Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease

The aim of this study was to establish a peptidomic profile based on LC-MS/MS and random forest (RF) algorithm to distinguish the urinary peptidomic scenario of type 2 diabetes mellitus (T2DM) patients with different stages of diabetic kidney disease (DKD). Urine from 60 T2DM patients was collected:...

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Detalles Bibliográficos
Autores principales: Brondani, Letícia de Almeida, Soares, Ariana Aguiar, Recamonde-Mendoza, Mariana, Dall’Agnol, Angélica, Camargo, Joíza Lins, Monteiro, Karina Mariante, Silveiro, Sandra Pinho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6985249/
https://www.ncbi.nlm.nih.gov/pubmed/31988353
http://dx.doi.org/10.1038/s41598-020-58067-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6985249/
https://www.ncbi.nlm.nih.gov/pubmed/31988353
http://dx.doi.org/10.1038/s41598-020-58067-7

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