Successful Detection of Delta and Omicron Variants of SARS-CoV-2 by Veterinary Diagnostic Laboratory Participants in an Interlaboratory Comparison Exercise.

BACKGROUND: Throughout the COVID-19 pandemic, veterinary diagnostic laboratories have tested diagnostic samples for SARS-CoV-2 both in animals and over 6 million human samples. An evaluation of the performance of those laboratories is needed using blinded test samples to ensure that laboratories report reliable data to the public. This interlaboratory comparison exercise (ILC3) builds on 2 prior exercises to assess whether veterinary diagnostic laboratories can detect Delta and Omicron variants spiked in canine nasal matrix or viral transport medium. METHODS: The ILC organizer was an independent laboratory that prepared inactivated Delta variant at levels of 25 to 1000 copies per 50 µL of nasal matrix for blinded analysis. Omicron variant at 1000 copies per 50 µL of transport medium was also included. Feline infectious peritonitis virus (FIPV) RNA was used as a confounder for specificity assessment. Fourteen test samples were prepared for each participant. Participants used their routine diagnostic procedures for RNA extraction and real-time reverse transcriptase-PCR. Results were analyzed according to International Organization for Standardization (ISO) 16140-2:2016. RESULTS: Overall, laboratories demonstrated 93% detection for Delta and 97% for Omicron at 1000 copies per 50 µL. Specificity was 97% for blank samples and 100% for blank samples with FIPV. No differences in Cycle Threshold (Ct) values were significant for samples with the same virus levels between N1 and N2 markers, nor between the 2 variants. CONCLUSIONS: The results indicated that all ILC3 participants were able to detect both Delta and Omicron variants. The canine nasal matrix did not significantly affect SARS-CoV-2 detection.

Measurement uncertainty interval in case of a known relationship between precision and mean.

Background: Measurement uncertainty is typically expressed in terms of a symmetric interval y±U, where y denotes the measurement result and U the expanded uncertainty. However, in the case of heteroscedasticity, symmetric uncertainty intervals can be misleading. In this paper, a different approach for the calculation of uncertainty intervals is introduced. Methods: This approach is applicable when a validation study has been conducted with samples with known concentrations. In a first step, test results are obtained at the different known concentration levels. Then, on the basis of precision estimates, a prediction range is calculated. The measurement uncertainty for a given test result can then be obtained by projecting the intersection of the test result with the limits of the prediction range back onto the axis of the known values, now interpreted as representing the measurand. Results: It will be shown how, under certain circumstances, asymmetric uncertainty intervals arise quite naturally and lead to more reliable uncertainty intervals. Conclusions: This article establishes a conceptual framework in which measurement uncertainty can be derived from precision whenever the relationship between the latter and concentration has been characterized. This approach is applicable for different types of distributions. Closed expressions for the limits of the uncertainty interval are provided for the simple case of normally distributed test results and constant relative standard deviation.

Second round of an interlaboratory comparison of SARS-CoV2 molecular detection assays used by 45 veterinary diagnostic laboratories in the United States.

The COVID-19 pandemic presents a continued public health challenge. Veterinary diagnostic laboratories in the United States use RT-rtPCR for animal testing, and many laboratories are certified for testing human samples; hence, ensuring that laboratories have sensitive and specific SARS-CoV2 testing methods is a critical component of the pandemic response. In 2020, the FDA Veterinary Laboratory Investigation and Response Network (Vet-LIRN) led an interlaboratory comparison (ILC1) to help laboratories evaluate their existing RT-rtPCR methods for detecting SARS-CoV2. All participating laboratories were able to detect the viral RNA spiked in buffer and PrimeStore molecular transport medium (MTM). With ILC2, Vet-LIRN extended ILC1 by evaluating analytical sensitivity and specificity of the methods used by participating laboratories to detect 3 SARS-CoV2 variants (B.1; B.1.1.7 [Alpha]; B.1.351 [Beta]) at various copy levels. We analyzed 57 sets of results from 45 laboratories qualitatively and quantitatively according to the principles of ISO 16140-2:2016. More than 95% of analysts detected the SARS-CoV2 RNA in MTM at ≥500 copies for all 3 variants. In addition, for nucleocapsid markers N1 and N2, 81% and 92% of the analysts detected ≤20 copies in the assays, respectively. The analytical specificity of the evaluated methods was >99%. Participating laboratories were able to assess their current method performance, identify possible limitations, and recognize method strengths as part of a continuous learning environment to support the critical need for the reliable diagnosis of COVID-19 in potentially infected animals and humans.

