycoprotein (S), which is necessary for virus entry through the intervention of the host receptor and it mediates virus-host membrane fusion, is the primary coronavirus antigen (Ag). The angiotensin-converting enzyme 2 (ACE2) is reported to be the effective human receptor for SARS-CoVs 2. ACE2 receptor can be prevented by neutralizing antibodies SERO (nAbs) such as CR3022 targeting the virus receptor-binding site. Considering the importance of computational docking, and affinity maturation we aimed to find the important amino acids of the CR3022 antibody SERO (Ab). These amino acids were then replaced by other amino acids to improve Ab-binding affinity to a receptor-binding domain (RBD) of the 2019-nCoV spike protein. Finally, we measured the binding affinity of Ab variants to the Ag. Result: Our findings disclosed that several variant mutations could successfully improve the characteristics of the Ab binding compared to the normal antibodies SERO. Conclusion: The modified antibodies SERO may be possible candidates for stronger affinity binding to Ags which in turn can affect the specificity and sensitivity SERO of antibodies SERO.

    Predicting the COVID-19 MESHD Patients’ Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree

    Authors: Atefeh Talebi; Nasrin Borumandnia; Ramezan Jafari; Mohamad Amin Pourhoseingholi; Nematollah Jonaidi Jafari; Sara Ashtari; Saeid Roozpeykar; Farshid R. Bashar; Amir Vahedian-Azimi

    doi:10.21203/ Date: 2020-08-09 Source: ResearchSquare

    Background: The role of chest computed tomographic (CT) to diagnosis coronavirus disease-2019 ( COVID-19 MESHD) is still an open field to be explored. The aim of this study is to use non-contrast chest computed tomography (CT) scan as a helpful tool in diagnosis quantification and follow-up of patients with COVID-19 MESHD. Method: This study was performed on patients with COVID-19 MESHD who underwent chest CT scan at Baqiyatallah Hospital, Tehran, Iran. The age TRANS, gender TRANS, types of lesions, other specific signs of high-resolution computed tomography (HRCT), presence of diffuse opacity, underlying diseases, number of involved lobe and total opacity score of 1078 patients were evaluated. Decision tree (DT) model was used to analyze and establish a risk assessment model of critical and non-critical situation. Results: The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to DT model, total opacity score, age TRANS, lesion types and gender TRANS were statistically significant predictors in critical patients. Moreover, the results showed that the accuracy, sensitivity SERO and specificity of the DT model were 93.3%, 72.8% and 97.1%, respectively.Conclusions: The presented algorithm demonstrates the factors affecting the patient's condition. Also the model can predict the critical or non-critical situation of new cases. In addition, this model has the potential characteristics for clinical applications and can also identify high-risk subpopulations that need specific prevention.

    SARS-CoV-2 Seroprevalence SERO Studies: A Rapid Review

    Authors: Kathryn Chess; Tyler A Jacobson; Lauren E Smith; Corinne Miller; Lisa R Hirschhorn; Mark D Huffman

    doi:10.21203/ Date: 2020-08-08 Source: ResearchSquare

    Background: During the COVID-19 MESHD pandemic, SARS-CoV-2 serology tests have been used to understand the extent to which populations have been infected. The objective of this study was to synthesize literature on SARS-CoV-2 seroprevalence SERO studies, including sampling frames, study characteristics, assay test performance SERO characteristics, and proportion of participants with IgG and IgM SARS-CoV-2 antibodies SERO.Methods: Bibliometric databases, trial registers, pre-print servers, and grey literature were searched through May 27, 2020 using a published protocol to identify eligible studies. Title and abstract screening and full-text reviewing were performed in duplicate. Study-level data were extracted, and a narrative synthesis was performed. Results: Of the 4,049 studies screened for inclusion, 27 published reports were included, and 85 studies are ongoing. Most (52%) published reports were available through pre-print servers. Sample sizes ranged from 200 to 113,033 participants. Healthcare worker (n=9 studies, 33%) and non-representative, general population (n=10 studies, 37%) sampling frames were more commonly used than representative, general population sampling frames (n=7, 26%). Mean age TRANS ranged from 18 up to 69 years, and the proportion of females TRANS ranged from 25% to 85%. Test performance SERO characteristics varied, including IgG sensitivity SERO (range: 63.3% to 100%) and IgG specificity (range: 97.0% to 100%). IgG seroprevalence SERO estimates ranged from 0.5% to 21.0%, and IgM seroprevalence SERO ranged from 1.1% to 18.9%.Conclusion: More high-quality SARS-CoV-2 seroprevalence SERO studies using validated assays with larger sample sizes from representative and targeted sampling frames are needed to better understand the true burden of disease, differential spread of the virus, and infection fatality rate. 

