Electric Field Assisted Self-Assembly of Viruses into Colored Thin Films

Effects of the Magnetic Orientation of M13 Bacteriophage on Phage Display Selection

Although phage display selection using a library of M13 bacteriophage has become a powerful tool for finding peptides that bind to target materials on demand, a remaining concern of this method is the interference by the M13 main body, which is a huge filament >103  times larger than the displayed peptide, and therefore would nonspecifically adhere to the target or sterically inhibit the binding of the displayed peptide. Meanwhile, filamentous phages are known to be orientable by an external magnetic field. If M13 filaments are magnetically oriented during the library selection, their angular arrangement relative to the target surface would be changed, being expected to control the interference by the M13 main body. This study reports that the magnetic orientation of M13 filaments vertical to the target surface significantly affects the selection. When the target surface was affinitive to the M13 main body, this orientation notably suppressed the nonspecific adhesion. Furthermore, when the target surface was less affinitive to the M13 main body and intrinsically free from the nonspecific adhesion, this orientation drastically changed the population of M13 clones obtained through library selection. The method of using no chemicals but only a physical stimulus is simple, clean, and expected to expand the scope of phage display selection.

Evaluation of the Morphological and Biological Functions of Vascularized Microphysiological Systems with Supervised Machine Learning

Vascularized microphysiological systems and organoids are contemporary preclinical experimental platforms representing human tissue or organ function in health and disease. While vascularization is emerging as a necessary physiological organ-level feature required in most such systems, there is no standard tool or morphological metric to measure the performance or biological function of vascularized networks within these models. Further, the commonly reported morphological metrics may not correlate to the network's biological function-oxygen transport. Here, a large library of vascular network images was analyzed by the measure of each sample's morphology and oxygen transport potential. The oxygen transport quantification is computationally expensive and user-dependent, so machine learning techniques were examined to generate regression models relating morphology to function. Principal component and factor analyses were applied to reduce dimensionality of the multivariate dataset, followed by multiple linear regression and tree-based regression analyses. These examinations reveal that while several morphological data relate poorly to the biological function, some machine learning models possess a relatively improved, but still moderate predictive potential. Overall, random forest regression model correlates to the biological function of vascular networks with relatively higher accuracy than other regression models.

Recent Progress in the Detection of Bacteria Using Bacteriophages: A Review

Bacteria will likely become our most significant enemies of the 21st century, as we are approaching a post-antibiotic era. Bacteriophages, viruses that infect bacteria, allow us to fight infections caused by drug-resistant bacteria and create specific, cheap, and stable sensors for bacteria detection. Here, we summarize the recent developments in the field of phage-based methods for bacteria detection. We focus on works published after mid-2017. We underline the need for further advancements, especially related to lowering the detection (below 1 CFU/mL; CFU stands for colony forming units) and shortening the time of analysis (below one hour). From the application point of view, portable, cheap, and fast devices are needed, even at the expense of sensitivity.

Phage-Based Sensors in Medicine: A Review

Bacteriophages are interesting entities on the border of biology and chemistry. In nature, they are bacteria parasites, while, after genetic manipulation, they gain new properties, e.g., selectively binding proteins. Owing to this, they may be applied as recognition elements in biosensors. Combining bacteriophages with different transducers can then result in the development of innovative sensor designs that may revolutionize bioanalytics and improve the quality of medical services. Therefore, here, we review the use of bacteriophages, or peptides from bacteriophages, as new sensing elements for the recognition of biomarkers and the construction of the highly effective diagnostics tools.