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10.1002/path.5653 http://scihub22266oqcxt.onion/10.1002/path.5653 33626204!8014445!33626204     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       J+Pathol 2021 ; 254 (2): 173-184 Nephropedia Template TP {{tp|p=33626204|t=2021. Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure |pdf=|usr=}}{{33626204}} gab.com Text Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure JO: J Pathol http://www.kidney.de/pget.php?&tw=1&pmid=33626204 #FOAvip Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pneumopathy is characterized by a complex clinical picture and heterogeneous pathological lesions, both involving alveolar and vascular components. The severity and distribution of morphological lesions associated with SARS-CoV-2 and how they relate to clinical, laboratory, and radiological data have not yet been studied systematically. The main goals of the present study were to objectively identify pathological phenotypes and factors that, in addition to SARS-CoV-2, may influence their occurrence. Lungs from 26 patients who died from SARS-CoV-2 acute respiratory failure were comprehensively analysed. Robust machine learning techniques were implemented to obtain a global pathological score to distinguish phenotypes with prevalent vascular or alveolar injury. The score was then analysed to assess its possible correlation with clinical, laboratory, radiological, and tissue viral data. Furthermore, an exploratory random forest algorithm was developed to identify the most discriminative clinical characteristics at hospital admission that might predict pathological phenotypes of SARS-CoV-2. Vascular injury phenotype was observed in most cases being consistently present as pure form or in combination with alveolar injury. Phenotypes with more severe alveolar injury showed significantly more frequent tracheal intubation; longer invasive mechanical ventilation, illness duration, intensive care unit or hospital ward stay; and lower tissue viral quantity (p < 0.001). Furthermore, in this phenotype, superimposed infections, tumours, and aspiration pneumonia were also more frequent (p < 0.001). Random forest algorithm identified some clinical features at admission (body mass index, white blood cells, D-dimer, lymphocyte and platelet counts, fever, respiratory rate, and PaCO(2) ) to stratify patients into different clinical clusters and potential pathological phenotypes (a web-app for score assessment has also been developed; https://r-ubesp.dctv.unipd.it/shiny/AVI-Score/). In SARS-CoV-2 positive patients, alveolar injury is often associated with other factors in addition to viral infection. Identifying phenotypical patterns at admission may enable a better stratification of patients, ultimately favouring the most appropriate management. (c) 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. Twit Text FOAVip Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure ????J Pathol http://www.kidney.de/pget.php?&tw=1&pmid=33626204 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33626204 #FOAvip English Wikipedia <ref>{{Cite pmid|33626204}}</ref> Machine learning-based analysis of alveolar and vascular injury in SARS-CoV-2 acute respiratory failure #MMPMID33626204 Calabrese F; Pezzuto F; Fortarezza F; Boscolo A; Lunardi F; Giraudo C; Cattelan A; Del Vecchio C; Lorenzoni G; Vedovelli L; Sella N; Rossato M; Rea F; Vettor R; Plebani M; Cozzi E; Crisanti A; Navalesi P; Gregori D J Pathol 2021[Jun]; 254 (2): 173-184 PMID33626204show ga Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pneumopathy is characterized by a complex clinical picture and heterogeneous pathological lesions, both involving alveolar and vascular components. The severity and distribution of morphological lesions associated with SARS-CoV-2 and how they relate to clinical, laboratory, and radiological data have not yet been studied systematically. The main goals of the present study were to objectively identify pathological phenotypes and factors that, in addition to SARS-CoV-2, may influence their occurrence. Lungs from 26 patients who died from SARS-CoV-2 acute respiratory failure were comprehensively analysed. Robust machine learning techniques were implemented to obtain a global pathological score to distinguish phenotypes with prevalent vascular or alveolar injury. The score was then analysed to assess its possible correlation with clinical, laboratory, radiological, and tissue viral data. Furthermore, an exploratory random forest algorithm was developed to identify the most discriminative clinical characteristics at hospital admission that might predict pathological phenotypes of SARS-CoV-2. Vascular injury phenotype was observed in most cases being consistently present as pure form or in combination with alveolar injury. Phenotypes with more severe alveolar injury showed significantly more frequent tracheal intubation; longer invasive mechanical ventilation, illness duration, intensive care unit or hospital ward stay; and lower tissue viral quantity (p < 0.001). Furthermore, in this phenotype, superimposed infections, tumours, and aspiration pneumonia were also more frequent (p < 0.001). Random forest algorithm identified some clinical features at admission (body mass index, white blood cells, D-dimer, lymphocyte and platelet counts, fever, respiratory rate, and PaCO(2) ) to stratify patients into different clinical clusters and potential pathological phenotypes (a web-app for score assessment has also been developed; https://r-ubesp.dctv.unipd.it/shiny/AVI-Score/). In SARS-CoV-2 positive patients, alveolar injury is often associated with other factors in addition to viral infection. Identifying phenotypical patterns at admission may enable a better stratification of patients, ultimately favouring the most appropriate management. (c) 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. |*Machine Learning[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19/*diagnosis/*virology[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Respiratory Distress Syndrome/diagnosis/*etiology[MESH] |Respiratory Insufficiency/diagnosis/virology[MESH] |SARS-CoV-2/*pathogenicity[MESH]Machine learning-based analysis of alveolar and vascular injury in SAR(epmc).pdf DeepDyve Pubget Overpricing