ABSTRACT
BACKGROUND: Burkholderia cepacia complex (Bcc) has emerged as an important opportunistic pathogen with rising concern in pharmaceuticals and cosmetic products. The Bcc supplement (S2-BCC-S) was purposely developed and used with the Pseudomonas vial (PD-109) for the detection of Bcc through the Soleris® Next Generation automated instrument system. OBJECTIVE: This study aimed to evaluate the performance of the Soleris Bcc testing method for cosmetic products. METHOD: Inclusivity and exclusivity were assessed with the Soleris Bcc method and the United States Pharmacopeia (USP) method in three enrichment broths. Matrix testing was conducted using 28 cosmetic products to compare the equivalency of the Soleris Bcc method to that of the USP reference method. Repeatability of the Soleris Bcc assay, method robustness, product stability, and lot-to-lot consistency of the Soleris reagents were also assessed. RESULTS: Both the Soleris Bcc and the USP methods supported the growth of all 26 inclusivity strains, except the USP method missed one inclusivity strain in one broth. For exclusivity, 0-6% was presumptive positive with the Soleris Bcc method, and 42-48% was presumptive positive with the reference method. Kappa index was 0.96 for the matrix testing, indicating a good agreement between the Soleris Bcc assay and the reference method for testing Bcc in cosmetics. Repeatability results showed the coefficient of variation was less than 4%. The robustness and ruggedness study yielded detection times within 1 h differences when small variations were introduced. The lot-to-lot study showed consistent results among four lots of the Bcc reagents. CONCLUSIONS: The automated Soleris method was successfully demonstrated to be robust, sensitive, and specific for Bcc detection in cosmetic products. HIGHLIGHTS: The Soleris Bcc method is user-friendly. It shows the results in real time and generates the report automatically. Implementation of this method for detection of Bcc in cosmetics would save significant time and resources.
Subject(s)
Burkholderia cepacia complex , Cosmetics , Indicators and ReagentsABSTRACT
Reentrant electrical scroll waves have been shown to underlie many cardiac arrhythmias, but the inability to observe locations away from the heart surfaces and the restriction of observations to only one or two state variables have made understanding arrhythmia mechanisms challenging. Recently, we showed that data assimilation from spatiotemporally sparse surrogate observations could be used to reconstruct a reliable time series of state estimates of reentrant cardiac electrical waves including unobserved variables in one and three spatial dimensions. However, real cardiac tissue is unlikely to be described accurately by mathematical models because of errors in model formulation and parameterization as well as intrinsic but poorly described spatial heterogeneity of electrophysiological properties in the heart. Here, we extend our previous work to assess how model error affects the accuracy of cardiac state estimates achieved using data assimilation with the Local Ensemble Transform Kalman Filter. We focus on one-dimensional states of discordant alternans characterized by significant wavelength oscillations. We demonstrate that data assimilation can provide high-quality estimates under a wide range of model error conditions, ranging from varying one or more parameter values to using an entirely different model to generate the truth state. We illustrate how multiplicative and additive inflation can be used to reduce error in the state estimates. Even when the truth state contains underlying spatial heterogeneity, we show that using a homogeneous model in the data assimilation algorithm can achieve good results. Overall, we find data assimilation to be a robust approach for reconstructing complex cardiac electrical states corresponding to arrhythmias even in the presence of model error.
