Highly accurate heart failure classification using carbon nanotube thin film biosensors and machine learning assisted data analysis

Guo, Zilei; Tian, Renfang; Xu, Wenzuo; Yip, Darren; Radyk, Mary; Santos, Francesca Brillantes; Yip, Amelia; Chen, Tao; Tang, Xiaowu Shirley

doi:10.1016/j.biosx.2022.100187

Cited by 10 publications

(4 citation statements)

References 34 publications

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“…In this mercury detection platform, machine-learning algorithms such as random forest, multilayer perceptron, and XGBoost are both utilized as classification and regression algorithms to classify and predict the fluorescence intensity and concentration of heavy metals. Furthermore, Guo et al developed carbon nanotube thin film biosensors for the identification of heart failure . This study utilized a classification-based machine learning algorithm to aid in the identification process.…”

Section: Computer Technology In Sensing Systemsmentioning

confidence: 99%

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Automation and Computerization of (Bio)sensing Systems

Raju,

Elpa,

Urban

2024

ACS Sens.

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Sensing systems necessitate automation to reduce human effort, increase reproducibility, and enable remote sensing. In this perspective, we highlight different types of sensing systems with elements of automation, which are based on flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. Finally, we discuss the role of computer technology in sensing systems. Automated flow injection and sequential injection techniques offer precise and efficient sample handling and dependable outcomes. They enable continuous analysis of numerous samples, boosting throughput, and saving time and resources. They enhance safety by minimizing contact with hazardous chemicals. Microfluidic systems are enhanced by automation to enable precise control of parameters and increase of analysis speed. Robotic sampling and sample preparation platforms excel in precise execution of intricate, repetitive tasks such as sample handling, dilution, and transfer. These platforms enhance efficiency by multitasking, use minimal sample volumes, and they seamlessly integrate with analytical instruments. Other sensor prototypes utilize mechanical devices and computer technology to address real-world issues, offering efficient, accurate, and economical real-time solutions for analyte identification and quantification in remote areas. Computer technology is crucial in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from the sensor data, supporting the Internet of Things with efficient data management.

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Section: Computer Technology In Sensing Systemsmentioning

confidence: 99%

“…Furthermore, Guo et al developed carbon nanotube thin film biosensors for the identification of heart failure. 157 This study utilized a classification-based machine learning algorithm to aid in the identification process. The application of deep learning techniques in biosensors is gradually expanding.…”

Section: Computer Technology In Sensing Systemsmentioning

confidence: 99%

Automation and Computerization of (Bio)sensing Systems

Raju,

Elpa,

Urban

2024

ACS Sens.

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“…As a result, CNT has found extensive application across diverse domains like electrocatalysis, photocatalysis, electromagnetic shielding, and electrochemical sensors 38 – 41 . In a research conducted by Wang and colleagues, an electrode modified with a 3D g-C 3 N 4 /multi-wall (MW) CNTs/graphene oxide (GO) hybrid was fabricated and applied as an electrochemical sensor for the simultaneous detection of uric acid, dopamine, and ascorbic acid 42 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids

Habibi,

Mousavi,

Shekofteh-Gohari

et al. 2024

Sci Rep

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The simultaneous identification of drugs has considerable difficulties due to the intricate interplay of analytes and the interference present in biological matrices. In this study, we introduce an innovative electrochemical sensor that overcomes these hurdles, enabling the precise and simultaneous determination of morphine (MOR), methadone (MET), and uric acid (UA) in urine samples. The sensor harnesses the strategically adapted carbon nanotubes (CNT) modified with graphitic carbon nitride (g-C3N4) nanosheets to ensure exceptional precision and sensitivity for the targeted analytes. Through systematic optimization of pivotal parameters, we attained accurate and quantitative measurements of the analytes within intricate matrices employing the fast Fourier transform (FFT) voltammetry technique. The sensor’s performance was validated using 17 training and 12 test solutions, employing the widely acclaimed machine learning method, partial least squares (PLS), for predictive modeling. The root mean square error of cross-validation (RMSECV) values for morphine, methadone, and uric acid were significantly low, measuring 0.1827 µM, 0.1951 µM, and 0.1584 µM, respectively, with corresponding root mean square error of prediction (RMSEP) values of 0.1925 µM, 0.2035 µM, and 0.1659 µM. These results showcased the robust resiliency and reliability of our predictive model. Our sensor’s efficacy in real urine samples was demonstrated by the narrow range of relative standard deviation (RSD) values, ranging from 3.71 to 5.26%, and recovery percentages from 96 to 106%. This performance underscores the potential of the sensor for practical and clinical applications, offering precise measurements even in complex and variable biological matrices. The successful integration of g-C3N4-CNT nanocomposites and the robust PLS method has driven the evolution of sophisticated electrochemical sensors, initiating a transformative era in drug analysis.

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“…1,2 Electrochemical, optical, and electromechanical biosensors are the three main types. 3 Different groups use different signal transduction mechanisms. Evidence suggests that biosensors with nanoscale capabilities are promising.…”

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confidence: 99%

Design of Terahertz Refractive Index-Based Spiral Hollow-Core Photonic Crystal Biosensor Using Enhanced Probabilistic Pyramid Neural Networks for Brain Tumor Detection

2024

ECS J. Solid State Sci. Technol.

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Cancer diagnosis is difficult and costly due to the complexity of the brain. Photonic technology-based biosensors show potential for identifying malignant tissues, including brain tumors, but they are often costly, time-consuming, and computationally difficult. To address these challenges, we propose an enhanced probabilistic pyramid neural networks (EPPNN)-based hollow-core photonic crystal fiber (PCF) biosensor with terahertz refractive index (THzBio-ECPPN) for detection of cancerous brain tumors. The approach is divided into two stages: biosensor design and brain tumor detection. Initially, PCF geometry with suspended cladding and a spiral-shaped hollow-core in the terahertz (THz) band is proposed. The PCF biosensors' characteristics are then calculated using the EPPNN model. The EPPNN model's hyperparameters are modified using the circle-inspired optimization algorithm to maximize accuracy and minimize effective mode loss. The proposed biosensor is then used to identify brain tumors. Experimental evaluations utilizing MATLAB show that the suggested strategy surpasses earlier methods, with a higher sensitivity (98%). The sensor has exceptional performance characteristics, such as a high figure of merit of 1.25-1.35 RI range and sensitivity of 50000 nm/RIU, indicating its potential for precise detection of changes in refractive index. This combination of photonic crystal structures and neural networks has enormous potential for improving cancerous tumor accuracy to 99.92%, precision to 99.23%, specificity to 99.73%,and sensitivity to 99.36% of brain tumor diagnosis

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Highly accurate heart failure classification using carbon nanotube thin film biosensors and machine learning assisted data analysis

Cited by 10 publications

References 34 publications

Automation and Computerization of (Bio)sensing Systems

Automation and Computerization of (Bio)sensing Systems

Machine learning-enhanced drug testing for simultaneous morphine and methadone detection in urinary biofluids

Design of Terahertz Refractive Index-Based Spiral Hollow-Core Photonic Crystal Biosensor Using Enhanced Probabilistic Pyramid Neural Networks for Brain Tumor Detection

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