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10.3389/fchem.2021.746134 http://scihub22266oqcxt.onion/10.3389/fchem.2021.746134 34660538!8517325!34660538     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Chem 2021 ; 9 (ä): 746134 Nephropedia Template TP {{tp|p=34660538|t=2021. Machine Learning of Serum Metabolic Patterns Encodes Asymptomatic SARS-CoV-2 Infection |pdf=|usr=}}{{34660538}} gab.com Text Machine Learning of Serum Metabolic Patterns Encodes Asymptomatic SARS-CoV-2 Infection JO: Front Chem http://www.kidney.de/pget.php?&tw=1&pmid=34660538 #FOAvip Asymptomatic COVID-19 has become one of the biggest challenges for controlling the spread of the SARS-CoV-2. Diagnosis of asymptomatic COVID-19 mainly depends on quantitative reverse transcription PCR (qRT-PCR), which is typically time-consuming and requires expensive reagents. The application is limited in countries that lack sufficient resources to handle large-scale assay during the COVID-19 outbreak. Here, we demonstrated a new approach to detect the asymptomatic SARS-CoV-2 infection using serum metabolic patterns combined with ensemble learning. The direct patterns of metabolites and lipids were extracted by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) within 1 s with simple sample preparation. A new ensemble learning model was developed using stacking strategy with a new voting algorithm. This approach was validated in a large cohort of 274 samples (92 asymptomatic COVID-19 and 182 healthy control), and provided the high accuracy of 93.4%, with only 5% false negative and 7% false positive rates. We also identified a biomarker panel of ten metabolites and lipids, as well as the altered metabolic pathways during asymptomatic SARS-CoV-2 Infection. The proposed rapid and low-cost approach holds promise to apply in the large-scale asymptomatic COVID-19 screening. Twit Text FOAVip Machine Learning of Serum Metabolic Patterns Encodes Asymptomatic SARS-CoV-2 Infection ????Front Chem http://www.kidney.de/pget.php?&tw=1&pmid=34660538 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34660538 #FOAvip English Wikipedia <ref>{{Cite pmid|34660538}}</ref> Machine Learning of Serum Metabolic Patterns Encodes Asymptomatic SARS-CoV-2 Infection #MMPMID34660538 Wan Q; Chen M; Zhang Z; Yuan Y; Wang H; Hao Y; Nie W; Wu L; Chen S Front Chem 2021[]; 9 (ä): 746134 PMID34660538show ga Asymptomatic COVID-19 has become one of the biggest challenges for controlling the spread of the SARS-CoV-2. Diagnosis of asymptomatic COVID-19 mainly depends on quantitative reverse transcription PCR (qRT-PCR), which is typically time-consuming and requires expensive reagents. The application is limited in countries that lack sufficient resources to handle large-scale assay during the COVID-19 outbreak. Here, we demonstrated a new approach to detect the asymptomatic SARS-CoV-2 infection using serum metabolic patterns combined with ensemble learning. The direct patterns of metabolites and lipids were extracted by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) within 1 s with simple sample preparation. A new ensemble learning model was developed using stacking strategy with a new voting algorithm. This approach was validated in a large cohort of 274 samples (92 asymptomatic COVID-19 and 182 healthy control), and provided the high accuracy of 93.4%, with only 5% false negative and 7% false positive rates. We also identified a biomarker panel of ten metabolites and lipids, as well as the altered metabolic pathways during asymptomatic SARS-CoV-2 Infection. The proposed rapid and low-cost approach holds promise to apply in the large-scale asymptomatic COVID-19 screening. äMachine Learning of Serum Metabolic Patterns Encodes Asymptomatic SARS(epmc).pdf DeepDyve Pubget Overpricing