Recently, new virus-based sensor systems that operate on M13 bacteriophage infrastructure have attracted considerable attention. These systems can detect a range of chemicals with excellent sensitivity and selectivity. Filaments consistent with M13 bacteriophages can be ordered by highly established forms of self-assembly. This allows M13 bacteriophages to build a homogeneous distribution and infiltrate the network structure of nanostructures under mild conditions. Phage display, involving the genetic engineering of M13 bacteriophages, is another strong feature of the M13 bacteriophage as a functional building block. The numerous genetic modification possibilities of M13 bacteriophages are clearly the key features, and far more applications are envisaged. This paper reviews the recent progress in the application of the M13 bacteriophage self-assembly structures through to sensor systems and discusses future M13 bacteriophage technology.

M13 bacteriophage-based colorimetric sensors, especially multi-array sensors, have been successfully demonstrated to be a powerful platform for detecting extremely small amounts of target molecules. Colorimetric sensors can be fabricated easily using self-assembly of genetically engineered M13 bacteriophage which incorporates peptide libraries on its surface. However, the ability to discriminate many types of target molecules is still required. In this work, we introduce a statistical method to efficiently analyze a huge amount of numerical results in order to classify various types of target molecules. To enhance the selectivity of M13 bacteriophage-based colorimetric sensors, a multi-array sensor system can be an appropriate platform. On this basis, a pattern-recognizing multi-array biosensor platform was fabricated by integrating three types of sensors in which genetically engineered M13 bacteriophages (wild-, RGD-, and EEEE-type) were utilized as a primary building block. This sensor system was used to analyze a pattern of color change caused by a reaction between the sensor array and external substances, followed by separating the specific target substances by means of hierarchical cluster analysis. The biosensor platform could detect drug contaminants such as hormone drugs (estrogen) and antibiotics. We expect that the proposed biosensor system could be used for the development of a first-analysis kit, which would be inexpensive and easy to supply and could be applied in monitoring the environment and health care.

The COVID-19 pandemic was caused by a highly contagious coronavirus that has triggered worldwide control actions such as social distancing and lockdowns. COVID-19 control actions have resulted in improved air quality locally and around the world in the short-term by limiting human activity. We analyzed the impacts of social distancing and transboundary pollutants on air quality changes using open data and examined the corresponding health benefits focusing on two domestic cities (Seoul and Daegu) in Korea where the spread of coronavirus was severe. During the COVID-19 pandemic, PM_2.5, PM₁₀, and NO₂ concentrations decreased significantly by 31%, 61%, and 33%, respectively, compared to the previous three years. In particular, the PM_2.5/PM₁₀ ratio fell 24.5% after the implementation of social distancing, suggesting a decrease in anthropogenic emissions. Moreover, we found that the air quality index (AQI) also improved significantly, with a focus on reducing exposure to sensitive groups. In Seoul and Daegu, improved air quality prevented 250 and 78 premature deaths, and health costs were USD 884 million and USD 278 million, respectively. On the other hand, health loss due to COVID-19 deaths was in sharp contrast to USD 7.1 million and USD 543.6 million. Our findings indicate a significant association between COVID-19 prevalence patterns and health outcomes.