Validity assessment of Michigan's proposed qPCR threshold value for rapid water-quality monitoring of E. coli contamination.

Michigan's water-quality standards specify that E. coli concentrations at bathing beaches must not exceed 300 E. coli per 100 mL, as determined by the geometric mean of culture-based concentrations in three or more representative samples from a given beach on a given day. Culture-based analysis requires 18⁠-⁠24 h to complete, so results are not available on the day of sampling. This one-day delay is problematic because results cannot be used to prevent recreation at beaches that are unsafe on the sampling day, nor do they reliably indicate whether recreation should be prevented the next day, due to high between-day variability in E. coli concentrations demonstrated by previous studies. By contrast, qPCR-based E. coli concentrations can be obtained in 3-4 h, making same-day beach notification decisions possible. Michigan has proposed a qPCR threshold value (qTV) for E. coli of 1.863 log10 gene copies per reaction as a potential equivalent value to the state standard, based on statistical analysis of a set of state-wide training data from 2016 to 2018. The main purpose of the present study is to assess the validity of the proposed qTV by determining whether the implied qPCR-based beach notification decisions agree well with culture-based decisions on two sets of test data from 2016⁠-⁠2018 (6,564 samples) and 2019-2020 (3,205 samples), and whether performance of the proposed qTV is similar on the test and training data. The results show that performance of Michigan's proposed qTV on both sets of test data was consistently good (e.g., 95% agreement with culture-based beach notification decisions during 2019⁠-⁠2020) and was as good as or better than its performance on the training data set. The false-negative rate for the proposed qTV was 25-29%, meaning that beach notification decisions based on the qTV would be expected to permit recreation on the day of sampling in 25-29% of cases where the beach exceeds the state standard for FIB contamination. This false-negative rate is higher than one would hope to see but is well below the corresponding error rate for culture-based decisions, which permit recreation at beaches that exceed the state standard on the day of sampling in 100% of cases because of the one-day delay in obtaining results. The key advantage of qPCR-based analysis is that it permits a large percentage (71-75%) of unsafe beaches to be identified in time to prevent recreation on the day of sampling.

Large-scale comparison of E. coli levels determined by culture and a qPCR method (EPA Draft Method C) in Michigan towards the implementation of rapid, multi-site beach testing.

Fecal pollution remains a challenge for water quality managers at Great Lakes and inland recreational beaches. The fecal indicator of choice at these beaches is typically Escherichia coli (E. coli), determined by culture-based methods that require over 18 h to obtain results. Researchers at the United States Environmental Protection Agency (EPA) have developed a rapid E. coli qPCR methodology (EPA Draft Method C) that can provide same-day results for improving public health protection with demonstrated sensitivity, specificity, and data acceptance criteria. However, limited information is currently available to compare the occurrence of E. coli determined by cultivation and by EPA Draft Method C (Method C). This study provides a large-scale data collection effort to compare the occurrence of E. coli determined by these alternative methods at more than 100 Michigan recreational beach and other sites using the complete set of quantitative data pairings and selected subsets of the data and sites meeting various eligibility requirements. Simple linear regression analyses of composite (pooled) data indicated a correlation between results of the E. coli monitoring approaches for each of the multi-site datasets as evidenced by Pearson R-squared values ranging from 0.452 to 0.641. Theoretical Method C threshold values, expressed as mean log10 target gene copies per reaction, that corresponded to an established E. coli culture method water quality standard of 300 MPN or CFU /100 mL varied only from 1.817 to 1.908 for the different datasets using this model. Different modeling and derivation approaches that incorporated within and between-site variability in the estimates also gave Method C threshold values in this range but only when relatively well-correlated datasets were used to minimize the error. A hypothetical exercise to evaluate the frequency of water impairments based on theoretical qPCR thresholds corresponding to the E. coli water quality standard for culture methods suggested that the methods may provide the same beach notification outcomes over 90% of the time with Method C results differing from culture method results that indicated acceptable and unacceptable water quality at overall rates of 1.9% and 6.6%, respectively. Results from this study provide useful information about the relationships between E. coli determined by culture and qPCR methods across many diverse freshwater sites and should facilitate efforts to implement qPCR-based E. coli detection for rapid recreational water quality monitoring on a large scale in the State of Michigan.

A comparison of E. coli concentration estimates quantified by the EPA and a Michigan laboratory network using EPA Draft Method C.

We evaluated data from 10 laboratories that analyzed water samples from 82 recreational water sites across the state of Michigan between 2016 and 2018. Water sample replicates were analyzed by experienced U.S. Environmental Protection Agency (EPA) analysts and Michigan laboratories personnel, many of whom were newly trained, using EPA Draft Method C-a rapid quantitative polymerase chain reaction (qPCR) technique that provides same day Escherichia coli (E. coli) concentration results. Beach management decisions (i.e. remain open or issue an advisory or closure) based on E. coli concentration estimates obtained by Michigan labs and by the EPA were compared; the beach management decision agreed in 94% of the samples analyzed. We used the Wilcoxon one-sample signed rank test and nonparametric quantile regression to assess (1) the degree of agreement between E. coli concentrations quantified by Michigan labs versus the EPA and (2) Michigan lab E. coli measurement precision, relative to EPA results, in different years and water body types. The median quantile regression curve for Michigan labs versus EPA approximated the 1:1 line of perfect agreement more closely as years progressed. Similarly, Michigan lab E. coli estimates precision also demonstrated yearly improvements. No meaningful difference was observed in the degree of association between Michigan lab and EPA E. coli concentration estimates for inland lake and Great Lakes samples (median regression curve average slopes 0.93 and 0.95, respectively). Overall, our study shows that properly trained laboratory personnel can perform Draft Method C to a degree comparable with experienced EPA analysts. This allows health departments that oversee recreational water quality monitoring to be confident in qPCR results generated by the local laboratories responsible for analyzing the water samples.

