Corpus overview


MeSH Disease

Human Phenotype

Pneumonia (198)

Hypertension (127)

Fever (118)

Cough (100)

Respiratory distress (83)


    displaying 41 - 50 records in total 2294
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    Human Pluripotent Stem Cell-Derived Neural Cells and Brain Organoids Reveal SARS-CoV-2 Neurotropism

    Authors: Fadi Jacob; Sarshan Pather; Wei-Kai Huang; Samuel Zheng Hao Wong; Haowen Zhou; Feng Zhang; Beatrice Cubitt; Catherine Z Chen; Miao Xu; Manisha Pradhan; Daniel Y Zhang; Wei Zheng; Anne G Bang; Hongjun Song; Juan Carlos de la Torre; Guo-li Ming

    doi:10.1101/2020.07.28.225151 Date: 2020-07-28 Source: bioRxiv

    Neurological complications are common in patients with COVID-19. While SARS-CoV-2, the causal pathogen of COVID-19, has been detected in some patient brains, its ability to infect brain cells and impact their function are not well understood, and experimental models using human brain cells are urgently needed. Here we investigated the susceptibility of human induced pluripotent stem cell (hiPSC)-derived monolayer brain cells and region-specific brain organoids to SARS-CoV-2 infection MESHD. We found modest numbers of infected neurons and astrocytes, but greater infection MESHD of choroid plexus epithelial cells. We optimized a protocol to generate choroid plexus organoids from hiPSCs, which revealed productive SARS-CoV-2 infection MESHD that leads to increased cell death MESHD and transcriptional dysregulation indicative of an inflammatory response and cellular function deficits. Together, our results provide evidence for SARS-CoV-2 neurotropism and support use of hiPSC-derived brain organoids as a platform to investigate the cellular susceptibility, disease MESHD mechanisms, and treatment strategies for SARS-CoV-2 infection MESHD.

    Modeling Control, Lockdown & Exit Strategies for COVID-19 Pandemic in India

    Authors: Madhab Barman; Snigdhashree Nayak; Manoj Kumar Yadav; Soumyendu Raha; Nachiketa Mishra

    doi:10.1101/2020.07.25.20161992 Date: 2020-07-28 Source: medRxiv

    COVID-19--a viral infectious disease MESHD--has quickly emerged as a global pandemic infecting millions of people with a significant number of deaths MESHD across the globe. The symptoms of this disease MESHD vary widely. Depending on the symptoms an infected person is broadly classified into two categories namely, asymptomatic TRANS and symptomatic. Asymptomatic TRANS individuals display mild or no symptoms but continue to transmit the infection MESHD to otherwise healthy individuals. This particular aspect of asymptomatic infection MESHD asymptomatic TRANS poses a major obstacle in managing and controlling the transmission TRANS of the infectious disease MESHD. In this paper, we attempt to mathematically model the spread of COVID-19 in India under various intervention strategies. We consider SEIR type epidemiological models, incorporated with India specific social contact matrix representing contact structures among different age groups TRANS of the population. Impact of various factors such as presence of asymptotic TRANS individuals, lockdown strategies, social distancing practices, quarantine, and hospitalization on the disease MESHD transmission TRANS is extensively studied. Numerical simulation of our model is matched with the real COVID-19 data of India till May 15, 2020, for the purpose of estimating the model parameters. Our model with zone-wise lockdown is seen to give a decent prediction for July 20, 2020.

    Modelling the SARS-CoVid-2 outbreak in Italy: development of a robust statistical index to track disease MESHD dynamics.

    Authors: Mariano Bizzarri; Mario Di Traglia; Alessandro Giuliani; Annarita Vestri; Valeria Fedeli; Alberto Prestininzi

