Probing the existence of non-thermal Terahertz radiation induced changes of the protein solution structure

No observable non-thermal effect of microwave radiation on the growth of microtubules

Despite widespread public interest in the health impact of exposure to microwave radiation, studies of the influence of microwave radiation on biological samples are often inconclusive or contradictory. Here we examine the influence of microwave radiation of frequencies 3.5 GHz, 20 GHz and 29 GHz on the growth of microtubules, which are biological nanotubes that perform diverse functions in eukaryotic cells. Since microtubules are highly polar and can extend several micrometres in length, they are predicted to be sensitive to non-ionizing radiation. Moreover, it has been speculated that tubulin dimers within microtubules might rapidly toggle between different conformations, potentially participating in computational or other cooperative processes. Our data show that exposure to microwave radiation yields a microtubule growth curve that is distorted relative to control studies utilizing a homogeneous temperature jump. However, this apparent effect of non-ionizing radiation is reproduced by control experiments using an infrared laser or hot air to heat the sample and thereby mimic the thermal history of samples exposed to microwaves. As such, no non-thermal effects of microwave radiation on microtubule growth can be assigned. Our results highlight the need for appropriate control experiments in biophysical studies that may impact on the sphere of public interest.

Membrane-mediated modulation of mitochondrial physiology by terahertz waves

Extensive studies have demonstrated the diverse impacts of electromagnetic waves at gigahertz and terahertz (THz) frequencies on cytoplasmic membrane properties. However, there is little evidence of these impacts on intracellular membranes, particularly mitochondrial membranes crucial for mitochondrial physiology. In this study, human neuroblast-like cells were exposed to continuous 0.1 THz radiation at an average power density of 33 mW/cm2. The analysis revealed that THz exposure significantly altered the mitochondrial ultrastructure. THz waves enhanced the enzymatic activity of the mitochondrial respiratory chain but disrupted supercomplex assembly, compromising mitochondrial respiration. Molecular dynamics simulations revealed altered rates of change in the quantity of hydrogen bonds and infiltration of water molecules in lipid bilayers containing cardiolipin, indicating the specific behavior of cardiolipin, a signature phospholipid in mitochondria, under THz exposure. These findings suggest that THz radiation can significantly alter mitochondrial membrane properties, impacting mitochondrial physiology through a mechanism related to mitochondrial membrane, and provide deeper insight into the bioeffects of THz radiation.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.