Performance assessment of the Bruker Biotyper MALDI-TOF MS for the identification of difficult-to-identify viridans group streptococci.

Differentiating Streptococcus pneumoniae among nonpneumococcal viridans group streptococci (VGS) is challenging in conventional laboratories. Therefore, we aimed to evaluate the performance of the latest Bruker Biotyper matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) system in identifying VGS by comparing the results to those of the specific gene sequencing approach. Clinical isolates were initially identified using the BD Phoenix system to identify Streptococcus species. The optochin test was used to distinguish nonpneumococcal VGS from S. pneumoniae. The species of individual reference strains and clinical isolates were determined by comparing the sequences of the 16S rDNA, gyrB, sodA, groESL, or coaE genes with those in the GenBank sequence databases. We evaluated the performance of the Bruker Biotyper MALDI-TOF MS in VGS identification using two different machines with three databases. We collected a total of 103 nonpneumococcal VGS and 29 S. pneumoniae blood isolates at a medical center in northern Taiwan. Among these isolates, only seven could not be identified at the species level by the specific gene sequencing approach. We found that none of the nonpneumococcal VGS isolates were misidentified as pneumococci by the latest Biotyper system, and vice versa. However, certain strains, especially those in the mitis and bovis groups, could still not be correctly identified. The latest Bruker Biotyper 4.1 (DB_10833) showed significant improvement in identifying VGS strains. However, a specific gene sequencing test is still needed to precisely differentiate the species of strains in the mitis and bovis groups.

Phase pattern denoising using a regularized cost function with complex-valued Markov random fields based on a discrete model.

We present a simple and effective method for denoising phase patterns based on a discrete model. The proposed filtering method transforms the image denoising problem to solving the energy diffusion problem of a system with complex-valued fields. We establish an appropriate cost function that uses the discrete form of complex-valued Markov random fields. The attractiveness of the proposed filtering method includes three points: the first is that the filtering process can be easily implemented using an iterative method, the second is that 2π phase jumps are well preserved, and the third is its little computational effort. The performance of the proposed method is demonstrated by simulated and experimentally obtained phase patterns.