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Noninvasive Assessment of Kidney Injury by Combining Structure and Function Using Artificial Intelligence-Based Manganese-Enhanced Magnetic Resonance Imaging.
Zhou, Li; Yang, Zizhen; Guo, Li; Zou, Quan; Zhang, Hong; Sun, Shao-Kai; Ye, Zhaoxiang; Zhang, Cai.
Afiliação
  • Zhou L; Department of Radiology, Tianjin Chest Hospital, Tianjin 300052, China.
  • Yang Z; Department of Radiology, Ningbo No.2 Hospital, Ningbo 315012, China.
  • Guo L; School of Medical Imaging, Tianjin Medical University, Tianjin 300203, China.
  • Zou Q; Department of Radiology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
  • Zhang H; Department of Radiology, Tianjin Chest Hospital, Tianjin 300052, China.
  • Sun SK; School of Medical Imaging, Tianjin Medical University, Tianjin 300203, China.
  • Ye Z; Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China.
  • Zhang C; Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China.
ACS Appl Mater Interfaces ; 16(5): 5474-5485, 2024 Feb 07.
Article em En | MEDLINE | ID: mdl-38271189
ABSTRACT
Contrast-enhanced magnetic resonance imaging (MRI) is seriously limited in kidney injury detection due to the nephrotoxicity of clinically used gadolinium-based contrast agents. Herein, we propose a noninvasive method for the assessment of kidney injury by combining structure and function information based on manganese (Mn)-enhanced MRI for the first time. As a proof of concept, the Mn-melanin nanoprobe with good biocompatibility and excellent T1 relaxivity is applied in MRI of a unilateral ureteral obstruction mice model. The abundant renal structure and function information is obtained through qualitative and quantitative analysis of MR images, and a brand new comprehensive assessment framework is proposed to precisely identify the degree of kidney injury successfully. Our study demonstrates that Mn-enhanced MRI is a promising approach for the highly sensitive and biosafe assessment of kidney injury in vivo.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Manganês Tipo de estudo: Qualitative_research Limite: Animals Idioma: En Revista: ACS Appl Mater Interfaces Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Manganês Tipo de estudo: Qualitative_research Limite: Animals Idioma: En Revista: ACS Appl Mater Interfaces Ano de publicação: 2024 Tipo de documento: Article