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

Genetically engineered bacteriophages as novel nanomaterials: applications beyond antimicrobial agents

Bacteriophages, also known as phages, are viruses that replicate in bacteria and archaea. Phages were initially discovered as antimicrobial agents, and they have been used as therapeutic agents for bacterial infection in a process known as "phage therapy." Recently, phages have been investigated as functional nanomaterials in a variety of areas, as they can function not only as therapeutic agents but also as biosensors and tissue regenerative materials. Phages are nontoxic to humans, and they possess self-assembled nanostructures and functional properties. Additionally, phages can be easily genetically modified to display specific peptides or to screen for functional peptides via phage display. Here, we demonstrated the application of phage nanomaterials in the context of tissue engineering, sensing, and probing.

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.