Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks.

BACKGROUND: Blockage of some ion channels and in particular, the hERG (human Ether-a'-go-go-Related Gene) cardiac potassium channel delays cardiac repolarization and can induce arrhythmia. In some cases it leads to a potentially life-threatening arrhythmia known as Torsade de Pointes (TdP). Therefore recognizing drugs with TdP risk is essential. Candidate drugs that are determined not to cause cardiac ion channel blockage are more likely to pass successfully through clinical phases II and III trials (and preclinical work) and not be withdrawn even later from the marketplace due to cardiotoxic effects. The objective of the present study is to develop an SAR (Structure-Activity Relationship) model that can be used as an early screen for torsadogenic (causing TdP arrhythmias) potential in drug candidates. The method is performed using descriptors comprised of atomic NMR chemical shifts (13C and 15N NMR) and corresponding interatomic distances which are combined into a 3D abstract space matrix. The method is called 3D-SDAR (3-dimensional spectral data-activity relationship) and can be interrogated to identify molecular features responsible for the activity, which can in turn yield simplified hERG toxicophores. A dataset of 55 hERG potassium channel inhibitors collected from Kramer et al. consisting of 32 drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR model. RESULTS: An artificial neural network (ANN) with multilayer perceptron was used to define collinearities among the independent 3D-SDAR features. A composite model from 200 random iterations with 25% of the molecules in each case yielded the following figures of merit: training, 99.2%; internal test sets, 66.7%; external (blind validation) test set, 68.4%. In the external test set, 70.3% of positive TdP drugs were correctly predicted. Moreover, toxicophores were generated from TdP drugs. CONCLUSION: A 3D-SDAR was successfully used to build a predictive model for drug-induced torsadogenic and non-torsadogenic drugs based on 55 compounds. The model was tested in 38 external drugs.

Alignment-independent technique for 3D QSAR analysis.

Molecular biochemistry is controlled by 3D phenomena but structure-activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules' potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions "Best-for-Each" template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R Test (2) = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R Test (2) = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3-7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R Test (2) = 0.65. The best 2D > 3D model was analyzed for underlying structure-activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates.

Instrumental improvements and sample preparations that enable reproducible, reliable acquisition of mass spectra from whole bacterial cells.

RATIONALE: Rapid sub-species characterization of pathogens is required for timely responses in outbreak situations. Pyrolysis mass spectrometry (PyMS) has the potential to be used for this purpose. METHODS: However, in order to make PyMS practical for traceback applications, certain improvements related to spectrum reproducibility and data acquisition speed were required. The main objectives of this study were to facilitate fast detection (<30 min to analyze 6 samples, including preparation) and sub-species-level bacterial characterization based on pattern recognition of mass spectral fingerprints acquired from whole cells volatilized and ionized at atmospheric pressure. An AccuTOF DART mass spectrometer was re-engineered to permit ionization of low-volatility bacteria by means of Plasma Jet Ionization (PJI), in which an electric discharge, and, by extension, a plasma beam, impinges on sample cells. RESULTS: Instrumental improvements and spectral acquisition methodology are described. Performance of the re-engineered system was assessed using a small challenge set comprised of assorted bacterial isolates differing in identity by varying amounts. In general, the spectral patterns obtained allowed differentiation of all samples tested, including those of the same genus and species but different serotypes. CONCLUSIONS: Fluctuations of ±15% in bacterial cell concentrations did not substantially compromise replicate spectra reproducibility.

Chromosome and gene copy number variation allow major structural change between species and strains of Leishmania.

Leishmania parasites cause a spectrum of clinical pathology in humans ranging from disfiguring cutaneous lesions to fatal visceral leishmaniasis. We have generated a reference genome for Leishmania mexicana and refined the reference genomes for Leishmania major, Leishmania infantum, and Leishmania braziliensis. This has allowed the identification of a remarkably low number of genes or paralog groups (2, 14, 19, and 67, respectively) unique to one species. These were found to be conserved in additional isolates of the same species. We have predicted allelic variation and find that in these isolates, L. major and L. infantum have a surprisingly low number of predicted heterozygous SNPs compared with L. braziliensis and L. mexicana. We used short read coverage to infer ploidy and gene copy numbers, identifying large copy number variations between species, with 200 tandem gene arrays in L. major and 132 in L. mexicana. Chromosome copy number also varied significantly between species, with nine supernumerary chromosomes in L. infantum, four in L. mexicana, two in L. braziliensis, and one in L. major. A significant bias against gene arrays on supernumerary chromosomes was shown to exist, indicating that duplication events occur more frequently on disomic chromosomes. Taken together, our data demonstrate that there is little variation in unique gene content across Leishmania species, but large-scale genetic heterogeneity can result through gene amplification on disomic chromosomes and variation in chromosome number. Increased gene copy number due to chromosome amplification may contribute to alterations in gene expression in response to environmental conditions in the host, providing a genetic basis for disease tropism.

