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10.3389/fimmu.2021.636289 http://scihub22266oqcxt.onion/10.3389/fimmu.2021.636289 33763080!7982722!33763080     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 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Immunol 2021 ; 12 (ä): 636289 Nephropedia Template TP {{tp|p=33763080|t=2021. Predicting Lyme Disease From Patients Peripheral Blood Mononuclear Cells Profiled With RNA-Sequencing |pdf=|usr=}}{{33763080}} gab.com Text Predicting Lyme Disease From Patients Peripheral Blood Mononuclear Cells Profiled With RNA-Sequencing JO: Front Immunol http://www.kidney.de/pget.php?&tw=1&pmid=33763080 #FOAvip Although widely prevalent, Lyme disease is still under-diagnosed and misunderstood. Here we followed 73 acute Lyme disease patients and uninfected controls over a period of a year. At each visit, RNA-sequencing was applied to profile patients' peripheral blood mononuclear cells in addition to extensive clinical phenotyping. Based on the projection of the RNA-seq data into lower dimensions, we observe that the cases are separated from controls, and almost all cases never return to cluster with the controls over time. Enrichment analysis of the differentially expressed genes between clusters identifies up-regulation of immune response genes. This observation is also supported by deconvolution analysis to identify the changes in cell type composition due to Lyme disease infection. Importantly, we developed several machine learning classifiers that attempt to perform various Lyme disease classifications. We show that Lyme patients can be distinguished from the controls as well as from COVID-19 patients, but classification was not successful in distinguishing those patients with early Lyme disease cases that would advance to develop post-treatment persistent symptoms. Twit Text FOAVip Predicting Lyme Disease From Patients Peripheral Blood Mononuclear Cells Profiled With RNA-Sequencing ????Front Immunol http://www.kidney.de/pget.php?&tw=1&pmid=33763080 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33763080 #FOAvip English Wikipedia <ref>{{Cite pmid|33763080}}</ref> Predicting Lyme Disease From Patients Peripheral Blood Mononuclear Cells Profiled With RNA-Sequencing #MMPMID33763080 Clarke DJB; Rebman AW; Bailey A; Wojciechowicz ML; Jenkins SL; Evangelista JE; Danieletto M; Fan J; Eshoo MW; Mosel MR; Robinson W; Ramadoss N; Bobe J; Soloski MJ; Aucott JN; Ma'ayan A Front Immunol 2021[]; 12 (ä): 636289 PMID33763080show ga Although widely prevalent, Lyme disease is still under-diagnosed and misunderstood. Here we followed 73 acute Lyme disease patients and uninfected controls over a period of a year. At each visit, RNA-sequencing was applied to profile patients' peripheral blood mononuclear cells in addition to extensive clinical phenotyping. Based on the projection of the RNA-seq data into lower dimensions, we observe that the cases are separated from controls, and almost all cases never return to cluster with the controls over time. Enrichment analysis of the differentially expressed genes between clusters identifies up-regulation of immune response genes. This observation is also supported by deconvolution analysis to identify the changes in cell type composition due to Lyme disease infection. Importantly, we developed several machine learning classifiers that attempt to perform various Lyme disease classifications. We show that Lyme patients can be distinguished from the controls as well as from COVID-19 patients, but classification was not successful in distinguishing those patients with early Lyme disease cases that would advance to develop post-treatment persistent symptoms. |Adult[MESH] |COVID-19/genetics/immunology[MESH] |Cytokines/genetics/immunology[MESH] |Female[MESH] |Follow-Up Studies[MESH] |Humans[MESH] |Leukocytes, Mononuclear/chemistry/*immunology[MESH] |Lyme Disease/blood/*genetics/immunology[MESH] |Machine Learning[MESH] |Male[MESH] |Middle Aged[MESH] |Prospective Studies[MESH]Predicting Lyme Disease From Patients Peripheral Blood Mononuclear Ce(epmc).pdf DeepDyve Pubget Overpricing