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

The colorimetric sensor-based electronic nose has been demonstrated to discriminate specific gaseous molecules for various applications, including health or environmental monitoring. However, conventional colorimetric sensor systems rely on RGB sensors, which cannot capture the complete spectral response of the system. This limitation can degrade the performance of machine learning analysis, leading to inaccurate identification of chemicals with similar functional groups. Here, we propose a novel time-resolved hyperspectral (TRH) data set from colorimetric array sensors consisting of 1D spatial, 1D spectral, and 1D temporal axes, which enables hierarchical analysis of multichannel 2D spectrograms via a convolution neural network (CNN). We assessed the outstanding classification performance of the TRH data set compared to an RGB data set by conducting a relative humidity (RH) concentration classification. The time-dependent spectral response of the colorimetric sensor was measured and trained as a CNN model using TRH and RGB sensor systems at different RH levels. While the TRH model shows a high classification accuracy of 97.5% for the RH concentration, the RGB model yields 72.5% under identical conditions. Furthermore, we demonstrated the detection of various functional volatile gases with the TRH system by using experimental and simulation approaches. The results reveal distinct spectral features from the TRH system, corresponding to changes in the concentration of each substance.

3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer.

The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERS can enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced Raman intensity through detection limit down to 10-6 M (femtomole-(10-17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.

Non-intrusive quality appraisal of differentiation-induced cardiovascular stem cells using E-Nose sensor technology.

Stem cell technology holds immense potential for revolutionizing medicine, particularly in regenerative treatment for heart disease. The unique capacity of stem cells to differentiate into diverse cell types offers promise in repairing damaged tissues and implanting organs. Ensuring the quality of differentiated cells, essential for specific functions, demands in-depth analysis. However, this process consumes time and incurs substantial costs while invasive methods may alter stem cell features during differentiation and deplete cell numbers. To address these challenges, we propose a non-invasive strategy, using cellular respiration, to assess the quality of differentiation-induced stem cells, notably cardiovascular stem cells. This evaluation employs an electronic nose (E-Nose) and neural pattern separation (NPS). Our goal is to assess differentiation-induced cardiac stem cells (DICs) quality through E-Nose data analysis and compare it with standard commercial human cells (SCHCs). Sensitivity and specificity were evaluated by interacting SCHCs and DICs with the E-Nose, achieving over 90% classification accuracy. Employing selective combinations optimized by NPS, E-Nose successfully classified all six cell types. Consequently, the relative similarity among DICs like cardiomyocytes, endothelial cells with SCHCs was established relied on comparing response data from the E-Nose sensor without resorting to complex evaluations.

Understanding the Role of M13 Bacteriophage Thin Films on a Metallic Nanostructure through a Standard and Dynamic Model.

The dynamic and surface manipulation of the M13 bacteriophage via the meeting application demands the creation of a pathway to design efficient applications with high selectivity and responsivity rates. Here, we report the role of the M13 bacteriophage thin film layer that is deposited on an optical nanostructure involving gold nanoparticles/SiO2/Si, as well as its influence on optical and geometrical properties. The thickness of the M13 bacteriophage layer was controlled by varying either the concentration or humidity exposure levels, and optical studies were conducted. We designed a standard and dynamic model based upon three-dimensional finite-difference time-domain (3D FDTD) simulations that distinguished the respective necessity of each model under variable conditions. As seen in the experiments, the origin of respective peak wavelength positions was addressed in detail with the help of simulations. The importance of the dynamic model was noted when humidity-based experiments were conducted. Upon introducing varied humidity levels, the dynamic model predicted changes in plasmonic properties as a function of changes in NP positioning, gap size, and effective index (this approach agreed with the experiments and simulated results). We believe that this work will provide fundamental insight into understanding and interpreting the geometrical and optical properties of the nanostructures that involve the M13 bacteriophage. By combining such significant plasmonic properties with the numerous benefits of M13 bacteriophage (like low-cost fabrication, multi-wavelength optical characteristics devised from a single structure, reproducibility, reversible characteristics, and surface modification to suit application requirements), it is possible to develop highly efficient integrated plasmonic biomaterial-based sensor nanostructures.

Artificial Neural Processing-Driven Bioelectronic Nose for the Diagnosis of Diabetes and Its Complications.

