Corpus overview


Overview

MeSH Disease

Human Phenotype

Transmission

Seroprevalence
    displaying 1 - 10 records in total 31
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    Characteristics and outcomes of patients admitted to Swedish intensive care units for COVID-19 during the first 60 days of the 2020 pandemic: a registry-based, multicenter, observational study.

    Authors: Michelle S Chew; Patrik Blixt; Rasmus Ahman; Lars Engerstrom; Henrik Andersson; Ritva Kiiski Berggren; Anders Tegnell; Sarah McIntyre

    doi:10.1101/2020.08.06.20169599 Date: 2020-08-07 Source: medRxiv

    Background The mortality of patients admitted to the intensive care unit (ICU) with COVID-19 is unclear due to variable censoring and substantial proportions of undischarged patients at follow-up. Nationwide data have not been previously reported. We studied the outcomes of Swedish patients at 30 days after ICU admission. Methods We conducted a registry-based cohort study of all adult TRANS patients admitted to Swedish ICUs from 6 March-6 May, 2020 with laboratory confirmed COVID-19 disease MESHD and complete 30-day follow-up. Data including baseline characteristics, comorbidities, intensive care treatments, organ failures and outcomes were collected. The primary outcome was 30-day all-cause mortality. A multivariable model was used to determine the independent association between potential predictor variables and the primary outcome. Results A total of 1563 patients were identified. Median ICU length of stay was 12 (5-21) days, and fifteen patients remained in ICU at the time of follow-up. Median age TRANS was 61 (52-69), median Simplified Acute Physiology Score III (SAPS III) was 53 (46-59), and 66.8% had at least one comorbidity. Median PaO2/FiO2 on admission was 97.5 (75.0-140.6) mmHg, 74.7% suffered from moderate to severe acute respiratory distress HP syndrome MESHD (ARDS). The 30-day all-cause mortality was 26.7%. The majority of deaths MESHD occurred during ICU admission. Age TRANS, male TRANS sex (adjusted odds ratio [aOR] 1.5 [1.1-2.1]), SAPS III score (aOR 1.3 [1.2-1.4]), severe ARDS (aOR 3.1 [2.0-4.8], specific COVID-19 pharmacotherapy (aOR 1.4 [1.0-1.9]), and CRRT (aOR 2.2 [1.6-3.0]), were associated with increased mortality. With the exception of chronic lung disease HP lung disease MESHD, the presence of comorbidities was not independently associated with mortality. Conclusions Thirty-day mortality rate in COVID-19 patients admitted to Swedish intensive care units is generally lower than previously reported. Mortality appears to be driven by age TRANS, baseline disease MESHD severity, the degree of organ failure and ICU treatment, rather than preexisting comorbidities.

    Does Cystic Fibrosis MESHD Constitute an Advantage in COVID-19 Infection MESHD?

    Authors: Valentino Bezzerri; Francesca Lucca; Sonia Volpi; Marco Cipolli

    doi:10.21203/rs.3.rs-53181/v1 Date: 2020-08-04 Source: ResearchSquare

    The Veneto region is one of the most affected Italian regions by COVID-19. Chronic lung diseases HP lung diseases MESHD, such as chronic obstructive pulmonary disease MESHD chronic obstructive pulmonary disease HP (COPD), may constitute a risk factor in COVID-19. Moreover, respiratory viruses were generally associated with severe pulmonary impairment in cystic fibrosis MESHD (CF). We would have therefore expected numerous cases of severe COVID-19 among the CF population. Surprisingly, we found that CF patients were significantly protected against infection MESHD by SARS-CoV-2. We discussed this aspect formulating some reasonable theories.

    Integrative Analysis for COVID-19 Patient Outcome Prediction

    Authors: Hanqing Chao; Xi Fang; Jiajin Zhang; Fatemeh Homayounieh; Chiara D. Arru; Subba R. Digumarthy; Rosa Babaei; Hadi K. Mobin; Iman Mohseni; Luca Saba; Alessandro Carriero; Zeno Falaschi; Alessio Pasche; Ge Wang; Mannudeep K. Kalra; Pingkun Yan

    id:2007.10416v1 Date: 2020-07-20 Source: arXiv

    While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia MESHD pneumonia HP, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression MESHD with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia MESHD pneumonia HP. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance SERO of prediction to achieve AUC up to 0.884 and sensitivity SERO as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases MESHD including but not limited to community acquired pneumonia MESHD pneumonia HP.

    New SARS-CoV-2 Infection MESHD in a Pet Cat with Severe Lung Disease MESHD in Italy

    Authors: Nicolò Musso; Angelita Costantino; Sebastiano La Spina; Alessandra Finocchiaro; Francesca Andronico; Stefano Stracquadanio; Luigi Liotta; Rosanna Visalli; Giovanni Emmanuele

    id:10.20944/preprints202007.0398.v1 Date: 2020-07-17 Source: Preprints.org

    The pandemic respiratory disease MESHD COVID-19, caused by severe acute respiratory syndrome MESHD coronavirus 2 (SARS-CoV-2), emerged in Wuhan in December 2019 and then spread throughout the world; Italy was the most affected European country. Despite the close pet-human contact, little is known about the predisposition of pets to SARS-CoV-2. Among these, felines are the most susceptible. In this study, a domestic cat with clear symptoms of pneumonia MESHD pneumonia HP, confirmed by Rx imaging, was found to be infected by SARS-CoV-2 using quantitative RT–qPCR from a nasal swab. This is the first Italian study reporting on the request of the scientific community to focus attention on the possible role of pets as a SARS-CoV-2 reservoir. An important question remains unanswered: did the cat die from SARS-CoV-2 infection MESHD?

