Corpus overview


MeSH Disease

Human Phenotype

Pneumonia (231)

Fever (70)

Cough (38)

Hypertension (27)

Falls (24)


    displaying 711 - 720 records in total 1730
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    Development of Machine Learning models to predict RT-PCR results for Severe Acute Respiratory Syndrome Coronavirus 2 MESHD (SARS-CoV-2) in patients with influenza-like symptoms using only basic clinical data.

    Authors: Thomas Langer; Martina Favarato; Riccardo Giudici; Gabriele Bassi; Roberta Garberi; Fabiana Villa; Hedwige Gay; Anna Zeduri; Sara Bragagnolo; Alberto Molteni; Andrea Beretta; Matteo Corradin; Mauro Moreno; Chiara Vismara; Carlo Federico Perno; Massimo Buscema; Enzo Grossi; Roberto Fumagalli

    doi:10.21203/ Date: 2020-06-29 Source: ResearchSquare

    Background: Reverse Transcription-Polymerase Chain Reaction (RT-PCR) for Severe Acute Respiratory Syndrome Coronavirus 2 MESHD ( SARS-COV-2 MESHD) diagnosis currently requires quite a long time span. A quicker and more efficient diagnostic tool in emergency departments could improve management during this global crisis. Our main goal was assessing the accuracy of artificial intelligence in predicting the results of RT-PCR for SARS-COV-2 MESHD, using basic information at hand in all emergency departments.Methods: This is a retrospective study carried out between February 22, 2020 and March 16, 2020 in one of the main hospitals in Milan, Italy. We screened for eligibility all patients admitted with influenza-like symptoms tested for SARS-COV-2. Patients under 12 years old and patients in whom the leukocyte formula was not performed in the ED were excluded. Input data through artificial intelligence were made up of a combination of clinical, radiological and routine laboratory data upon hospital admission. Different Machine Learning algorithms MESHD available on WEKA data mining software and on Semeion Research Centre depository were trained using both the Training and Testing and the K-fold cross-validation protocol.Results: Among 199 patients subject to study (median [interquartile range] age TRANS 65 [46-78] years; 127 [63.8%] men), 124 [62.3%] resulted positive to SARS-COV-2 MESHD. The best Machine Learning System reached an accuracy of 91.4% with 94.1% sensitivity SERO and 88.7% specificity. Conclusion: Our study suggests that properly trained artificial intelligence algorithms may be able to predict correct results in RT-PCR for SARS-COV-2 MESHD, using basic clinical data. If confirmed, on a larger-scale study, this approach could have important clinical and organizational implications.

    A novel optical biosensor for the early diagnosis of sepsis HP sepsis MESHD and severe COVID-19: the PROUD study

    Authors: Sarantia Doulou; Konstantinos Leventogiannis; Maria Tsilika; Matthew Rodencal; Konstantina Katrini; Nikolaos Antonakos; Miltiades Kyprianou; Emmanouil Karofylakis; Athanassios Karageorgos; Panagiotis Koufargyris; Gennaios Christopoulos; George Kassianidis; Kimon Stamatelopoulos; Robert Newberry; Evangelos J. Giamarellos-Bourboulis

    doi:10.21203/ Date: 2020-06-28 Source: ResearchSquare

    Background The accuracy of a new optical biosensor ( OB MESHD) point-of-care device for the detection of severe infections HP is studied.Methods The OB emits different wavelengths and outputs information associated with heart rate, pulse oximetry, levels of nitric oxide and kidney function. At the derivation phase, recordings were done every two hours for three consecutive days after hospital admission in 142 patients at high-risk for sepsis HP sepsis MESHD by placing the OB on the forefinger. At the validation phase, single recordings were done in 54 patients with symptoms of viral infection MESHD; 38 were diagnosed with COVID-19.Results At the derivation phase, the cutoff value of positive likelihood of 18 provided 100% specificity and 100% positive predictive value SERO for the diagnosis of sepsis HP sepsis MESHD. These were 87.5% and 91.7% respectively at the validation phase. OB MESHD diagnosed severe COVID-19 with 83.3% sensitivity SERO and 87.5% negative predictive value SERO.Conclusions The studied OB MESHD seems valuable for the discrimination of infection severity.