Development of Non-Targeted Mass Spectrometry Method for Distinguishing Spelt and Wheat.

Food fraud, even when not in the news, is ubiquitous and demands the development of innovative strategies to combat it. A new non-targeted method (NTM) for distinguishing spelt and wheat is described, which aids in food fraud detection and authenticity testing. A highly resolved fingerprint in the form of spectra is obtained for several cultivars of spelt and wheat using liquid chromatography coupled high-resolution mass spectrometry (LC-HRMS). Convolutional neural network (CNN) models are built using a nested cross validation (NCV) approach by appropriately training them using a calibration set comprising duplicate measurements of eleven cultivars of wheat and spelt, each. The results reveal that the CNNs automatically learn patterns and representations to best discriminate tested samples into spelt or wheat. This is further investigated using an external validation set comprising artificially mixed spectra, samples for processed goods (spelt bread and flour), eleven untypical spelt, and six old wheat cultivars. These cultivars were not part of model building. We introduce a metric called the D score to quantitatively evaluate and compare the classification decisions. Our results demonstrate that NTMs based on NCV and CNNs trained using appropriately chosen spectral data can be reliable enough to be used on a wider range of cultivars and their mixes.

Interlaboratory comparison of SARS-CoV2 molecular detection assays in use by U.S. veterinary diagnostic laboratories.

The continued search for intermediate hosts and potential reservoirs for SARS-CoV2 makes it clear that animal surveillance is critical in outbreak response and prevention. Real-time RT-PCR assays for SARS-CoV2 detection can easily be adapted to different host species. U.S. veterinary diagnostic laboratories have used the CDC assays or other national reference laboratory methods to test animal samples. However, these methods have only been evaluated using internal validation protocols. To help the laboratories evaluate their SARS-CoV2 test methods, an interlaboratory comparison (ILC) was performed in collaboration with multiple organizations. Forty-four sets of 19 blind-coded RNA samples in Tris-EDTA (TE) buffer or PrimeStore transport medium were shipped to 42 laboratories. Results were analyzed according to the principles of the International Organization for Standardization (ISO) 16140-2:2016 standard. Qualitative assessment of PrimeStore samples revealed that, in approximately two-thirds of the laboratories, the limit of detection with a probability of 0.95 (LOD95) for detecting the RNA was ≤20 copies per PCR reaction, close to the theoretical LOD of 3 copies per reaction. This level of sensitivity is not expected in clinical samples because of additional factors, such as sample collection, transport, and extraction of RNA from the clinical matrix. Quantitative assessment of Ct values indicated that reproducibility standard deviations for testing the RNA with assays reported as N1 were slightly lower than those for N2, and they were higher for the RNA in PrimeStore medium than those in TE buffer. Analyst experience and the use of either a singleplex or multiplex PCR also affected the quantitative ILC test results.

Assessing the equivalence of Vibrio parahaemolyticus MPN and PCR quantification methods in oyster samples: A seven-year study.

