Integrated Urinalysis Devices Based on Interface‐Engineered Field‐Effect Transistor Biosensors Incorporated With Electronic Circuits. Issue 36 (7th August 2022)

• Record Type: Journal Article
• Authors: Yang, Yanbing; 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
• Is Part Of: Advanced materials. Volume 34:Issue 36(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-07
• Subjects: bladder cancer -- healthcare monitoring devices -- machine learning -- transistor biosensors -- urinalysis; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202203224
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract: 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. Abstract : A molecular‐specificity electronic device based on an interface‐engineered field‐effect transistor biosensor is engineered for non‐invasive urinalysis. The urinalysis device achieves the reliable identification of five bladder‐tumor‐associated proteins in urine samples. Integrated with a machine‐learning algorithm, the device realizes a 95.0% bladder cancer diagnosis accuracy and can classify cancer stages with a 90.0% accuracy.