    Using Bluetooth Low Energy (BLE) Signal Strength Estimation to Facilitate Contact Tracing TRANS for COVID-19

    Authors: Gary F. Hatke; Monica Montanari; Swaroop Appadwedula; Michael Wentz; John Meklenburg; Louise Ivers; Jennifer Watson; Paul Fiore

    id:2006.15711v2 Date: 2020-06-28 Source: arXiv

    The process of contact tracing TRANS to reduce the spread of highly infectious and life-threatening diseases has traditionally been a primarily manual process managed by public health entities. This process becomes challenged when faced with a pandemic of the proportions of SARS-CoV2. Digital contact tracing TRANS has been proposed as way to augment manual contact tracing TRANS and lends itself to widely proliferated devices such as cell phones and wearables. This paper describes a method and analysis of determining whether two cell phones, carried by humans, were in persistent contact of no more than 6 feet over 15 minutes using Bluetooth Low Energy signals. The paper describes the approach to detecting these signals, as well as a data-driven performance SERO analysis showing that larger numbers of samples coupled with privacy preserving auxiliary information improves detection performance SERO.

    Sex, age TRANS, and hospitalization drive antibody SERO responses in a COVID-19 convalescent plasma SERO donor population

    Authors: Sabra Klein; Andrew Pekosz; Han-Sol Park; Rebecca Ursin; Janna Shapiro; Sarah Benner; Kirsten Littlefield; Swetha Kumar; Harnish Mukesh Naik; Michael Betenbaugh; Ruchee Shrestha; Annie Wu; Robert Hughes; Imani Burgess; Patricio Caturegli; Oliver Laeyendecker; Thomas Quinn; David Sullivan; Shmuel Shoham; Andrew Redd; Evan Bloch; Arturo Casadevall; Aaron Tobian

    doi:10.1101/2020.06.26.20139063 Date: 2020-06-28 Source: medRxiv

    Convalescent plasma SERO is currently one of the leading treatments for COVID-19, but there is a paucity of data identifying therapeutic efficacy. A comprehensive analysis of the antibody SERO responses in potential plasma SERO donors and an understanding of the clinical and demographic factors that drive variant antibody SERO responses is needed. Among 126 potential convalescent plasma SERO donors, the humoral immune response was evaluated by a SARS-CoV-2 virus neutralization assay using Vero-E6-TMPRSS2 cells, commercial IgG and IgA ELISA SERO to Spike (S) protein S1 domain (Euroimmun), IgA, IgG and IgM indirect ELISAs SERO to the full-length S or S-receptor binding domain (S-RBD), and an IgG avidity assay. Multiple linear regression and predictive models were utilized to assess the correlations between antibody SERO responses with demographic and clinical characteristics. IgG titers were greater than either IgM or IgA for S1, full length S, and S-RBD in the overall population. Of the 126 plasma SERO samples, 101 (80%) had detectable neutralizing titers. Using neutralization titer as the reference, the sensitivity SERO of the IgG ELISAs SERO ranged between 95-98%, but specificity was only 20-32%. Male TRANS sex, older age TRANS, and hospitalization with COVID-19 were all consistently associated with increased antibody SERO responses across the serological assays SERO. Neutralizing antibody SERO titers were reduced over time in contrast to overall antibody SERO responses. There was substantial heterogeneity in the antibody SERO response among potential convalescent plasma SERO donors, but sex, age TRANS and hospitalization emerged as factors that can be used to identify individuals with a high likelihood of having strong antiviral antibody SERO levels.

