Comparison of methods for isolation of bacterial and fungal DNA from human blood

The study aimed at optimization of DNA isolation from blood of representatives of four microbial groups causing sepsis, i.e., Gram negative: Escherichia coli, Gram positive: Staphylococcus aureus, yeast: Candida albicans, and filamentous fungus: Aspergillus fumigatus. Additionally, the five commercial kits for microbial DNA isolation from the blood were tested. The developed procedure of DNA isolation consisted of three consecutive steps, i.e., mechanical disruption, chemical lysis, and thermal lysis. Afterward, DNA was isolated from the previously prepared samples (erythrocyte lysis) with the use of five commercial kits for DNA isolation. They were compared paying heed to detection limit, concentration, DNA purity, and heme concentration in samples. The isolation of DNA without preliminary erythrocyte lysis resulted in far higher heme concentration than when lysis was applied. In the variant with erythrocyte lysis, two of the commercial kits were most effective in purifying the DNA extract from heme. Designed procedure allowed obtaining microbial DNA from all four groups of pathogens under study in the amount sufficient to conduct the rtPCR reaction, which aimed at detecting them in the blood

The Combined Estimator for Stochastic Equations on Graphs with Fractional Noise

In the present paper, we study the problem of estimating a drift parameter in stochastic evolution equations on graphs. We focus on equations driven by fractional Brownian motions, which are particularly useful e.g., in biology or neuroscience. We derive a novel estimator (the combined estimator) and prove its strong consistency in the long-span asymptotic regime with a discrete-time sampling scheme. The promising performance of the combined estimator for finite samples is examined under various scenarios by Monte Carlo simulations