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10.1021/acs.analchem.0c04497 http://scihub22266oqcxt.onion/10.1021/acs.analchem.0c04497 33471512!8023531!33471512     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Anal+Chem 2021 ; 93 (4): 2471-2479 Nephropedia Template TP {{tp|p=33471512|t=2021. Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning |pdf=|usr=}}{{33471512}} gab.com Text Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning JO: Anal Chem http://www.kidney.de/pget.php?&tw=1&pmid=33471512 #FOAvip COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application. Twit Text FOAVip Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning ????Anal Chem http://www.kidney.de/pget.php?&tw=1&pmid=33471512 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33471512 #FOAvip English Wikipedia <ref>{{Cite pmid|33471512}}</ref> Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning #MMPMID33471512 Delafiori J; Navarro LC; Siciliano RF; de Melo GC; Busanello ENB; Nicolau JC; Sales GM; de Oliveira AN; Val FFA; de Oliveira DN; Eguti A; Dos Santos LA; Dalcoquio TF; Bertolin AJ; Abreu-Netto RL; Salsoso R; Baia-da-Silva D; Marcondes-Braga FG; Sampaio VS; Judice CC; Costa FTM; Duran N; Perroud MW; Sabino EC; Lacerda MVG; Reis LO; Favaro WJ; Monteiro WM; Rocha AR; Catharino RR Anal Chem 2021[Feb]; 93 (4): 2471-2479 PMID33471512show ga COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application. |*Machine Learning[MESH] |*Metabolomics[MESH] |Adult[MESH] |Aged[MESH] |Automation[MESH] |Biomarkers/metabolism[MESH] |Brazil[MESH] |COVID-19/*diagnosis/virology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Risk Assessment[MESH]Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics (epmc).pdf DeepDyve Pubget Overpricing