    Predicting the disease outcome in COVID-19 positive patients through Machine Learning: a retrospective cohort study with Brazilian data

    Authors: Fernanda Sumika Hojo Souza; Natália Satchiko Hojo-Souza; Edimilson Batista Santos; Cristiano Maciel Silva; Daniel Ludovico Guidoni

    doi:10.1101/2020.06.26.20140764 Date: 2020-06-28 Source: medRxiv

    The first officially registered case of COVID-19 in Brazil was on February 26, 2020. Since then, the situation has worsened with more than 672,000 confirmed cases TRANS and at least 36,000 reported deaths at the time of this writing. Accurate diagnosis of patients with COVID-19 is extremely important to offer adequate treatment, and avoid overloading the healthcare system. Characteristics of patients such as age TRANS, comorbidities and varied clinical symptoms can help in classifying the level of infection severity, predict the disease outcome and the need for hospitalization. Here, we present a study to predict a poor prognosis in positive COVID-19 patients and possible outcomes using machine learning. The study dataset comprises information of 13,690 patients concerning closed cases due to cure or death MESHD. Our experimental results show the disease outcome can be predicted with a ROC AUC of 0.92, Sensitivity SERO of 0.88 and Specificity of 0.82 for the best prediction model. This is a preliminary retrospective study which can be improved with the inclusion of further data. Conclusion: Machine learning techniques fed with demographic and clinical data along with comorbidities of the patients can assist in the prognostic prediction and physician decision-making, allowing a faster response and contributing to the non-overload of healthcare systems.

    Evaluation on the diagnostic efficiency of different methods in detecting COVID-19.

    Authors: Haitao Yang; Yuzhu Lan; Xiujuan Yao; Sheng Lin; Baosong Xie

    doi:10.1101/2020.06.25.20139931 Date: 2020-06-26 Source: medRxiv

    Objective: To evaluate the diagnostic efficiency of different methods in detecting COVID-19 to provide preliminary evidence on choosing favourable method for COVID-19 detection. Methods: PubMed, Web of Science and Embase databases were searched for identifing eligible articles. All data were calculated utilizing Meta Disc 1.4, Revman 5.3.2 and Stata 12. The diagnostic efficiency was assessed via these indicators including summary sensitivity SERO and specificity, positive likelihood ratio (PLR), negative LR (NLR), diagnostic odds ratio (DOR), summary receiver operating characteristic curve (sROC) and calculate the AUC. Results: 18 articles (3648 cases) were included. The results showed no significant threshold exist. EPlex: pooled sensitivity SERO was 0.94; specificity was 1.0; PLR was 90.91; NLR was 0.07; DOR was 1409.49; AUC=0.9979, Q*=0.9840. Panther Fusion: pooled sensitivity SERO was 0.99; specificity was 0.98; PLR was 42.46; NLR was 0.02; DOR was 2300.38; AUC=0.9970, Q*=0.9799. Simplexa: pooled sensitivity SERO was 1.0; specificity was 0.97; PLR was 26.67; NLR was 0.01; DOR was 3100.93; AUC=0.9970, Q*=0.9800. Cobas: pooled sensitivity SERO was 0.99; specificity was 0.96; PLR was 37.82; NLR was 0.02; DOR was 3754.05; AUC=0.9973, Q*=0.9810. RT-LAMP: pooled sensitivity SERO was 0.98; specificity was 0.99; PLR was 36.22; NLR was 0.04; DOR was 751.24; AUC=0.9905, Q*=0.9596. Xpert Xpress: pooled sensitivity SERO was 0.99; specificity was 0.97; PLR was 27.44; NLR was 0.01; DOR was 3488.15; AUC=0.9977, Q*=0.9829. Conclusions: These methods (ePlex, Panther Fusion, Simplexa, Cobas, RT-LAMP and Xpert Xpress) bear higher sensitivity SERO and specificity, and might be efficient methods complement to the gold standard.

