MS2 Coliphage as a Surrogate for 2009 Pandemic Influenza A (H1N1) Virus (pH1N1) in Surface Survival Studies on N95 Filtering Facepiece Respirators.

Research on influenza viruses regarding transmission and survival has surged in the recent years due to infectious emerging strains and outbreaks such as the 2009 Influenza A (H1N1) pandemic. MS2 coliphage has been applied as a surrogate for pathogenic respiratory viruses, such as influenza, as it's safe for personnel to handle and requires less time and labor to measure virus infectivity. However, direct comparisons to determine the effectiveness of coliphage as a surrogate for influenza virus regarding droplet persistence on personal protective equipment such as N95 filtering facepiece respirators (FFRs) are lacking. Persistence of viral droplets deposited on FFRs in healthcare settings is important to discern due to the potential risk of infection via indirect fomite transmission. The objective of this study was to determine if MS2 coliphage could be applied as a surrogate for influenza A viruses for studying persistence when applied to the FFRs as a droplet. The persistence of MS2 coliphage and 2009 Pandemic Influenza A (H1N1) Virus on FFR coupons in different matrices (viral media, 2% fetal bovine serum, and 5 mg ml-1 mucin) were compared over time (4, 12, 24, 48, 72, and 144 hours) in typical absolute humidity conditions (4.1 × 105 mPa [18°C/20% relative humidity (RH)]). Data revealed significant differences in viral infectivity over the 6-day period (H1N1- P <0.0001; MS2 - P <0.005), although a significant correlation of viral log10 reduction in 2% FBS (P <0.01) was illustrated. Overall, MS2 coliphage was not determined to be a sufficient surrogate for influenza A virus with respect to droplet persistence when applied to the N95 FFR as a droplet.

A method to determine the available UV-C dose for the decontamination of filtering facepiece respirators.

AIMS: To develop a method to assess model-specific parameters for ultraviolet-C (UV-C, 254 nm) decontamination of filtering facepiece respirators (FFRs). METHODS AND RESULTS: UV-C transmittance was quantified for the distinct composite layers of six N95 FFR models and used to calculate model-specific α-values, the percentage of the surface UV-C irradiance available for the internal filtering medium (IFM). Circular coupons, excised from the FFRs, were exposed to aerosolized particles containing MS2 coliphage and treated with IFM-specific UV-C doses ranging from 38 to 4707 J m(-2). Models exposed to a minimum IFM dose of 1000 J m(-2) demonstrated at least a 3 log reduction (LR) in viable MS2. Model-specific exposure times to achieve this IFM dose ranged from 2 to 266 min. CONCLUSIONS: UV-C transmits into and through FFR materials. LR of MS2 was a function of model-specific IFM UV-C doses. SIGNIFICANCE AND IMPACT OF THE STUDY: Filtering facepiece respirators are in high demand during infectious disease outbreaks, potentially leading to supply shortages. Reuse of disposable FFRs after decontamination has been discussed as a possible remediation strategy, but to date lacks supporting scientific evidence. The methods described here can be used to assess the likelihood that UV-C decontamination will be successful for specific FFR models.

Application of quantitative chemometric analysis techniques to direct sampling mass spectrometry.

This paper explores the use of direct sampling mass spectrometry coupled with multivariate chemometric analysis techniques for the analysis of sample mixtures containing analytes with similar mass spectra. Water samples containing varying mixtures of toluene, ethyl benzene, and cumene were analyzed by purge-and-trap/direct sampling mass spectrometry. Multivariate calibration models were built using partial least-squares regression (PLS), trilinear partial least-squares regression (tri-PLS), and parallel factor analysis (PARAFAC), with the latter two methods taking advantage of the differences in the temporal profiles of the analytes. The prediction errors for each model were compared to those obtained with simple univariate regression. Multivariate quantitative methods were found to be superior to univariate regression when a unique ion for quantitation could not be found. For prediction samples that contained unmodeled, interfering compounds, PARAFAC outperformed the other analysis methods. The uniqueness of the PARAFAC model allows for estimation of the mass spectra of the interfering compounds, which can be subsequently identified via visual inspection or a library search.

Selective metals determination with a photoreversible spirobenzopyran.