Diabetes and its complications affect the younger population and are associated with a high mortality rate; however, early diagnosis can contribute to the selection of appropriate treatment regimens that can reduce mortality. Although diabetes diagnosis via exhaled breath has great potential for early diagnosis, research on such diagnosis is restricted to disease detection, requiring in-depth examination to diagnose and classify diseases and their complications. This study demonstrates the use of an artificial neural processing-based bioelectronic nose to accurately diagnose diabetes and classify diabetic types (type I and II) and their complications, such as heart disease. Specifically, an M13 phage-based electronic nose (e-nose) is used to explore the features of subjects with diabetes at various levels of cellular and organismal organization (cells, liver organoids, and mice). Exhaled breath samples are collected during culturing and exposed to the phage-based e-nose. Compared with cells, liver organoids cultured under conditions mimicking a diabetic environment display properties that closely resemble the characteristics of diabetic mice. Using neural pattern separation, the M13 phage-based e-nose achieves a classification success rate of over 86% for four conditions in mice, namely, type 1 diabetes, type 2 diabetes, diabetic cardiomyopathy, and cardiomyopathy.

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

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.

Investigation of colorimetric biosensor array based on programable surface chemistry of M13 bacteriophage towards artificial nose for volatile organic compound detection: From basic properties of the biosensor to practical application.

Various threats such as explosives, drugs, environmental hormones, and spoiled food manifest themselves with the presence of volatile organic compounds (VOCs) in our environment. In order to recognize and respond to these threats early, the demand for highly sensitive and selective electronic noses is increasing. The M13 bacteriophage-based optoelectronic nose is an excellent candidate to meet all these requirements. However, the phage-based electronic nose is still in its infancy, and strategies that include a systematic approach and development are still essential. Here, we have integrated theoretical and experimental approaches to analyze the correlation between the surface chemistry of genetically engineered phage and the phage-based optoelectronic nose properties. The reactivity of the genetically engineered phage color film to some VOCs were quantitatively analyzed, and the correlation with the binding affinity value calculated by Density-functional theory (DFT) was compared. This demonstrates that phage color films have controllable reactivity through a genetic engineering. We have selected phages that are advantageous in distinguishing each VOCs in this work through hierarchical cluster analysis (HCA). The reason for this difference was verified through the optimized geometry calculated by DFT. Through this, it was confirmed that the tryptophan-based and the Histidine-based of genetically engineered phage film are important in distinguishing the VOCs (Y-hexanolactone, 2-isopropyl-4-methylthiazole, ethanol, acetone, ethyl acetate, and acetaldehyde) used in this work to evaluate the peach freshness quality. This was applied to the design of a field-applied phage-based optoelectronic nose and verified by measuring the freshness of the actual fruit.

Programmable self-assembly of M13 bacteriophage for micro-color pattern with a tunable colorization.

Over the last decade, the M13 bacteriophage has been used widely in various applications, such as sensors, bio-templating, and solar cells. The M13 colorimetric sensor was developed to detect toxic gases to protect the environment, human health, and national security. Recent developments in phage-based colorimetric sensor technologies have focused on improving the sensing characteristics, such as the sensitivity and selectivity on a large scale. On the other hand, few studies have examined precisely controllable micro-patterning techniques in phage-based self-assembly. This paper developed a color patterning technique through self-assembly of the M13 bacteriophages. The phage was self-assembled into a nanostructure through precise temperature control at the meniscus interface. Furthermore, barcode color patterns could be fabricated using self-assembled M13 bacteriophage on micrometer scale areas by manipulating the grooves on the SiO2 surface. The color patterns exhibited color tunability based on the phage nano-bundles reactivity. Overall, the proposed color patterning technique is expected to be useful for preparing new color sensors and security patterns.

M13 Bacteriophage-Assisted Morphological Engineering of Crack-Based Sensors for Highly Sensitive and Wide Linear Range Strain Sensing.

Despite their extraordinary mechanosensitivities, most channel-like crack-based strain sensors are limited by their poor levels of stretchability and linearity. This work presents a simple yet efficient way of modulating the cracking structure of thin metal films on elastomers to facilitate the development of high-performance wearable strain sensors. A net-shaped crack structure based on a thin platinum (Pt) film can be produced by coating an elastomer surface with M13 bacteriophages (phages) and consequently engineering the surface strain upon stretching. This process produces a Pt-on-phage (PoP) strain sensor that simultaneously exhibits high levels of stretchability (24%), sensitivity (maximum gauge factor ≈ 845.6 for 20-24%), and linearity (R2 ≈ 0.988 up to 20%). In addition, the sensor performance can be further modulated by either changing the phage coating volume or adding a silver nanowire coating to the PoP sensor film. The balanced strain-sensing performance, combined with fast response times and high levels of mechanical flexibility and operational stability, enables the devices to detect a wide range of human motions in real time after being attached to various body parts. Furthermore, PoP-based strain sensors can be usefully extended to detect more complex multidimensional strains through further strain engineering on a cross-patterned PoP film.