    doi:10.21203/ Date: 2020-07-28 Source: ResearchSquare

    COVID-19 pandemic in Italy had a spatial distribution that made the tracking of its time course quite difficult. The most relevant anomaly was the marked spatial heterogeneity of pandemics. Lombardia region accounted for around 60% of fatal cases (while hosting 15% of Italian population); moreover, 86% of fatalities concentrated in four Northern Italy regions. The ‘explosive’ outbreak of COVID-19 in Lombardia at the very beginning of pandemic fatally biased the R-like statistics routinely used to control the disease MESHD dynamics. To (at least partially) overcome this bias, we propose a new index RI= dH/dI (daily derivative ratio of H and I, given H=Healed and I=Infected), corresponding to the ratio between healed and infected patients relative daily changes. The proposed index is less affected than R by the uncertainty related to the estimated number of infected persons and allows to follow (and possibly forecast) epidemic dynamics in a largely model-independent way. To analyze the dynamics of the epidemic, starting from the beginning of the virus spreading - when data are insufficient to make an estimate by adopting a SIR model - a "sigmoidal family" model was introduced. That approach allowed in estimating the epidemic peak using the few data gathered even before mid-March. Based on this analysis, the peak had been expected to occur by end of April. Later on, real data of the epidemic evolution have demonstrated to fit with the predicted values.The methodology of analysis of the dynamics of the epidemic we are proposing herein aims to forecast the time and intensity of the epidemic peak (forward prediction), while allowing identifying the (more likely) beginning of the epidemic (backward prediction). Finally, we established a relationship between hospitalization in intensive care units (ICU) versus deaths MESHD daily rates by avoiding the necessity to rely on precise estimates of the infected fraction of the population (the most difficult, uncertain and expensive data that can confidently be acquired, especially in presence of an elevated number of asymptomatic TRANS patients). The joint evolution of the above parameters over time allows for a trustworthy and unbiased estimation of the dynamics of the epidemics.

    Longitudinal COVID-19 Surveillance and Characterization in the Workplace with Public Health and Diagnostic Endpoints

    Authors: Manjula Gunawardana; Jessica Breslin; John M Cortez; Sofia Rivera; Simon Webster; F Javier Ibarrondo; Otto O Yang; Richard B Pyles; Christina M Ramirez; Amy P Adler; Peter A Anton; Marc M Baum

    doi:10.1101/2020.07.25.20160812 Date: 2020-07-28 Source: medRxiv

    Background The rapid spread of severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2) and the associated coronavirus disease MESHD 2019 (COVID-19) have precipitated a global pandemic heavily challenging our social behavior, economy, and healthcare infrastructure. Public health practices currently represent the primary interventions for managing the spread of the pandemic. We hypothesized that frequent, longitudinal workplace disease MESHD surveillance would represent an effective approach to controlling SARS-CoV-2 transmission TRANS among employees and their household members, reducing potential economic consequences and loss of productivity of standard isolation methods, while providing new insights into viral-host dynamics. Methodology and Findings On March 23, 2020 a clinical study (OCIS-05) was initiated at a small Southern California organization. Results from the first 3 months of the ongoing study are presented here. Study participants (27 employees and 27 household members) consented to provide frequent nasal or oral swab samples that were analyzed by RT-qPCR for SARS-CoV-2 RNA using CDC protocols. Only participants testing negative were allowed to enter the "safe zone" workplace facility. Optional blood SERO samples were collected at baseline and throughout the 3-month study. Serum SERO virus-specific antibody SERO concentrations (IgG, IgM, and IgA) were measured using a selective, sensitive, and quantitative ELISA assay SERO developed in house. A COVID-19 infection MESHD model, based on traditional SEIR compartmental models combined with Bayesian non-linear mixed models and modern machine learning, was used to predict the number of employees and household members who would have become infected in the absence of workplace surveillance. Two study participants were found to be infected by SARS-CoV-2 during the study. One subject, a household member, tested positive clinically by RT-qPCR prior to enrollment and experienced typical COVID-19 symptoms that did not require hospitalization. While on study, the participant was SARS-CoV-2 RNA positive for at least 71 days and had elevated virus-specific antibody SERO concentrations (medians: IgM, 9.83 ug mL-1; IgG, 11.5 ug mL-1; IgA, 1.29 ug mL-1) in serum samples SERO collected at three timepoints. A single, unrelated employee became positive for SARS-CoV-2 RNA over the course of the study, but remained asymptomatic TRANS with low associated viral RNA copy numbers. The participant did not have detectable serum SERO IgM and IgG concentrations, and IgA concentrations decayed rapidly (half-life: 1.3 d). The employee was not allowed entry to the safe zone workplace until testing negative three consecutive times over 7 d. No other employees or household members contracted COVID-19 over the course of the study. Our model predicted that under the current prevalence SERO in Los Angeles County without surveillance intervention, up to 7 employees (95% CI = 3-10) would have become infected with at most 1 of them requiring hospitalizations and 0 deaths MESHD. Conclusions Our clinical study met its primary objectives by using intense longitudinal testing to provide a safe work environment during the COVID-19 pandemic, and elucidating SARS-CoV-2 dynamics in recovering and asymptomatic TRANS participants. The surveillance plan outlined here is scalable and transferrable. The study represents a powerful example on how an innovative public health initiative can be dovetailed with scientific discovery.