    Resource optimization in COVID-19 diagnosis

    Authors: Sueli Akemi Taniwaki; Sheila O. S. Silva; Nelson Fernando Santana-Clavijo; Juliana A Conselheiro; Gisely T Barone; Adriana A. R. Menezes; Elder S. Pereira; Paulo E. Brandao

    doi:10.1101/2020.06.25.172528 Date: 2020-06-26 Source: bioRxiv

    AbtsractThe emergence and rapid dissemination worldwide of a novel Coronavirus (SARS-CoV-2) results in decrease of swabs availability for clinical samples collection, as well as, reagents for RT-qPCR diagnostic kits considered a confirmatory test for COVID-19 infection MESHD. This scenario, showed the requirement of improve de diagnostic capacity, so the aim of this study were to verify the possibility of reducing the reaction volume of RT-qPCR and to test cotton swabs as alternative for sample collection. RT-qPCR volumes and RNA sample concentration were optimized without affecting the sensitivity SERO of assays, using both probe-based and intercalation dyes methods. Although rayon swabs showed better performance SERO, cotton swabs could be used as alternative type for clinical sample collection. COVID-19 laboratory diagnosis is important to isolate and restrict the dissemination of virus, so seek for alternatives to decrease the coast of assays improve the control of disease.

    Seroprevalence SERO of Antibodies to SARS-CoV-2 SERO in Six Sites in the United States, March 23-May 3, 2020

    Authors: Fiona P. Havers; Carrie Reed; Travis W. Lim; Joel M. Montgomery; John D. Klena; Aron J. Hall; Alicia M. Fry; Deborah L. Cannon; Cheng-Feng Chiang; Aridth Gibbons; Inna Krapiunaya,; Maria Morales-Betoulle; Katherine Roguski; Mohammed Rasheed; Brandi Freeman; Sandra Lester; Lisa Mills; Darin S. Carroll; S. Michelle Owen; Jeffrey A. Johnson; Vera A. Semenova; - State Collaborator Group; Jarad Schiffer; Natalie P. Thornburg

    doi:10.1101/2020.06.25.20140384 Date: 2020-06-26 Source: medRxiv

    Importance: Reported cases of SARS-CoV-2 infection MESHD likely underestimate the prevalence SERO of infection MESHD in affected communities. Large-scale seroprevalence SERO studies provide better estimates of the proportion of the population previously infected. Objective: To estimate prevalence SERO of SARS-CoV-2 antibodies SERO in convenience samples from several geographic sites in the United States. Design: Serologic testing SERO of convenience samples using residual sera obtained for routine clinical testing by two commercial laboratory companies. Setting: Connecticut (CT), south Florida (FL), Missouri (MO), New York City metro region (NYC), Utah (UT), and Washington State's (WA) Puget Sound region. Participants: Persons of all ages TRANS with serum SERO collected during intervals from March 23 through May 3, 2020. Exposure: SARS-CoV-2 virus infection MESHD. Main outcomes and measures: We estimated the presence of antibodies to SARS-CoV-2 SERO spike protein using an ELISA assay SERO. We standardized estimates to the site populations by age TRANS and sex. Estimates were adjusted for test performance SERO characteristics (96.0% sensitivity SERO and 99.3% specificity). We estimated the number of infections MESHD in each site by extrapolating seroprevalence SERO to site populations. We compared estimated infections to number of reported COVID-19 cases as of last specimen collection date. Results: We tested sera from 11,933 persons. Adjusted estimates of the proportion of persons seroreactive to the SARS-CoV-2 spike protein ranged from 1.13% (95% confidence interval [CI] 0.70-1.94) in WA to 6.93% (95% CI 5.02-8.92) in NYC (collected March 23-April 1). For sites with later collection dates, estimates ranged from 1.85% (95% CI 1.00-3.23, collected April 6-10) for FL to 4.94% (95% CI 3.61-6.52) for CT (April 26-May 3). The estimated number of infections MESHD ranged from 6 to 24 times the number of reported cases in each site. Conclusions and relevance: Our seroprevalence SERO estimates suggest that for five of six U.S. sites, from late March to early May 2020, >10 times more SARS-CoV-2 infections MESHD occurred than the number of reported cases. Seroprevalence SERO and under-ascertainment varied by site and specimen collection period. Most specimens from each site had no evidence of antibody to SARS-CoV-2 SERO. Tracking population seroprevalence SERO serially, in a variety of specific geographic sites, will inform models of transmission TRANS dynamics and guide future community-wide public health measures.