Thymol treatment of bacteria prior to matrix-assisted laser desorption/ionization time-of-flight mass spectrometric analysis aids in identifying certain bacteria at the subspecies level.

RATIONALE: The identification of bacteria based on mass spectra produced by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) has become routine since its introduction in 1996. The major drawback is that bacterial patterns produced by MALDI are dependent on sample preparation prior to analysis. This results in poor reproducibility in identifying bacterial types and between laboratories. The need for a more broadly applicable and useful sample handling procedure is warranted. METHODS: Thymol was added to the suspension solvent of bacteria prior to MALDI analysis. The suspension solvent consisted of ethanol, water and TFA. The bacterium was added to the thymol suspension solvent and heated. An aliquot of the bacterial suspension was mixed directly with the matrix solution at a 9:1 ratio, matrix/bacteria solution, respectively. The mixture was then placed on the MALDI plate and allowed to air dry before MALDI analysis. RESULTS: The thymol method improved the quality of spectra and number of peaks when compared to other sample preparation procedures studied. The bacterium-identifying biomarkers assigned to four strains of E. coli were statistically 95% reproducible analyzed on three separate days. The thymol method successfully differentiated between the four E. coli strains. In addition, the thymol procedure could identify nine out of ten S. enterica serovars over a 3-day period and nine S. Typhimurium strains from the other ten serovars 90% of the time over the same period. CONCLUSIONS: The thymol method can identify certain bacteria at the sub-species level and yield reproducible results over time. It improves the quality of spectra by increasing the number of peaks when compared to the other sample preparation methods assessed in this study. Published in 2014. This article is a U.S. Government work and is in the public domain in the USA.

Computational identification of a phospholipidosis toxicophore using (13)C and (15)N NMR-distance based fingerprints.

Modified 3D-SDAR fingerprints combining (13)C and (15)N NMR chemical shifts augmented with inter-atomic distances were used to model the potential of chemicals to induce phospholipidosis (PLD). A curated dataset of 328 compounds (some of which were cationic amphiphilic drugs) was used to generate 3D-QSDAR models based on tessellations of the 3D-SDAR space with grids of different density. Composite PLS models averaging the aggregated predictions from 100 fully randomized individual models were generated. On each of the 100 runs, the activities of an external blind test set comprised of 294 proprietary chemicals were predicted and averaged to provide composite estimates of their PLD-inducing potentials (PLD+ if PLD is observed, otherwise PLD-). The best performing 3D-QSDAR model utilized a grid with a density of 8ppm×8ppm in the C-C region, 8ppm×20ppm in the C-N region and 20ppm×20ppm in the N-N region. The classification predictive performance parameters of this model evaluated on the basis of the external test set were as follows: accuracy=0.70, sensitivity=0.73 and specificity=0.66. A projection of the most frequently occurring bins on the standard coordinate space suggested a toxicophore composed of an aromatic ring with a centroid 3.5-7.5Å distant from an amino-group. The presence of a second aromatic ring separated by a 4-5Å spacer from the first ring and at a distance of between 5.5Å and 7Å from the amino-group was also associated with a PLD+ effect. These models provide comparable predictive performance to previously reported models for PLD with the added benefit of being based entirely on non-confidential, publicly available training data and with good predictive performance when tested in a rigorous, external validation exercise.

Photobleaching with phloxine B sensitizer to reduce food matrix interference for detection of Escherichia coli serotype O157:H7 in fresh spinach by flow cytometry.

A flow cytometric method (RAPID-B™) with detection sensitivity of one viable cell of Escherichia coli serotype O157:H7 in fresh spinach (Spinacia oleracea) was developed and evaluated. The major impediment to achieving this performance was mistaking autofluorescing spinach particles for tagged target cells. Following a 5 h non-selective enrichment, artificially inoculated samples were photobleached, using phloxine B as a photosensitizer. Samples were centrifuged at high speed to concentrate target cells, then gradient centrifuged to separate them from matrix debris. In external laboratory experiments, RAPID-B and the reference method both correctly detected E. coli O157:H7 at inoculations of ca. 15 cells. In a follow-up study, after 4 cell inoculations of positives and 6 h enrichment, RAPID-B correctly identified 92% of 25 samples. The RAPID-B method limit of detection (LOD) was one cell in 25 g. It proved superior to the reference method (which incorporated real time-PCR, selective enrichment, and culture plating elements) in accuracy and speed.