    New Insights on Excess Deaths MESHD and COVID-19

    Authors: Harry P Wetzler; Herbert W Cobb

    doi:10.1101/2020.07.24.20051508 Date: 2020-07-27 Source: medRxiv

    Background: Weinberger and colleagues estimated that 27,065 of the 122,300 excess deaths MESHD in the United States between March 1 and May 30, 2020 did not have a COVID-19 cause of death MESHD. Methods: The Centers for Disease MESHD Control and Prevention (CDC) post weekly data on mortality for 13 causes of death MESHD from the most prevalent comorbid conditions reported on death MESHD certificates where COVID-19 was listed as a cause of death MESHD. The 2015-2019 data for weeks 10 through 22 were used to forecast the number of deaths MESHD from the 13 causes in the absence of COVID-19 during 2020. The forecast was subtracted from the observed number of deaths MESHD for each cause during the period March 1 to May 30, 2020. Results: The total of the differences for each of the 13 causes of death MESHD, 18,489 deaths MESHD, accounts for over two-thirds of the 27,065 excess deaths MESHD not due to COVID-19. Conclusion: Combining the 95,235 reported COVID-19 deaths MESHD with the 18,489 from the 13 most frequent comorbid conditions reported on death MESHD certificates where COVID-19 was a cause suggests that as many as 93% of the excess deaths MESHD were due to COVID-19 and implies that COVID-19 deaths MESHD were undercounted. Ongoing assessment of excess deaths MESHD and causes of death MESHD is needed to provide a better understanding of the pandemics dynamics.

    Trends of in-hospital and 30-day mortality after percutaneous coronary intervention in England before and after the COVID-19 era

    Authors: Mohamed O Mohamed; Tim Kinnaird; Nick Curzen; Peter Ludman; Jianhua Wu; Muhammad Rashid; Ahmad Shoaib; Mark de Belder; John Deanfield; Chris Gale; Mamas A Mamas

    doi:10.1101/2020.07.18.20155549 Date: 2020-07-27 Source: medRxiv

    Objectives: To examine short-term primary causes of death MESHD after percutaneous coronary intervention (PCI) in a national cohort before and during COVID-19. Background: Public reporting of PCI outcomes is a performance SERO metric and a requirement in many healthcare systems. There are inconsistent data on the causes of death MESHD after PCI, and what proportion of these are attributable to cardiac causes. Methods: All patients undergoing PCI in England between 1st January 2017 and 10th May 2020 were retrospectively analysed (n=273,141), according to their outcome from the date of PCI; no death MESHD and in-hospital, post-discharge, and 30-day death MESHD. Results: The overall rates of in-hospital and 30-day death MESHD were 1.9% and 2.8%, respectively. The rate of 30-day death MESHD declined between 2017 (2.9%) and February 2020 (2.5%), mainly due to lower in-hospital death MESHD (2.1% vs. 1.5%), before rising again from 1st March 2020 (3.2%) due to higher rates of post-discharge mortality. Only 59.6% of 30-day deaths MESHD were due to cardiac causes, the most common being acute coronary syndrome MESHD, cardiogenic shock MESHD cardiogenic shock HP and heart failure MESHD, and this persisted throughout the study period. 10.4% of 30-day deaths MESHD after 1st March 2020 were due to confirmed COVID-19. Conclusions: In this nationwide study, we show that 40% of 30-day deaths MESHD are due to non-cardiac causes. Non-cardiac deaths MESHD have increased even more from the start of the COVID-19 pandemic, with one in ten deaths MESHD from March 2020 being COVID-19 related. These findings raise a question of whether public reporting of PCI outcomes should be cause-specific.

