Machine learning models for predicting steroid-resistant of nephrotic syndrome

BACKGROUND: In the absence of effective measures to predict steroid responsiveness, patients with nonhereditary steroid-resistant nephrotic syndrome (SRNS) have a significantly increased risk of progression to end-stage renal disease. In view of the poor outcomes of SRNS, it is urgent to identify th...

Descripción completa

Detalles Bibliográficos
Autores principales: Ye, Qing, Li, Yuzhou, Liu, Huihui, Mao, Jianhua, Jiang, Hangjin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Immunology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911108/
https://www.ncbi.nlm.nih.gov/pubmed/36776850
http://dx.doi.org/10.3389/fimmu.2023.1090241

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911108/
https://www.ncbi.nlm.nih.gov/pubmed/36776850
http://dx.doi.org/10.3389/fimmu.2023.1090241

Ejemplares similares