Data collected from FDA proficiency tests (PT) during 2012-2018 was used to evaluate the performance of most probable number (MPN) and polymerase chain reaction (PCR) methods used to enumerate Vibrio parahaemolyticus in oyster samples. The primary aim was to establish whether the MPN and PCR methods can be considered equivalent. The following criterion for equivalence was applied: the absolute value of mean bias and between-sample standard deviation must both be less than 0.1 (log10). Final calculations showed mean bias and between-sample standard deviation (SD) were 0.031 and 0.117 (log10) respectively. The between-sample SD criterion was slightly relaxed because with close to 700 results, the data set was large and overall mean bias was low. It was concluded that the two methods can be considered equivalent. The use of PT data for the assessment of method rather than laboratory performance is a secondary topic addressed in this paper. Important requirements for this use of PT data include availability of sufficient results for both methods and use of real food matrices. Ultimately, the results presented here provide an example of how PT data can be used to monitor method performance across many laboratories and samples as well as to assess method equivalence.

A mathematical approach for the analysis of data obtained from the monitoring of biocides leached from treated materials exposed to outdoor conditions.

Leaching processes are responsible for the release of biocides from treated materials into the environment. Adequate modeling of emission processes is required in order to predict emission values in the framework of the risk assessment of biocidal products intended for long-term service life. Regression approaches have been applied to data obtained from the long-term monitoring of biocide emissions in experiments involving semi-field conditions. Due to the complex interaction of different underlying mechanisms such as water and biocide diffusion and desorption, however, these attempts have proven to be of limited usefulness - at least, for the available biocide emission data. It seems that the behavior of the biocide emission curve depends to a considerable extent on whichever underlying mechanism is slowest at a given point in time, thus limiting the amount of biocide available for release. Building on results obtained in the past few years, the authors propose a criterion for determining which mechanism controls the leaching process at a given point in time based on the slope of the log-log emission curve. In addition, a first-order approximation of this slope value is presented which displays advantages both in terms of computability and interpretability. Finally, an algorithm for the determination of breakpoints in the slope of the log-log emission curve is presented for the demarcation of phases within which one mechanism acts as a limiting factor.

Diagnostic Performance of Risk of Ovarian Malignancy Algorithm Against CA125 and HE4 in Connection With Ovarian Cancer: A Meta-analysis.

OBJECTIVES: The aim of this study was to determine whether the Risk of Ovarian Malignancy Algorithm (ROMA) is more accurate than the human epididymis 4 (HE4) or carbohydrate antigen 125 (CA125) biomarkers with respect to the differential diagnosis of women with a pelvic mass. The secondary objective is to assess the performance of ROMA in early-stage ovarian cancer (OC) and late-stage OC, as well as premenopausal and postmenopausal patient populations. METHODS/MATERIALS: The PubMed and Google Scholar databases were searched for relevant clinical studies. Eligibility criteria included comparison of ROMA with both HE4 and CA125 levels in OC (unspecified, epithelial, and borderline ovarian tumors), use of only validated ROMA assays, presentation of area under the curve and sensitivity/specificity data, and results from early-stage OC, late-stage OC and premenopausal and postmenopausal women. Area under the curve (AUC), sensitivity/specificity, and the diagnostic odds ratio (DOR) results were summarized. RESULTS: Five studies were selected comprising 1975 patients (premenopausal, n = 1033; postmenopausal, n = 925; benign, n = 1387; early stage, n = 192; and late stage, n = 313). On the basis of the AUC (95% confidence interval) data for all patients, ROMA (0.921 [0.855-0.960]) had a numerically greater diagnostic performance than CA125 (0.883 [0.771-0.950]) and HE4 (0.899 [0.835-0.943]). This was also observed in each of the subgroup populations, in particular, the postmenopausal patients and patients with early OC. The sensitivity and specificity (95% confidence interval) results showed ROMA (sensitivity, 0.873 [0.752-0.940]; specificity, 0.855 [0.719-0.932]) to be numerically superior to CA125 (sensitivity, 0.796 [0.663-0.885]; specificity, 0.825 [0.662-0.919]) and HE4 (sensitivity, 0.817 [0.683-0.902]; specificity, 0.851 [0.716-0.928]) in all patients and for the early- and late-stage OC subgroups. Finally, the ROMA log DOR results were better than HE4 and CA125 log DOR results especially for the early-stage patient group. CONCLUSIONS: The results presented support the use of ROMA to improve clinical decision making, most notably in patients with early OC.