    Patient characteristics and predictors of mortality in 470 adults TRANS admitted to a district general hospital in England with Covid-19

    Authors: Joseph V Thompson; Nevan Meghani; Bethan M Powell; Ian Newell; Roanna Craven; Gemma Skilton; Lydia J Bagg; Irha Yaqoob; Michael J Dixon; Eleanor J Evans; Belina Kambele; Asif Rehman; Georges Ng Man Kwong

    doi:10.1101/2020.07.21.20153650 Date: 2020-07-27 Source: medRxiv

    Background Understanding risk factors for death MESHD in Covid 19 is key to providing good quality clinical care. Due to a paucity of robust evidence, we sought to assess the presenting characteristics of patients with Covid 19 and investigate factors associated with death MESHD. Methods Retrospective analysis of adults TRANS admitted with Covid 19 to Royal Oldham Hospital, UK. Logistic regression modelling was utilised to explore factors predicting death MESHD. Results 470 patients were admitted, of whom 169 (36%) died. The median age TRANS was 71 years (IQR 57 to 82), and 255 (54.3%) were men. The most common comorbidities were hypertension MESHD hypertension HP (n=218, 46.4%), diabetes (n=143, 30.4%) and chronic neurological disease MESHD (n=123, 26.1%). The most frequent complications were acute kidney injury MESHD acute kidney injury HP (n=157, 33.4%) and myocardial injury (n=21, 4.5%). Forty three (9.1%) patients required intubation and ventilation, and 39 (8.3%) received non-invasive ventilation Independent risk factors for death MESHD were increasing age TRANS (OR per 10 year increase above 40 years 1.87, 95% CI 1.57 to 2.27), hypertension MESHD hypertension HP (OR 1.72, 1.10 to 2.70), cancer (OR 2.20, 1.27 to 3.81), platelets <150x103/microlitre (OR 1.93, 1.13 to 3.30), C-reactive protein >100 micrograms/mL (OR 1.68, 1.05 to 2.68), >50% chest radiograph infiltrates, (OR 2.09, 1.16 to 3.77) and acute kidney injury MESHD acute kidney injury HP (OR 2.60, 1.64 to 4.13). There was no independent association between death MESHD and gender TRANS, ethnicity, deprivation level, fever MESHD fever HP, SpO2/FiO2 (oxygen saturation index), lymphopenia MESHD lymphopenia HP or other comorbidities. Conclusions We characterised the first wave of patients with Covid 19 in one of Englands highest incidence areas, determining which factors predict death MESHD. These findings will inform clinical and shared decision making, including the use of respiratory support and therapeutic agents.

    Covid-19 Pandemic: ARIMA and Regression Model based Worldwide Death MESHD Cases Predictions

    Authors: Vikas Chaurasia; Saurabh Pal

    doi:10.21203/ Date: 2020-07-27 Source: ResearchSquare

    Covid-19 has now taken a frightening form. As the day passes, it is becoming more and more widespread and now it has become an epidemic. The death MESHD rate, which was earlier in the hundreds, changed to thousands and then progressed to millions respectively. If the same situation persists over time, the day is not far when the humanity of all the countries on the globe will be endangered and we yearn for breath. From January 2020 till now, many scientists, researchers and doctors are also trying to solve this complex problem so that proper arrangements can be made by the governments in the hospitals and the death MESHD rate can be reduced. The presented research article shows the estimated mortality rate by the ARIMA model and the Regression model. This dataset has been collected precisely from DataHub-Novel Coronavirus 2019 - Dataset from 22nd January to 29th June 2020. In order to show the current mortality rate of the entire subject, the correlation coefficients of attributes (MAE, MSE, RMSE and MAPE) were used, where the average absolute percentage error validated the model by 99.09%. The ARIMA model is used to generate auto_arima SARIMAX results, auto_arima residual plots, ARIMA model results, and corresponding prediction plots on the training data set. These data indicate a continuous decline in death MESHD cases. By applying a regression model, the coefficients generated by the regression model are estimated, and the actual death MESHD cases and expected death MESHD cases are compared and analyzed. It is found that the predicted mortality rate has decreased after May 2, 2020. It will learn help the government and doctors prepare for the next plans. Based on short- period predictions these methods can use forecast for long- period.