    COVID-19 Classification in X-ray Chest images using a New Convolutional Neural Network: CNN-COVID

    Authors: Pedro Moises de Sousa; Marianne Modesto; Gabrielle Macedo Pereira; Carlos Alberto da Costa Junior; Luis Vinicius de Moura; Christian Mattjie; Pedro Cunha Carneiro; Ana Maria Marques da Silva; Ana Claudia Patrocinio

    doi:10.21203/rs.3.rs-40913/v1 Date: 2020-07-09 Source: ResearchSquare

    PurposeAs COVID-19 causes lung inflamation and lesions, several scientists have worked on seeking a computer model able to identify medical images of patients with this disease MESHD and improve triage. Chest x-ray and computed tomography images are remarkably like images from patients with other lung diseases MESHD, which makes it hard to diagnose, and that is why there was an urge to seek a computer model. Thus, this paper proposes a computer model able to classify x-ray images of patients as with the new coronavirus. Chest x-ray exams were chosen for this study over computed tomography scans because they are low cost, results are obtained quickly, and x-ray equipment availability is higher in regions impacted with the disease MESHD.MethodsA new CNN network, CNN-COVID, was developed to classify chest images as with and without COVID. This article collected images of patients with and without COVID-19 from Covid-19 image data collection and ChestXray14 banks. To assess the network’s accuracy, 10 training sessions and tests were done using CNN-COVID. A confusion MESHD confusion HP matrix was generated to assess the performance SERO of the model and calculate the following metrics: sensitivity SERO (Se), specificity (Sp), and F1 score. Besides, the Receiver Operating Characteristic (ROC) curves and the Areas Under the Curve (AUCs) were used for assessment.ResultsThe following values were obtained: AUC = 0.9720, Se = 98%, and Sp = 96% for the validation set. A total of 10 tests were executed and the average was 0.9787, the lowest result being 0.9740 and the highest, 0.9870.ConclusionsThe results proved that the CNN-COVID model is a promising tool to help physicians classify chest images with pneumonia MESHD pneumonia HP, considering pneumonia MESHD pneumonia HP caused by COVID-19 and pneumonia MESHD pneumonia HP due to other causes.

    Identification of AAV serotypes for lung gene therapy in human embryonic stem cell derived lung organoids.

    Authors: Helena Meyer-Berg; Lucia Zhou Yang; María Pilar de Lucas; Alberto Zambrano; Stephen C. Hyde; Deborah R. Gill

    doi:10.21203/rs.3.rs-40720/v1 Date: 2020-07-08 Source: ResearchSquare

    Gene therapy is being investigated for a range of serious lung diseases MESHD, such as cystic fibrosis MESHD and emphysema MESHD emphysema HP. Recombinant adeno-associated virus (rAAV) is a well-established, safe, viral-vector for gene delivery with multiple natural and artificial serotypes available displaying alternate cell, tissue and species-specific tropisms. Efficient AAV serotypes for the transduction of the conducting airways have been identified for several species; however, efficient serotypes for human lung parenchyma have not yet been identified. Here, we screened the ability of multiple AAV serotypes to transduce lung bud organoids (LBOs) - a model of human lung parenchyma generated from human embryonic stem cells. Microinjection of LBOs allowed us to model transduction from the luminal surface, similar to dosing via vector inhalation. We identified the natural rAAV2 and rAAV6 serotypes, along with synthetic rAAV6 variants, as having tropism for the human lung parenchyma. Positive staining of LBOs for surfactant proteins B and C confirmed distal lung identity and suggested the suitability of these vectors for the transduction of alveolar type II cells. Our findings establish LBOs as a new model for pulmonary gene therapy and stress the relevance of LBOs as a viral infection MESHD model of the lung parenchyma as relevant in SARS-CoV-2 research.

    Substance Use Disorder in the COVID-19 Pandemic: A Systematic Review of Vulnerabilities and Complications

    Authors: Yufeng Wei; Rameen Shah

    id:10.20944/preprints202007.0061.v1 Date: 2020-07-05 Source: Preprints.org

    As the world endures the coronavirus disease MESHD 2019 (COVID-19) pandemic, conditions of 35 million vulnerable individuals struggling with substance use disorders (SUDs) worldwide have not received sufficient attention for their special health and medical needs. Many of these individuals are complicated by underlying health conditions, such as cardiovascular and lung diseases MESHD and undermined immune systems. During the pandemic, access to the healthcare systems and support groups is greatly diminished. Current research on COVID-19 has not addressed the unique challenges facing individuals with SUDs, including the heightened vulnerability and susceptibility to the disease MESHD. In this systematic review, we will discuss the pathogenesis and pathology of COVID-19, and highlight potential risk factors and complications to these individuals. We will also provide insights and considerations for COVID-19 treatment and prevention in patients with SUDs.