¹³C NMR-distance matrix descriptors: optimal abstract 3D space granularity for predicting estrogen binding.

An improved three-dimensional quantitative spectral data-activity relationship (3D-QSDAR) methodology was used to build and validate models relating the activity of 130 estrogen receptor binders to specific structural features. In 3D-QSDAR, each compound is represented by a unique fingerprint constructed from (13)C chemical shift pairs and associated interatomic distances. Grids of different granularity can be used to partition the abstract fingerprint space into congruent "bins" for which the optimal size was previously unexplored. For this purpose, the endocrine disruptor knowledge base data were used to generate 50 3D-QSDAR models with bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å, each of which was validated using 100 training/test set partitions. Best average predictivity in terms of R(2)test was achieved at 10 ppm ×10 ppm × Z Å (Z = 0.5, ..., 2.5 Å). It was hypothesized that this optimum depends on the chemical shifts' estimation error (±4.13 ppm) and the precision of the calculated interatomic distances. The highest ranked bins from partial least-squares weights were found to be associated with structural features known to be essential for binding to the estrogen receptor.

Reduction of food matrix interference by a combination of sample preparation and multi-dimensional gating techniques to facilitate rapid, high sensitivity analysis for Escherichia coli serotype O157 by flow cytometry.

Escherichia coli serotype O157 strains, which may be found in foods, often produce enterohemorrhagic toxins. The research goal was to facilitate rapid, sensitive detection in foods of E. coli serotype O157 by flow cytometry. Sample preparation methods were developed for potential use in 15 foods. Combined with multi-dimensional gating, these methods decreased time-to-results (TTR) for determination of low-level contamination. They mitigated the effects of interfering food components, concentrated cells for analysis without growth or, when necessary, used short-term incubation. The results showed qualitative analysis that was equivalent to culture plating in accuracy and superior in sensitivity and speed. Preparation time was 10-30 min per sample and detection took 3-4 min. Culture growth, if required, took an additional 4-6 h. A protocol for raw spinach analysis, using 4 h culture incubation, was 94% correct with one false negative for a low level inoculation. Its projected limit-of-detection (LOD) was 1 viable cell per 25 g of spinach, based on an average of 28 counts detected after growth and an estimated counts-to-threshold (C/T) ratio of 1.3. The results suggested potential uses for regulatory screening and food industry process control.

Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes.

An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals--drugs, pesticides, and environmental pollutants--interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D ¹³C and 1D ¹5N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D ¹³C-NMR and ¹5N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.

Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme.

Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2-3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D ¹³C-NMR and 1D ¹5N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.

Improving proton MR spectroscopy of brain tissue for noninvasive diagnostics.

PURPOSE: To examine preprocessing methods affecting the potential use of Magnetic Resonance Spectroscopy (MRS) as a noninvasive modality for detection and characterization of brain lesions and for directing therapy progress. MATERIALS AND METHODS: Two reference point re-calibration with linear interpolation (to compensate for magnetic field nonhomogeneity), weighting of spectra (to emphasize consistent peaks and depress chemical noise), and modeling based on chemical shift locations of 97 biomarkers were investigated. Results for 139 categorized scans were assessed by comparing Leave-One-Out (LOO) cross-validation and external validation. RESULTS: For distinction of nine brain tissue categories, use of re-calibration, variance weighting, and biomarker modeling improved LOO classification of MRS spectra from 31% to 95%. External validation of the two best nine-category models on 47 unknown samples gave 96% or 100% accuracy, respectively, compared with pathological diagnosis. CONCLUSION: Preprocessing of MRS spectra can significantly improve their diagnostic utility for automated consultation of pattern recognition models. Use of several techniques in combination greatly increases available proton MRS information content. Accurate assignment of unknowns among nine tissue classes represents a significant improvement, for a much more demanding task, than has been previously reported.

Modeling and assaying dioxin-like biological effects for both dioxin-like and certain non-dioxin-like compounds.