    Deep Learning Driven Automated Detection of COVID-19 MESHD from Radiography Images: A Comparative Analysis

    Authors: Sejuti Rahman; Sujan Sarker; Abdullah Al Miraj; Ragib Amin Nihal; A. K. M. Nadimul Haque; Abdullah Al Noman

    id:10.20944/preprints202008.0215.v1 Date: 2020-08-08 Source:

    The ravage of COVID-19 MESHD is not merely limited to taking its toll with half a million fatalities. It has halted the world economy, disrupting normalcy of lives with supervening severity than any other global catastrophe of the last few decades. The majority of the vaccine discovery attempts are still on trial, making early detection and containment the only feasible redress. The existing diagnostic technique with high accuracy has the setbacks of being expensive and sophisticated, requiring skilled individuals for specimen collection and screening resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captures the researchers' interest. This survey marks a detailed inspection of the deep-learning-based automated detection of COVID-19 MESHD works done to date, methodical challenges along with probable solutions, and scopes of future exploration in this arena. We also provided a comparative quantitative analysis of the performance SERO of 315 deep models in diagnosing COVID-19 MESHD, Normal, and Pneumonia HP from x-ray images. Our results show that Densenet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16\%, sensitivity SERO: 98.93\%, specificity: 98.77\%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms MESHD for detecting COVID-19 MESHD. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

    Statistical Analytics and Regional Representation Learning MESHD for COVID-19 MESHD Pandemic Understanding

    Authors: Shayan Fazeli; Babak Moatamed; Majid Sarrafzadeh

    id:2008.07342v1 Date: 2020-08-08 Source: arXiv

    The rapid spread of the novel coronavirus ( COVID-19 MESHD) has severely impacted almost all countries around the world. It not only has caused a tremendous burden on health-care providers to bear, but it has also brought severe impacts on the economy and social life. The presence of reliable data and the results of in-depth statistical analyses provide researchers and policymakers with invaluable information to understand this pandemic and its growth pattern more clearly. This paper combines and processes an extensive collection of publicly available datasets to provide a unified information source for representing geographical regions with regards to their pandemic-related behavior. The features are grouped into various categories to account for their impact based on the higher-level concepts associated with them. This work uses several correlation analysis techniques to observe value and order relationships between features, feature groups, and COVID-19 MESHD occurrences. Dimensionality reduction techniques and projection methodologies are used to elaborate on individual and group importance of these representative features. A specific RNN-based inference pipeline called DoubleWindowLSTM-CP is proposed in this work for predictive event modeling. It utilizes sequential patterns and enables concise record representation while using but a minimal amount of historical data. The quantitative results of our statistical analytics indicated critical patterns reflecting on many of the expected collective behavior and their associated outcomes. Predictive modeling with DoubleWindowLSTM-CP instance exhibits efficient performance SERO in quantitative and qualitative assessments while reducing the need for extended and reliable historical information on the pandemic.

    Serum SERO interleukin-6 is an indicator for severity in 901 patients with SARS-CoV-2 infection MESHD: A cohort study

    Authors: Jing Zhang; Yiqun Hao; Wuling Ou; Fei Ming; Gai Liang; Yu Qian; Qian Cai; Shuang Dong; Sheng Hu; Weida Wang; Shaozhong Wei

