Integrated Urinalysis Devices Based on Interface-Engineered Field-Effect Transistor Biosensors Incorporated With Electronic Circuits

Integrated Urinalysis Devices Based on Interface-Engineered Field-Effect Transistor Biosensors Incorporated With Electronic Circuits

© 2022 Wiley-VCH GmbH.

Bibliographische Detailangaben
Veröffentlicht in:Advanced materials (Deerfield Beach, Fla.). - 1998. - 34(2022), 36 vom: 02. Sept., Seite e2203224
1. Verfasser: Yang, Yanbing (VerfasserIn)
Weitere Verfasser: Wang, Jingfeng, Huang, Wanting, Wan, Guojia, Xia, Miaomiao, Chen, Duo, Zhang, Yun, Wang, Yiming, Guo, Fuding, Tan, Jie, Liang, Huageng, Du, Bo, Yu, Lilei, Tan, Weihong, Duan, Xiangfeng, Yuan, Quan
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2022
Zugriff auf das übergeordnete Werk:Advanced materials (Deerfield Beach, Fla.)
Schlagworte:Journal Article bladder cancer healthcare monitoring devices machine learning transistor biosensors urinalysis Biomarkers, Tumor Zinc Oxide SOI2LOH54Z
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520 |a Urinalysis is attractive in non-invasive early diagnosis of bladder cancer compared with clinical gold standard cystoscopy. However, the trace bladder tumor biomarkers in urine and the particularly complex urine environment pose significant challenges for urinalysis. Here, a clinically adoptable urinalysis device that integrates molecular-specificity indium gallium zinc oxide field-effect transistor (IGZO FET) biosensor arrays, a device control panel, and an internet terminal for directly analyzing five bladder-tumor-associated proteins in clinical urine samples, is reported for bladder cancer diagnosis and classification. The IGZO FET biosensors with engineered sensing interfaces provide high sensitivity and selectivity for identification of trace proteins in the complex urine environment. Integrating with a machine-learning algorithm, this device can identify bladder cancer with an accuracy of 95.0% in a cohort of 197 patients and 75 non-bladder cancer individuals, distinguishing cancer stages with an overall accuracy of 90.0% and assessing bladder cancer recurrence after surgical treatment. The non-invasive urinalysis device defines a robust technology for remote healthcare and personalized medicine 
650 4 |a Journal Article 
650 4 |a bladder cancer 
650 4 |a healthcare monitoring devices 
650 4 |a machine learning 
650 4 |a transistor biosensors 
650 4 |a urinalysis 
650 7 |a Biomarkers, Tumor  |2 NLM 
650 7 |a Zinc Oxide  |2 NLM 
650 7 |a SOI2LOH54Z  |2 NLM 
700 1 |a Wang, Jingfeng  |e verfasserin  |4 aut 
700 1 |a Huang, Wanting  |e verfasserin  |4 aut 
700 1 |a Wan, Guojia  |e verfasserin  |4 aut 
700 1 |a Xia, Miaomiao  |e verfasserin  |4 aut 
700 1 |a Chen, Duo  |e verfasserin  |4 aut 
700 1 |a Zhang, Yun  |e verfasserin  |4 aut 
700 1 |a Wang, Yiming  |e verfasserin  |4 aut 
700 1 |a Guo, Fuding  |e verfasserin  |4 aut 
700 1 |a Tan, Jie  |e verfasserin  |4 aut 
700 1 |a Liang, Huageng  |e verfasserin  |4 aut 
700 1 |a Du, Bo  |e verfasserin  |4 aut 
700 1 |a Yu, Lilei  |e verfasserin  |4 aut 
700 1 |a Tan, Weihong  |e verfasserin  |4 aut 
700 1 |a Duan, Xiangfeng  |e verfasserin  |4 aut 
700 1 |a Yuan, Quan  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t Advanced materials (Deerfield Beach, Fla.)  |d 1998  |g 34(2022), 36 vom: 02. Sept., Seite e2203224  |w (DE-627)NLM098206397  |x 1521-4095  |7 nnns 
773 1 8 |g volume:34  |g year:2022  |g number:36  |g day:02  |g month:09  |g pages:e2203224 
856 4 0 |u http://dx.doi.org/10.1002/adma.202203224  |3 Volltext 
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