    Segmentation and Evaluation of COVID-19 Lesion from CT scan Slices - A Study with Kapur/Otsu Function and Cuckoo Search Algorithm

    Authors: Suresh Chandra Satapathy; D. Jude Hemanth; Seifedine Kadry; Gunasekaran Manogaran; Naeem M S Hannon; V. Rajinikanth

    doi:10.21203/rs.3.rs-40148/v1 Date: 2020-07-04 Source: ResearchSquare

    Infection MESHD/ disease in lung MESHD is one of the acute illnesses in humans. Pneumonia MESHD Pneumonia HP is one of the major lung diseases MESHD and each year; the death MESHD rate due to the untreated pneumonia MESHD pneumonia HP is on rise globally. From December 2019; the pneumonia MESHD pneumonia HP caused by the Coronavirus Disease MESHD (COVID-19) has emerged as a global threat due to its rapidity. The clinical level assessment of the COVID-19 is normally performed with the Computed-Tomography scan Slice (CTS) or the Chest X-ray. This research aims to propose an image processing system to examine the COVID-19 infection MESHD in CTS. This work implements Cuckoo-Search-Algorithm (CSA) monitored Kapur/Otsu image thresholding and a chosen image segmentation procedure to extract the pneumonia MESHD pneumonia HP infection MESHD. After extracting the COVID-19 infection MESHD from the CTS, a relative assessment is then executed with the Ground-Truth-Image (GTI) offered by a radiologist and the essential performance SERO measures are then computed to confirm the superiority of the proposed technique. This work also presents a comparative assessment among the segmentation procedures, such as Level-Set (LS) and Chan-Vese (CV) methods. The experimental outcome authenticates that, the results by Kapur and Otsu threshold are approximately similar when the LS is implemented and the CV with the Otsu presents better values of Jaccard, Dice and Accuracy compared to other methods presented in this research.

    Massage Therapy for Pulmonary Function in Patients Recovering From Covid-19: A Protocol for Systematic Review and Meta-analysis

    Authors: Kelin Zhou; Shuo Dong; Guobing Fu; Shusheng Cui; Sheng Guo

    doi:10.21203/rs.3.rs-38936/v1 Date: 2020-06-29 Source: ResearchSquare

    Background:Starting in December 2019 in Wuhan (Hubei province, China), a novel coronavirus, designated SARS-CoV-2, has caused an international outbreak of a respiratory illness and rapidly evolved into a pandemic.Given the rapidly growing pandemic and the overwhelmedmedical system, the number of self‐quarantined and recovering patients is increasing.There is an urgentneed of alternative medicine to help patients relieve symptoms duringself‐quarantine, and possibly to help increase their chances of survivaland recovery from COVID-19.Massage (tuina) therapy is one of the widely employed complementary and alternative medicine interventions in the world.Long-term clinicalpractices and experiences have shown that massage therapy could significantly contribute to the healing of most respiratory conditions and lung disease MESHD.This systematic review and meta-analysis will summarize the current evidence of tuina (massage) used as an intervention for pulmonary function in COVID-19 recovering patients.Methods:We will search the following electronic databases for randomized controlled trials to evaluate the effectiveness and safety of massage therapy inimproving pulmonary function ofCOVID-19 recovering patients: Wanfang and Pubmed Database, CNKI, CENTRAL, CINAHL, EMBASE and MEDLINE. Each database will be searched from inception to June 2020. The entire process will include study selection, data extraction, risk of bias assessment and meta-analyses.Discussion:This proposed systematic review will evaluate the existing evidence and explore the potential roleof massage therapyon the effectiveness and safety in pulmonary function of COVID-19 recovering patients.The outcomes will include the improvement of pulmonary function and adverse effect.PROSPERO registration number:CRD42020192107

    Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study

    Authors: Chansik An; Hyunsun Lim; Dong-Wook Kim; Jung Hyun Chang; Yoon Jung Choi; Seong Woo Kim

    doi:10.21203/rs.3.rs-36458/v1 Date: 2020-06-19 Source: ResearchSquare

    The rapid spread of COVID-19 is likely to result in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7,772 (75.9%) recovered, and 2,237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age TRANS > 70, male TRANS sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus MESHD diabetes mellitus HP (DM), chronic lung disease HP lung disease MESHD, or asthma MESHD asthma HP were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities SERO (90.3% [95% confidence interval: 83.3, 97.3]and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities >90%. The most significant predictors for LASSO included old age TRANS and preexisting DM or cancer; for RF they were old age TRANS, infection MESHD route (at large clusters or from personal contact with an infected individual), and underlying hypertension MESHD hypertension HP. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources have to be wisely allocated without hesitation.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from Preprints.org and is updated on a daily basis (7am CET/CEST).

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MeSH Disease
Human Phenotype
Transmission
Seroprevalence


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