High-Frequency Alternating Current Block Using Macro-Sieve Electrodes: A Pilot Study.

Background and objective High-frequency alternating current (HFAC) can yield a rapid-acting and reversible nerve conduction block. The present study aimed to demonstrate the successful implementation of HFAC block delivery via regenerative macro-sieve electrodes (MSEs). Methods Dual-electrode assemblies in two configurations [dual macro-sieve electrode-1 (DMSE-I), DMSE-II] were fabricated from pairs of MSEs and implanted in the transected and subsequently repaired sciatic nerves of two male Lewis rats. After four months of postoperative nerve regeneration through the MSEs' transit zones, the efficacy of acute HFAC block was tested for both configurations. Frequencies ranging from 10 kHz to 42 kHz, and stimulus amplitudes with peak-to-peak voltages ranging from 2 V to 20 V were tested. Evoked muscle force measurement was used to quantify the nerve conduction block. Results HFAC stimulation delivered via DMSE assemblies obtained a complete block at frequencies of 14 to 26 kHz and stimulus amplitudes of 12 to 20 V p-p. The threshold voltage for the complete block showed an approximately linear dependence on frequency. The threshold voltage for the partial conduction block was also approximately linear. For those frequencies that displayed both partial and complete block, the partial block thresholds were consistently lower. Conclusion This study provides a proof of concept that regenerative MSEs can achieve complete and reversible conduction block via HFAC stimulation of regenerated nerve tissue. A chronically interfaced DMSE assembly may thereby facilitate the inactivation of targeted nerves in cases wherein pathologic neuronal hyperactivity is involved.

A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States.

Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

An in silico structural, functional and phylogenetic analysis with three dimensional protein modeling of alkaline phosphatase enzyme of Pseudomonas aeruginosa.

Phosphorus is a primary macronutrient required for normal plant health, metabolism and survival. It is present in soil in compound insoluble form for which plant cannot uptake it directly from the soil. Some phosphate solubilizing bacteria possess some important enzymes for phosphate solubilization as well as mineralization. Alkaline (or basic) phosphatase (EC 3.1.3.1) is a type of zinc containing dimeric hydrolase enzyme responsible for removing the phosphate groups from various kinds of molecules including nucleotides, proteins, and alkaloids. Unlike acid phosphatases alkaline phosphatases are most effective in an alkaline environment. Alkaline phosphatases (ALPs) are of immense importance in various agricultural industries including dairy industries for testing successful pasteurization process. In this present study, Pseudomonas aeruginosa phosphatase was selected for a detailed computational investigation to exploit its physicochemical characteristics, structural properties including 3D model, model quality analysis, phylogenetic assessment and functional analysis using a number of available standard bioinformatics tools. The protein having average molecular weight about 51 kDa, was found thermostable and alkaline in nature belonging to metalloenzyme superfamily. Specifically, the thermostable behavior of the protein is suitable for the dairy industry. Moreover, this theoretical overview will help researchers to get an idea about the predicted protein structure and it may also help to design genetically engineered phosphate solubilizing bacteria by designing specific primers.