    Hypoferremia predicts hospitalization and oxygen demand in COVID-19 patients

    Authors: Theresa Hippchen; Sandro Altamura; Martina U. Muckenthaler; Uta Merle

    doi:10.1101/2020.06.26.20140525 Date: 2020-06-26 Source: medRxiv

    Background: Iron metabolism might play a crucial role in cytokine release syndrome in COVID-19 patients. Therefore we assessed iron metabolism markers in COVID-19 patients for their ability to predict disease severity. Methods: COVID-19 patients referred to the Heidelberg University Hospital were retrospectively analyzed. Patients were divided into outpatients (cohort A, n=204), inpatients (cohort B, n=81), and outpatients later admitted to hospital because of health deterioration (cohort C, n=23). Results: Iron metabolism parameters were severely altered in patients of cohort B and C compared to cohort A. In multivariate regression analysis including age TRANS, gender TRANS, CRP and iron-related parameters only serum SERO iron and ferritin were significantly associated with hospitalization. ROC analysis revealed an AUC for serum SERO iron of 0.894 and an iron concentration <6micromol/l as the best cutoff-point predicting hospitalization with a sensitivity SERO of 94.7% and a specificity of 67.9%. When stratifying inpatients in a low- and high oxygen demand group serum SERO iron levels differed significantly between these two groups and showed a high negative correlation with the inflammatory parameters IL-6, procalcitonin, and CRP. Unexpectedly, serum SERO iron levels poorly correlate with hepcidin. Conclusion: We conclude that measurement of serum SERO iron can help predicting the severity of COVID-19. The differences in serum SERO iron availability observed between the low and high oxygen demand group suggest that disturbed iron metabolism likely plays a causal role in the pathophysiology leading to lung injury MESHD.

    Analytical and Clinical Validation for RT-qPCR detection of SARS-CoV-2 without RNA extraction

    Authors: Jose P. Miranda; Javiera Osorio; Mauricio Videla; Gladys Angel; Rossana Camponovo; Marcela Henriquez-Henriquez

    doi:10.1101/2020.06.24.20134783 Date: 2020-06-26 Source: medRxiv

    Background: The recent COVID-19 pandemic has posed an unprecedented challenge to laboratory diagnosis, based on the amplification of SARS-CoV-2 RNA. With global contagion figures exceeding 4 million persons, the shortage of reagents for RNA extraction represents a bottleneck for testing globally. We present the validation results for a RT-qPCR protocol without prior RNA extraction. Because of its simplicity, this protocol is suitable for widespread application in resource-limited settings. Methods: Optimal protocol was selected by comparing RT-qPCR performance SERO under a set of thermal (65{degrees}C, 70{degrees}C, and 95{degrees}C for 5, 10, and 30 minutes) and amplification conditions (3 or 3,5 uL loading volume; 2 commercial RT-qPCR kits with limit of detection below 10 copies/sample) in nasopharyngeal swabs stored at 4{degrees}C in sterile Weise buffer pH 7.2. The selected protocol was evaluated for classification concordance with the standard protocol (automated RNA extraction) in 130 routine samples and in 50 historical samples with Cq values near to the clinical decision limit. Results: Optimal selected conditions were: Thermal shock HP at 70{degrees}C for 10 minutes, loading 3.5 ul in the RT-qPCR. Prospective evaluation in 130 routine samples showed 100% classification concordance with the standard protocol. The evaluation in historical samples, selected because their Cqs were at the clinical decision limit, showed 94% concordance with our confirmatory-gold standard which includes manual RNA extraction. Conclusions: These results validate the use of this direct RT-qPCR protocol as a safe alternative for SARS CoV-2 diagnosis in case of a shortage of reagents for RNA extraction, with minimal clinical impact.

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MeSH Disease
Human Phenotype

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