13C NMR data have been correlated to Toxic Equivalency Factors (TEFs) of the 29 PCDDs, PCDFs, or PCBs for which non-zero TEFs have been defined. Such correlations are called quantitative spectrometric data-activity relationship (QSDAR) models. An improved QSDAR model predicted TEFs of 0.037 and 0.004, respectively, for 1,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and 1,2,3,4,7-pentachlorodibenzo-p-dioxin (PeCDD), both among the 390 congeners for which zero value TEFs are assumed. A QSDAR model of Relative Potency (REP) values estimated the corresponding values as 0.115 and 0.020. Results from both models indicated that these two congeners may exhibit significant dioxin-like toxicity. If other such congeners have non-zero toxicity, TEF-based risk assessments of some dioxin-, furan-, or PCB-contaminated sites or foods may underestimate toxicity. Both models were extensively cross-validated and the TEF model was externally validated. We confirmed the predictions by an independent in vitro method, a luciferase gene expression assay based on mouse liver cells that found REPs of 0.027 and 0.013, respectively, for 1,3,7,8-TCDD and 1,2,3,4,7-PeCDD. The QSDAR-estimated and gene-expression assayed values agreed. The models were used to predict activity for an applicability domain including 108 non-2,3,7,8 dioxin, furan, or PCB congeners and 2,3,7,8-tetrachlorophenothiazine, a dioxin analog proposed as a drug candidate. This study showed that QSDAR prediction followed by a relatively inexpensive in vitro assay could be used to nominate a few candidates among hundreds for further investigation. It suggested that in silico and in vitro nomination protocols may facilitate practical risk assessment when chemical family members exhibit different degrees of toxicity operating via a common mechanism.

Rapid Flow Cytometry Detection of a Single Viable Escherichia coli O157:H7 Cell in Raw Spinach Using a Simplified Sample Preparation Technique.

Very low cell count detection of Escherichia coli O157:H7 in foods is critical, since an infective dose for this pathogen may be only 10 cells, and fewer still for vulnerable populations. A flow cytometer is able to detect and count individual cells of a target bacterium, in this case E. coli O157:H7. The challenge is to find the single cell in a complex matrix like raw spinach. To find that cell requires growing it as quickly as possible to a number sufficiently in excess of matrix background that identification is certain. The experimental design for this work was that of a U.S. Food and Drug Administration (FDA) In-House Level 3 validation executed in the technology's originating laboratory. Using non-selective enrichment broth, 6.5 h incubation at 42°C, centrifugation for target cell concentration, and a highly selective E. coli O157 fluorescent antibody tag, the cytometry method proved more sensitive than a reference regulatory method (p = 0.01) for detecting a single target cell, one E. coli O157:H7 cell, in 25 g of spinach. It counted that cell's daughters with at least 38× signal-to-noise ratio, analyzing 25 samples in total-time-to-results of 9 h.

Flow-Cytometry-Based Method to Detect Escherichia coli and Shigella Spp. Using 16S rRNA-Based Probe.

Detection of microbial contamination in foods before they go on to the market can help prevent the occurrence of foodborne illness outbreaks. Current methods for the detection of Escherichia coli are limited by time-consuming procedures, which include multiple culture incubation steps, and require several days to get results. This unit describes the development of an improved rapid flow-cytometry-based detection method that has greater sensitivity and specificity. This method requires less time-to-results (TTR) and can detect a small number of E. coli in the presence of large numbers of other bacteria. Clear step-by-step protocols for cell concentration determination, sample preparation, and flow cytometric analysis are provided. © 2017 by John Wiley & Sons, Inc.

3D-SDAR modeling of hERG potassium channel affinity: A case study in model design and toxicophore identification.

A dataset of 237 human Ether-à-go-go Related Gene (hERG) potassium channel inhibitors (180 of which were used for model building and validation, whereas 57 constituted the "true" external prediction set) collected from 22 literature sources was modeled by 3D-SDAR. To produce reliable and reproducible classification models for hERG blocking, the initial set of 180 chemicals was split into two subsets: a balanced modeling set consisting of 118 compounds and an unbalanced validation set comprised of 62 compounds. A PLS bagging-like algorithm written in Matlab was used to process the data and assign each compound to one of the two (hERG+ or hERG-) activity classes. The best predictive model evaluated on the basis of a fully randomized hold-out test set (comprising 20% of the modeling set) used 4 latent variables and a grid of 6ppm×6ppm×1Å in the C-C region, 6ppm×30ppm×1Å in the C-N region, and 30ppm×30ppm×1Å in the N-N region. An overall accuracy of 0.84 was obtained for both the hold-out test set and the validation set. Further, an external prediction set consisting of 57 drugs and drug derivatives was used to estimate the true predictive power of the reported 3D-SDAR model - a slight reduction of the overall accuracy down to 0.77 was observed. 3D-SDAR map of the most frequently occurring bins and their projection on the standard coordinate space of the chemical structures allowed identification of a three-center toxicophore composed of two aromatic rings and an amino group. A U test along the distance axis of the most frequently occurring 3D-SDAR bins was used to set the distance limits of the toxicophore. This toxicophore was found to be similar to an earlier reported phospholipidosis (PLD) toxicophore.

