Multiarray Biosensor for Diagnosing Lung Cancer Based on Gap Plasmonic Color Films

Abstract

Adaptable and sensitive materials are essential for the development of advanced sensor systems such as bio and chemical sensors. Biomaterials can be used to develop multifunctional biosensor applications using genetic engineering. In particular, a plasmonic sensor system using a coupled film nanostructure with tunable gap sizes is a potential candidate in optical sensors because of its simple fabrication, stability, extensive tuning range, and sensitivity to small changes. Although this system has shown a good ability to eliminate humidity as an interferant, its performance in real-world environments is limited by low selectivity. To overcome these issues, we demonstrated the rapid response of gap plasmonic color sensors by utilizing metal nanostructures combined with genetically engineered M13 bacteriophages to detect volatile organic compounds (VOCs) and diagnose lung cancer from breath samples. The M13 bacteriophage was chosen as a recognition element because the structural protein capsid can readily be modified to target the desired analyte. Consequently, the VOCs from various functional groups were distinguished by using a multiarray biosensor based on a gap plasmonic color film observed by hierarchical cluster analysis. Furthermore, the lung cancer breath samples collected from 70 healthy participants and 50 lung cancer patients were successfully classified with a high rate of over 89% through supporting machine learning analysis.

Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data for Advanced Colorimetric E-Nose

“…For reference, the data format which was previously used to prepare RGB measurements for CNN analysis is shown in Figure S6. 22 The architecture of the CNN model was designed with a 3-layer filter and a flattening layer (Figure 3a). Each building block comprises a series of 2D convolutional layers followed by a maximum pooling layer, rectified linear unit (ReLu), and an adaptive moment estimation (Adam) method as an activation function and optimizer.…”

“…For the demonstration and comparison, we created a CNN model with TRH and RGB data, respectively, using the same experimental conditions and data processing approach to classify RH concentration (see the Supporting Information for details). For reference, the data format which was previously used to prepare RGB measurements for CNN analysis is shown in Figure S6 . The architecture of the CNN model was designed with a 3-layer filter and a flattening layer (Figure a).…”

“…The proposed technology is expected to be used in the development of next-generation e-nose colorimetric sensor systems for complex multigas classification, not only based on bacteriophage sensor arrays but also utilizing the enhanced chromaticity and fast response characteristics from state-of-theart colorimetric materials research with chemical synthesis and nanophotonic approaches. 3,4,21,22 ■ MATERIALS AND METHODS All experimental and simulation methods are described in the Supporting Information.…”

“…Advanced detection of biological and chemical substances by sensor systems is required for numerous applications including health care, environmental monitoring, and security. − Especially electronic nose (e-nose)-based pattern recognition methods have substantially increased the classification performance of gas sensors. − Among the various types of sensors, colorimetric sensors are promising for utilizing the e-nose mechanism in a variety of gas detection applications due to their simple array manufacturing method. − Colorimetric sensor array-based e-nose systems have demonstrated discrimination abilities for various volatile organic compounds (VOCs) in exhaled breath diagnostics. − However, most colorimetric e-nose experiments rely on conventional RGB sensors which cannot provide the full information on the dynamic spectral features induced by the target gases on the sensor arrays. Meanwhile, a hyperspectral imaging system can provide 3D information (2D spatial and 1D spectral data), preserving both full spectral information and the shape of the sample. , While the complete 3D hyperspectral data provides a tremendous advantage in feature recognition and classification, the increased dimensionality of this data introduces complexity in the analysis of colorimetric measurements with a simple algorithm. , The deep-learning (DL) approach enables the analysis of complex data in order to extract features, and, in particular, convolution neural networks (CNNs) have demonstrated outstanding abilities to capture spatial patterns and hierarchical features in visual data. , The utilization of convolution filters enables the model to learn while maintaining the association between neighboring pixels, thereby conserving spatial information and features .…”

Fate of Sniff-the-Diseases Through Nanomaterials-Supported Optical Biochip Sensors

“…Integration of plasmonic sensor systems with advanced materials and machine learning presents a promising avenue for enhancing the accuracy and reliability of lung cancer diagnosis through noninvasive breath analysis. 50 In addition to this breakthrough technology, various milestone studies have been cataloged in Table I, emphasizing the diverse range of advancements and achievements in the field of handheld diagnostic devices.…”