The photoreversible metal-ion complexation behavior of nitroquinolinospiropyranindoline (NQSP) was studied in combination with the selective identification of six different transition-metal ions, Zn(2+), Co(2+), Hg(2+), Cu(2+), Cd(2+), and Ni(2+), in single- and binary-component mixtures via partial least squares discriminant (PLSD) analysis. The plasticizer, dicapryl phthalate, was chosen as the support medium for this study on the basis of (1) its enhancement of the photoreversibility and hypsochromic shifts seen in metal complexation and (2) its potential application to supported liquid membranes for eventual sensor applications. Complexation of divalent transition-metal ions by NQSP in dicapryl phthalate produced variable hypsochromic shifts in the absorption spectra (30-60 nm), requiring chemometric techniques in order to overcome the spectral overlaps. PLSD analysis was used to build classification analysis models to differentiate between the six divalent transition-metal ions. The feasibility of performing mixture analysis was studied using the concept of net analyte signal prior to experimental verification. Single- and binary-component mixtures of metals were identified with 100 and 97.4% accuracy, respectively, which included no false positives in either the training or prediction sets.

Improved probabilistic neural network algorithm for chemical sensor array pattern recognition.

An improved probabilistic neural network (IPNN) algorithm for use in chemical sensor array pattern recognition applications is described. The IPNN is based on a modified probabilistic neural network (PNN) with three innovations designed to reduce the computational and memory requirements, to speed training, and to decrease the false alarm rate. The utility of this new approach is illustrated with the use of four data sets extracted from simulated and laboratory-collected surface acoustic wave sensor array data. A competitive learning strategy, based on a learning vector quantization neural network, is shown to reduce the storage and computation requirements. The IPNN hidden layer requires only a fraction of the storage space of a conventional PNN. A simple distance-based calculation is reported to approximate the optimal kernel width of a PNN. This calculation is found to decrease the training time and requires no user input. A general procedure for selecting the optimal rejection threshold for a PNN-based algorithm using Monte Carlo simulations is also demonstrated. This outlier rejection strategy is implemented for an IPNN classifier and found to reject ambiguous patterns, thereby decreasing the potential for false alarms.

Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy.

Genetic algorithms (GAs) are used to implement an automated wavelength selection procedure for use in building multivariate calibration models based on partial least-squares regression. The method also allows the number of latent variables used in constructing the calibration models to be optimized along with the selection of the wavelengths. The data used to test this methodology are derived from the determination of aqueous organic species by near-infrared spectroscopy. The three data sets employed focus on the determination of (1) methyl isobutyl ketone in water over the range of 1-160 ppm, (2) physiological levels of glucose in a phosphate buffer matrix containing bovine serum albumin and triacetin, and (3) glucose in a human serum matrix. These data sets feature analyte signals near the limit of detection and the presence of significant spectral interferences. Studies are performed to characterize the signal and noise characteristics of the spectral data, and optimal configurations for the GA are found for each data set through experimental design techniques. Despite the complexity of the spectral data, the GA procedure is found to perform well, leading to calibration models that significantly outperform those based on full spectrum analyses. In addition, a significant reduction in the number of spectral points required to build the models is realized.

Genetic algorithm-based protocol for coupling digital filtering and partial least-squares regression: application to the near-infrared analysis of glucose in biological matrices.

A multivariate calibration procedure is described that is based on the use of a genetic algorithm (GA) to guide the coupling of bandpass digital filtering and partial least-squares (PLS) regression. The measurement of glucose in three different biological matrices with near-infrared spectroscopy is employed to develop this protocol. The GA is employed to optimize the position and width of the bandpass digital filter, the spectral range for PLS regression, and the number of PLS factors used in building the calibration model. The optimization of these variables is difficult because the values of the variables employ different units, resulting in a tendency for local optima to occur on the response surface of the optimization. Two issues are found to be critical to the success of the optimization: the configuration of the GA and the development of an appropriate fitness function. An integer representation for the GA is employed to overcome the difficulty in optimizing variables that are dissimilar, and the optimal GA configuration is found through experimental design methods. Three fitness function calculations are compared for their ability to lead the GA to better calibration models. A fitness function based on the combination of the mean-squared error in the calibration set data, the mean-squared error in the monitoring set data, and the number of PLS factors raised to a weighting factor is found to perform best. Multiple random drawings of the calibration and monitoring sets are also found to improve the optimization performance. Using this fitness function and three random drawings of the calibration and monitoring sets, the GA found calibration models that required fewer PLS factors yet had similar or better prediction abilities compared to calibration models found through an optimization protocol based on a grid search method.