Development of a Flow Cytometry-Based Method for Rapid Detection of Escherichia coli and Shigella Spp. Using an Oligonucleotide Probe.

Standard methods to detect Escherichia coli contamination in food use the polymerase chain reaction (PCR) and agar culture plates. These methods require multiple incubation steps and take a long time to results. An improved rapid flow-cytometry based detection method was developed, using a fluorescence-labeled oligonucleotide probe specifically binding a16S rRNA sequence. The method positively detected 51 E. coli isolates as well as 4 Shigella species. All 27 non-E. coli strains tested gave negative results. Comparison of the new genetic assay with a total plate count (TPC) assay and agar plate counting indicated similar sensitivity, agreement between cytometry cell and colony counts. This method can detect a small number of E.coli cells in the presence of large numbers of other bacteria. This method can be used for rapid, economical, and stable detection of E. coli and Shigella contamination in the food industry and other contexts.

Rapid phenotypic characterization of Salmonella enterica strains by pyrolysis metastable atom bombardment mass spectrometry with multivariate statistical and artificial neural network pattern recognition.

Pyrolysis mass spectrometry was investigated for rapid characterization of bacteria. Spectra of Salmonella were compared to their serovars, pulsed-field gel electrophoresis (PFGE) patterns, antibiotic resistance profiles, and MIC values. Pyrolysis mass spectra generated via metastable atom bombardment were analyzed by multivariate principal component-discriminant analysis and artificial neural networks (ANNs). Spectral patterns developed by discriminant analysis and tested with Leave-One-Out (LOO) cross-validation distinguished Salmonella strains by serovar (97% correct) and by PFGE groups (49%). An ANN model of the same PFGE groups was cross-validated, using the LOO rule, with 92% agreement. Using an ANN, thirty previously unseen spectra were correctly classified by serotype (97%) and at the PFGE level (67%). Attempts by ANN to model spectra grouped by resistance profile-but ignoring PFGE or serotype-failed (10% correct), but ANNs differentiating ten samples of the same serotype/PFGE class were more successful. To assess the information content of PyMS data serendipitously associated with or directly related to resistance character, the ten isolates were grouped into four, three, or two categories. The four categories corresponded to four resistance profiles. The four class and three class ANNs showed much improved but insufficient modeling power. The two-class ANN and a corresponding multivariate model maximized inferential power for a coarse antibiotic-resistance-related distinction. They each cross-validated by LOO at 90%. This is the first direct correlation of pyrolysis metastable atom bombardment mass spectrometry with immunological (e.g. serology) or molecular biology (e.g. PFGE) based techniques.

Level 2 validation of a flow cytometric method for detection of Escherichia coli O157:H7 in raw spinach.

The Bacteriological Analytical Manual (BAM) method currently used by the United States Food and Drug Administration (FDA) to detect Escherichia coli O157:H7 in spinach was systematically compared to a new flow cytometry based method. This Food and Drug Administration (FDA) level 2 external laboratory validation study was designed to determine the latter method's sensitivity and speed for analysis of this pathogen in raw spinach. Detection of target cell inoculations with a low cell count is critical, since enterohemorrhagic strains of E. coli require an infective dose of as few as 10 cells (Schmid-Hempel and Frank, 2007). Although, according to the FDA, the infectious dose is unknown (Food and Drug Administration, 1993). Therefore, the inoculation level into the spinach, a total of 2.0±2.6 viable E. coli O157 cells, was specified to yield between 25% and 75% detection by the new method, out of 20 samples (10 positives and 10 negatives). This criterion was met in that the new method detected 60% of the nominally positive samples; the corresponding sensitivity of the reference method was 50%. For both methods the most likely explanation for false negatives was that no viable cells were actually introduced into the sample. In this validation study, the flow cytometry method was equal to the BAM in sensitivity and far superior in speed.