Simplified Analysis of Measurement Data from A Rapid E. coli qPCR Method (EPA Draft Method C) Using A Standardized Excel Workbook.

Draft method C is a standardized method for quantifying E. coli densities in recreational waters using quantitative polymerase chain reaction (qPCR). The method includes a Microsoft Excel workbook that automatically screens for poor-quality data using a set of previously proposed acceptance criteria, generates weighted linear regression (WLR) composite standard curves, and calculates E. coli target gene copies in test samples. We compared standard curve parameter values and test sample results calculated with the WLR model to those from a Bayesian master standard curve (MSC) model using data from a previous multi-lab study. The two models' mean intercept and slope estimates from twenty labs' standard curves were within each other's 95% credible or confidence intervals for all labs. E. coli gene copy estimates of six water samples analyzed by eight labs were highly overlapping among labs when quantified with the WLR and MSC models. Finally, we compared multiple labs' 2016-2018 composite curves, comprised of data from individual curves where acceptance criteria were not used, to their corresponding composite curves with passing acceptance criteria. Composite curves developed from passing individual curves had intercept and slope 95% confidence intervals that were often narrower than without screening and an analysis of covariance test was passed more often. The Excel workbook WLR calculation and acceptance criteria will help laboratories implement draft method C for recreational water analysis in an efficient, cost-effective, and reliable manner.

Evaluation of multiple laboratory performance and variability in analysis of recreational freshwaters by a rapid Escherichia coli qPCR method (Draft Method C).

There is interest in the application of rapid quantitative polymerase chain reaction (qPCR) methods for recreational freshwater quality monitoring of the fecal indicator bacteria Escherichia coli (E. coli). In this study we determined the performance of 21 laboratories in meeting proposed, standardized data quality acceptance (QA) criteria and the variability of target gene copy estimates from these laboratories in analyses of 18 shared surface water samples by a draft qPCR method developed by the U.S. Environmental Protection Agency (EPA) for E. coli. The participating laboratories ranged from academic and government laboratories with more extensive qPCR experience to "new" water quality and public health laboratories with relatively little previous experience in most cases. Failures to meet QA criteria for the method were observed in 24% of the total 376 test sample analyses. Of these failures, 39% came from two of the "new" laboratories. Likely factors contributing to QA failures included deviations in recommended procedures for the storage and preparation of reference and control materials. A master standard curve calibration model was also found to give lower overall variability in log10 target gene copy estimates than the delta-delta Ct (ΔΔCt) calibration model used in previous EPA qPCR methods. However, differences between the mean estimates from the two models were not significant and variability between laboratories was the greatest contributor to overall method variability in either case. Study findings demonstrate the technical feasibility of multiple laboratories implementing this or other qPCR water quality monitoring methods with similar data quality acceptance criteria but suggest that additional practice and/or assistance may be valuable, even for some more generally experienced qPCR laboratories. Special attention should be placed on providing and following explicit guidance on the preparation, storage and handling of reference and control materials.

Standardized data quality acceptance criteria for a rapid Escherichia coli qPCR method (Draft Method C) for water quality monitoring at recreational beaches.

There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made.

Effects of combined opioids on pain and mood in mammals.

The authors review the opioid literature for evidence of increased analgesia and reduced adverse side effects by combining mu-opioid-receptor (MOR) agonists, kappa-opioid-receptor (KOR) agonists, and nonselective low-dose-opioid antagonists (LD-Ant). We tested fentanyl (MOR agonist) and spiradoline (KOR agonist), singly and combined, against somatic and visceral pain models. Combined agonists induced additive analgesia in somatic pain and synergistic analgesia in visceral pain. Other investigators report similar effects and reduced tolerance and dependence with combined MOR agonist and KOR agonist. LD-Ant added to either a MOR agonist or KOR agonist markedly enhanced analgesia of either agonist. In accordance with other place-conditioning (PC) studies, our PC investigations showed fentanyl-induced place preference (CPP) and spiradoline-induced place aversion (CPA). We reduced fentanyl CPP with a low dose of spiradoline and reduced spiradoline CPA with a low dose of fentanyl. We propose combined MOR agonist, KOR agonist, and LD-Ant to produce superior analgesia with reduced adverse side effects, particularly for visceral pain.

Antinociceptive interactions of micro- and kappa-opioid agonists in the colorectal distension assay in rats.

Interactions of opioid agonists, fentanyl and oxymorphone (micro-selective) and spiradoline and enadoline(kappa-selective), were examined for additive, sub-additive, or supra-additive antinociception in the colorectal distension (CRD) assay. Single-dose values (mg/kg, 0.006-0.016 for fentanyl, 0.25-1.26 forspiradoline, etc.) were summed to formulate theoretical additive-dose plots for comparison with actual combined-dose effects. Combined fentanyl and spiradoline yielded additive (low-dose levels) or supraadditive(high-dose levels) effects. Single and combined doses of fentanyl (0.012 mg/kg) and spiradoline(0.3 mg/kg) were tested after pretreatment with saline, beta-funaltrexamine (b-FNA, micro-selective antagonist), or nor-binaltorphimine (n-BNI, kappa-selective antagonist). Supra-additive effects of combined agonists were attenuated by either antagonist (greater with n-BNI). But paradoxical patterns of antagonism of single-dose effects occurred: the fentanyl antinociception was not antagonized by b-FNA, whereas the spiradoline antinociception was. The results indicate complex interactions of agonists in this visceral pain model and potential for combined agonists to improve pain relief with decreased side effects