Extra-Low-Frequency Pulse Stimulated Conformational Change in Blood-Cell Proteins and Consequent Immune Activity Transformation

Inactivation of polyphenol oxidase by low intensity DC field: Experiment and mechanism analysis via molecular dynamics simulation and molecular docking

In this study, inactivation of mushroom polyphenol oxidase (PPO) by low intensity direct current (DC) electric field and its molecular mechanism were investigated. In the experiments under 3 V/cm, 5 V/cm, 7 V/cm and 9 V/cm electric fields, PPOs were all completely inactivated after different exposure times. Under 1 V/cm, a residual activity of 11.88 % remained. The inactivation kinetics confirms to Weibull model. Under 1-7 V/cm, n value closes to a constant about 1.3. The structural analysis of PPO under 3 V/cm and 5 V/cm by fluorescence emission spectroscopy and molecular dynamics (MD) simulation showed that the tertiary structure was slightly changed with increased radius of gyration, higher potential energy and rate of C-alpha fluctuation. After exposure to the electric field, most of the hydrophobic tryptophan (TRP) residues turned to the hydrophilic surface, resulting the fluorescence red-shifted and quenched. Molecular docking indicated that the receptor binding domain of catechol in PPO was changed. PPO under electric field was MD simulated the first time, revealing the changing mechanism of the electric field itself on PPO, a binuclear copper enzyme, which has a metallic center. All these suggest that the low intensity DC electric field would be a promising option for enzymatic browning inhibition or even enzyme activity inactivation.

Performance analysis of melanoma classifier using electrical modeling technique

An efficient and novel modeling approach is proposed in this paper for identifying proteins or genes involved in melanoma skin cancer. Two types of classifiers are modeled, based on the chemical structure and hydropathy property of amino acids. These classifiers are further implemented using NI LabVIEW-based hardware kit to observe the real-time response for proper diagnosis. The phase responses, pole-zero diagrams, and transient responses are examined to screen out the genes related to melanoma from healthy genes. The performance of the proposed classifier is measured using various performance measurement metrics in terms of accuracy, sensitivity, specificity, etc. The classifier is experimented along with a color code scheme on skin genes and illustrates the superiority in comparison with traditional methods by achieving 94% of classification accuracy with 96% of sensitivity.Graphical abstract An equivalent electrical model is developed for designing melanoma classifier. Initially, each amino acid is modeled using the RC passive circuit depending on their physicochemical structure and hydropathy nature, to form a gene structure model. The melanoma-related genes are detected by phase, transient, and color code analysis.