    SEIHCRD Model for COVID-19 spread scenarios, disease MESHD predictions and estimates the basic reproduction number TRANS, case fatality rate, hospital, and ICU beds requirement

    Authors: Avaneesh Singh; Manish Kumar Bajpai

    doi:10.1101/2020.07.24.20161752 Date: 2020-07-27 Source: medRxiv

    We have proposed a new mathematical method, SEIHCRD-Model that is an extension of the SEIR-Model adding hospitalized and critical twocompartments. SEIHCRD model has seven compartments: susceptible (S), exposed (E), infected (I), hospitalized (H), critical (C), recovered (R), and deceased or death MESHD (D), collectively termed SEIHCRD. We have studied COVID- 19 cases of six countries, where the impact of this disease MESHD in the highest are Brazil, India, Italy, Spain, the United Kingdom, and the United States. SEIHCRD model is estimating COVID-19 spread and forecasting under uncertainties, constrained by various observed data in the present manuscript. We have first collected the data for a specific period, then fit the model for death MESHD cases, got the values of some parameters from it, and then estimate the basic reproduction number TRANS over time, which is nearly equal to real data, infection MESHD rate, and recovery rate of COVID-19. We also compute the case fatality rate over time of COVID-19 most affected countries. SEIHCRD model computes two types of Case fatality rate one is CFR daily and the second one is total CFR. We analyze the spread and endpoint of COVID-19 based on these estimates. SEIHCRD model is time-dependent hence we estimate the date and magnitude of peaks of corresponding to the number of exposed cases, infected cases, hospitalized cases, critical cases, and the number of deceased cases of COVID-19 over time. SEIHCRD model has incorporated the social distancing parameter, different age groups TRANS analysis, number of ICU beds, number of hospital beds, and estimation of how much hospital beds and ICU beds are required in near future.

    An effective COVID-19 response in South America: the Uruguayan Conundrum

    Authors: Pilar Moreno; Gonzalo Andres Moratorio; Gregorio Iraola; Alvaro Fajardo; Fabian Aldunate; Marianoel Pereira; Paula Perbolianachis; Alicia Costabile; Fernando Lopez-Tort; Diego Simon; Cecilia Salazar; Ignacio Ferres; Florencia Diaz-Viraque; Andres Abin; Mariana Bresque; Matias Fabregat; Matias Maidana; Bernardina Rivera; Maria Cruces; Jorge Rodriguez; Paola Scavone; Miguel Alegretti; Adriana Nabon; Gustavo Gagliano; Raquel Rosa; Eduardo Henderson; Estela Bidegain; Leticia Zarantonelli; Claudia Piattoni; Gonzalo Greif; Maria Francia; Carlos Robello; Rosario Duran; Gustavo Brito; Victoria Bonnecarrere; Miguel Sierra; Rodney Colina; Monica Marin; Juan Cristina; Ricardo Erlich; Fernando Paganini; Henry Cohen; Rafael Radi; Luis Barbeito; Jose Badano; Otto Pritsch; Cecilia Fernandez; Rodrigo Arim; Carlos Batthyany; - Interinstitutional COVID-19 Working Group

    doi:10.1101/2020.07.24.20161802 Date: 2020-07-27 Source: medRxiv

    Background: South America has become the new epicenter of the COVID-19 pandemic with more than 1.1M reported cases and >50,000 deaths MESHD (June 2020). Conversely, Uruguay stands out as an outlier managing this health crisis with remarkable success. Methods: We developed a molecular diagnostic test to detect SARS-CoV-2. This methodology was transferred to research institutes, public hospitals and academic laboratories all around the country, creating a COVID-19 diagnostic lab network. Uruguay also implemented active epidemiological surveillance following the Test, Trace TRANS and Isolate (TETRIS) strategy coupled to real-time genomic epidemiology. Results: Three months after the first cases were detected, the number of positive individuals reached 826 (23 deaths MESHD, 112 active cases and 691 recovered). The Uruguayan strategy was based in a close synergy established between the national health authorities and the scientific community. In turn, academia rapidly responded to develop national RT-qPCR tests. Consequently, Uruguay was able to perform ~1,000 molecular tests per day in a matter of weeks. The COVID-19 diagnostic lab network performed more than 54% of the molecular tests in the country. This, together with real-time genomics, were instrumental to implement the TETRIS strategy, helping to contain domestic transmission TRANS of the main outbreaks registered so far. Conclusions: Uruguay has successfully navigated the first trimester of the COVID-19 health crisis in South America. A rapid response by the scientific community to increase testing capacity, together with national health authorities seeking out the support from the academia were fundamental to successfully contain, until now, the COVID-19 outbreak in the country.

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MeSH Disease
Human Phenotype

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