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10.1038/s41598-021-84565-3 http://scihub22266oqcxt.onion/10.1038/s41598-021-84565-3 33654146!7925543!33654146     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 4943 Nephropedia Template TP {{tp|p=33654146|t=2021. COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections |pdf=|usr=}}{{33654146}} gab.com Text COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33654146 #FOAvip The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89-92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model. Twit Text FOAVip COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33654146 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33654146 #FOAvip English Wikipedia <ref>{{Cite pmid|33654146}}</ref> COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections #MMPMID33654146 Carlomagno C; Bertazioli D; Gualerzi A; Picciolini S; Banfi PI; Lax A; Messina E; Navarro J; Bianchi L; Caronni A; Marenco F; Monteleone S; Arienti C; Bedoni M Sci Rep 2021[Mar]; 11 (1): 4943 PMID33654146show ga The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89-92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model. |Aged[MESH] |Aged, 80 and over[MESH] |Antibodies, Viral/analysis[MESH] |COVID-19/*diagnosis[MESH] |Comorbidity[MESH] |Computational Biology[MESH] |Deep Learning[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Normal Distribution[MESH] |Reproducibility of Results[MESH] |Saliva/*chemistry[MESH] |Sensitivity and Specificity[MESH]COVID-19 salivary Raman fingerprint: innovative approach for the detec(epmc).pdf DeepDyve Pubget Overpricing