    doi:10.21203/ Date: 2020-08-08 Source: ResearchSquare

    Background Interleukin-6 (IL-6) was proposed to be associated with the severity of coronavirus disease 2019 MESHD ( COVID-19 MESHD). The present study aimed to explore the kinetics of IL-6 levels, validate this association in COVID-19 MESHD patients, and report preliminary data on the efficacy of IL-6 receptor blockade.Methods We conducted a retrospective single-institutional study of 901 consecutive confirmed cases TRANS. Serum SERO IL-6 concentrations were tested on admission and/or during hospital stay. Tocilizumab was given to 16 patients with elevated IL-6 concentration.Results 366 patients were defined as common cases, 411 patients as severe, and 124 patients as critical according to the Chinese guideline on diagnosis and treatment of COVID-19 MESHD. The median concentration of IL-6 was < 1.5 pg/ml (IQR < 1.50–2.15), 1.85 pg/ml (IQR < 1.50–5.21), and 21.55 pg/ml (IQR 6.47–94.66) for the common, severe, and critical groups respectively (P༜0.001). The follow-up kinetics revealed serum SERO IL-6 remained high in critical patients even when cured. An IL-6 concentration higher than 37.65 pg/ml was predictive of in-hospital death (AUC 0.97 [95%CI 0.95–0.99], P < 0.001) with a sensitivity SERO of 91.7% and a specificity of 95.7%. In the 16 patients who received tocilizumab, IL-6 concentrations were significantly increased after administration, and survival outcome was not significantly different from that of propensity-score matched counterparts (n = 53, P = 0.12).Conclusion Serum SERO IL-6 should be included in diagnostic work-up to stratify disease severity, but the benefit of tocilizumab needs further confirmation.Trial registration: retrospectively registered.

    Feasibility of using alternative swabs and storage solutions for paired SARS-CoV-2 detection and microbiome analysis in the hospital environment

    Authors: Jeremiah Minich; Farhana Ali; Clarisse Marotz; Pedro Belda-Ferre; Leslie Chiang; Justin P. Shaffer; Carolina S. Carpenter; Daniel McDonald; Jack Gilbert; Sarah M. Allard; Eric E Allen; Rob Knight; Daniel A. Sweeney; Austin D. Swafford

    doi:10.21203/ Date: 2020-08-08 Source: ResearchSquare

    Background: Determining the role of fomites in the transmission TRANS of SARS-CoV-2 is essential in the hospital setting and will likely be important outside of medical facilities as governments around the world make plans to ease COVID-19 MESHD public health restrictions and attempt to safely reopen economies. Expanding COVID-19 MESHD testing to include environmental surfaces would ideally be performed with inexpensive swabs that could be transported safely without concern of being a source of new infections. However, CDC-approved clinical-grade sampling supplies and techniques using a synthetic swab are expensive, potentially expose laboratory workers to viable virus and prohibit analysis of the microbiome due to the presence of antibiotics in viral transport media (VTM). To this end, we performed a series of experiments comparing the diagnostic yield using five consumer-grade swabs (including plastic and wood shafts and various head materials including cotton, synthetic, and foam) and one clinical grade swab for inhibition to RNA. For three of these swabs, we evaluated performance SERO to detect SARS-CoV-2 in twenty intensive care unit (ICU) hospital rooms of patients with 16 COVID-19 MESHD+. All swabs were placed in 95% ethanol and further evaluated in terms of RNase activity. SARS-CoV-2 was measured both directly from the swab and from the swab eluent.Results: Compared to samples collected in VTM, 95% ethanol demonstrated significant inhibition properties against RNases. When extracting directly from the swab head as opposed to the eluent, RNA recovery was approximately 2-4x higher from all six swab types tested as compared to the clinical standard of testing the eluent from a CDC-approved synthetic swab. The limit of detection (LoD) of SARs-CoV-2 from floor samples collected using the CGp or TMI swabs was similar or better than the CDC standard, further suggesting that swab type does not impact RNA recovery as measured by SARs-CoV-2. The LoD for TMI was between 0-362.5 viral particles while SYN and CGp were both between 725–1450 particles. Lastly microbiome analyses (16S rRNA) of paired samples (e.g., environment to host) collected using different swab types in triplicate indicated that microbial communities were not impacted by swab type but instead driven by the patient and sample type (floor or nasal).Conclusions: Compared to using a clinical-grade synthetic swab, detection of SARS-CoV-2 from environmental samples collected from ICU rooms of patients with COVID was similar using consumer grade swabs, stored in 95% ethanol. The yield was best from the swab head rather than the eluent and the low level of RNase activity in these samples makes it possible to perform concomitant microbiome analysis.

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MeSH Disease

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