ABSTRACT
Background: Since the beginning of the COVID-19 pandemic veterinary diagnostic laboratories have tested diagnostic samples for SARS-CoV-2 not only in animals, but in over five 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 two prior exercises to assess whether veterinary diagnostic laboratories can detect Delta and Omicron variants spiked in canine nasal matrix or viral transport medium. Methods: Inactivated Delta variant at levels of 25 to 1,000 copies per 50 microliters of nasal matrix were prepared for participants by the ILC organizer, an independent laboratory, for blinded analysis. Omicron variant at 1,000 copies per 50 microliters of transport medium was also included. Feline infectious peritonitis virus (FIPV) RNA was used as a confounder for specificity assessment. A total of 14 test samples were prepared for each participant. Participants used their routine diagnostic procedures for RNA extraction and real-time RT-PCR. Results were analyzed according to International Organization for Standardization (ISO) 16140 - 2:2016. Results: The overall results showed 93% detection for Delta and 97% for Omicron at 1,000 copies per 50 microliters (22-200 copies per reaction). The overall specificity was 97% for blank samples and 100% for blank samples with FIPV. No differences in Ct values were significant for samples with the same virus levels between N1 and N2 markers, nor between the two 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.
Subject(s)
COVID-19 , PeritonitisABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic presents a continued public health challenge across the world. Veterinary diagnostic laboratories in the U.S. use real-time reverse transcriptase PCR (RT-PCR) for animal testing, and many are certified for testing human samples, so ensuring laboratories have sensitive and specific SARS-CoV-2 testing methods is a critical component of the pandemic response. In 2020, the FDA Veterinary Laboratory Investigation and Response Network (Vet-LIRN) led the first round of an Inter-Laboratory Comparison (ILC) Exercise to help laboratories evaluate their existing real-time RT-PCR methods for detecting SARS-CoV-2. The ILC1 results indicated that all participating laboratories were able to detect the viral RNA spiked in buffer and PrimeStore molecular transport medium (MTM). The current ILC (ILC2) aimed to extend ILC1 by evaluating analytical sensitivity and specificity of the methods used by participating laboratories to detect three SARS-CoV-2 variants (B.1, B.1.1.7 (Alpha) and B.1.351 (Beta)). ILC2 samples were prepared with RNA at levels between 10 to 10,000 copies per 50 μL MTM. Fifty-seven sets of results from 45 laboratories were qualitatively and quantitatively analyzed according to the principles of ISO 16140-2:2016. The results showed that over 95% of analysts detected the SARS-CoV-2 RNA in MTM at 500 copies or higher for all three variants. In addition, 81% and 92% of the analysts achieved a Level of Detection (LOD95 eff. vol. ) below 20 copies in the assays with nucleocapsid markers N1 and N2, respectively. The analytical specificity of the evaluated methods was over 99%. The study allowed participating laboratories 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 reliable diagnosis of COVID-19 in potentially infected animals and humans.
Subject(s)
COVID-19ABSTRACT
Despite known adverse effects of hydroxychloroquine (HCQ) and azithromycin (AZM) on cardiac function, HCQ and AZM have been used as combination therapy in the treatment of COVID-19 patients. Recent clinical data indicate higher complication rates with HCQ/AZM combination treatment in comparison to monotherapy. Here, we used human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to systematically investigate the effects of HCQ and AZM individually and in combination. The clinically observed QT prolongation caused by treatment with HCQ could be recapitulated in iPSC-CMs based on prolonged field potential duration (FPDc). Interestingly, HCQ-induced FPDc prolongation was strongly enhanced by combined treatment with AZM, although AZM alone slightly shortened FPDc in iPSC-CMs. Furthermore, combined treatment with AZM and HCQ leads to higher cardiotoxicity, more severe structural disarrangement, and more pronounced contractile and electrophysiological dysfunctions, compared to respective mono-treatments. First mechanistic insights underlying the synergistic effects of AZM and HCQ on iPSC-CM functionality are provided based on increased Cx43- and Nav1.5-protein levels. Taken together, our results highlight that combined treatment with HCQ and AZM strongly enhances the adverse effects on cardiomyocytes, providing mechanistic evidence for the high mortality in patients receiving HCQ/AZM combination treatment.
Subject(s)
COVID-19ABSTRACT
As the number of people affected by COVID-19 disease caused by the novel coronavirus SARS-CoV-2 ebbs and flows in different national and sub-national regions across the world, it is evident that our lifestyle and socio-economic trajectories will have to be adapted and adjusted to the changing scenarios. Novel forecasting tools and frameworks provide an arguable advantage to facilitate this adapting and adjusting process, by promoting efficient resource management at individual and institutional levels. Based on deterministic compartment models we propose an empirical top-down modeling approach to provide epidemic forecasts and risk calculations for (local) outbreaks. We use neural networks to develop leading indicators based on available data for different regions. These indicators are not only used to assess the risk of a (new) outbreak or to determine the effectiveness of a measure at an early stage, but also in parametric models to determine an effective forecast, along with the associated uncertainty. Based on initial results, we show the performance of such an approach and its robustness against inherent disturbances in epidemiological surveillance data. We foresee such a statistical framework to drive web-based automatic platforms to democratize the dissemination of prognosis results.