scholarly journals Noisy Pooled PCR for Virus Testing

2020 ◽  
Author(s):  
Junan Zhu ◽  
Kristina Rivera ◽  
Dror Baron
Keyword(s):  
Message Passing ◽  
Communication Channel ◽  
Group Testing ◽  
Diagnostic Tools ◽  
Rt Pcr ◽  
Message Passing Algorithm ◽  
Large Populations ◽  
Testing Algorithms ◽  
Virus Testing ◽  
False Positives And Negatives

AbstractFast testing can help mitigate the coronavirus disease 2019 (COVID-19) pandemic. Despite their accuracy for single sample analysis, infectious diseases diagnostic tools, like RT-PCR, require substantial resources to test large populations. We develop a scalable approach for determining the viral status of pooled patient samples. Our approach converts group testing to a linear inverse problem, where false positives and negatives are interpreted as generated by a noisy communication channel, and a message passing algorithm estimates the illness status of patients. Numerical results reveal that our approach estimates patient illness using fewer pooled measurements than existing noisy group testing algorithms. Our approach can easily be extended to various applications, including where false negatives must be minimized. Finally, in a Utopian world we would have collaborated with RT-PCR experts; it is difficult to form such connections during a pandemic. We welcome new collaborators to reach out and help improve this work!

scholarly journals Noisy Pooled PCR for Virus Testing

2020 ◽  
Author(s):  
Junan Zhu ◽  
Kristina Rivera ◽  
Dror Baron
Keyword(s):  
Message Passing ◽  
Communication Channel ◽  
Group Testing ◽  
Diagnostic Tools ◽  
Rt Pcr ◽  
Message Passing Algorithm ◽  
Large Populations ◽  
Testing Algorithms ◽  
Virus Testing ◽  
False Positives And Negatives

AbstractFast testing can help mitigate the coronavirus disease 2019 (COVID-19) pandemic. Despite their accuracy for single sample analysis, infectious diseases diagnostic tools, like RT-PCR, require substantial resources to test large populations. We develop a scalable approach for determining the viral status of pooled patient samples. Our approach converts group testing to a linear inverse problem, where false positives and negatives are interpreted as generated by a noisy communication channel, and a message passing algorithm estimates the illness status of patients. Numerical results reveal that our approach estimates patient illness using fewer pooled measurements than existing noisy group testing algorithms. Our approach can easily be extended to various applications, including where false negatives must be minimized. Finally, in a Utopian world we would have collaborated with RT-PCR experts; it is difficult to form such connections during a pandemic. We welcome new collaborators to reach out and help improve this work!

scholarly journals FGFR3 overexpression is a useful detection tool for FGFR3 fusions and sequence variations in glioma

2020 ◽  
Author(s):  
Jens Schittenhelm ◽  
Lukas Ziegler ◽  
Jan Sperveslage ◽  
Michel Mittelbronn ◽  
David Capper ◽  
...  
Keyword(s):  
Coiled Coil ◽  
Growth Factor Receptor ◽  
Gene Mutations ◽  
Diagnostic Tools ◽  
Wild Type ◽  
Rt Pcr ◽  
Fgfr3 Gene ◽  
Detection Tool ◽  
Sequence Variations ◽  
Gene Panel Sequencing

Abstract Background Fibroblast growth factor receptor (FGFR) inhibitors are currently used in clinical development. A subset of glioblastomas carries gene fusion of FGFR3 and transforming acidic coiled-coil protein 3. The prevalence of other FGFR3 alterations in glioma is currently unclear. Methods We performed RT-PCR in 101 glioblastoma samples to detect FGFR3-TACC3 fusions (“RT-PCR cohort”) and correlated results with FGFR3 immunohistochemistry (IHC). Further, we applied FGFR3 IHC in 552 tissue microarray glioma samples (“TMA cohort”) and validated these results in two external cohorts with 319 patients. Gene panel sequencing was carried out in 88 samples (“NGS cohort”) to identify other possible FGFR3 alterations. Molecular modeling was performed on newly detected mutations. Results In the “RT-PCR cohort,” we identified FGFR3-TACC3 fusions in 2/101 glioblastomas. Positive IHC staining was observed in 73/1024 tumor samples of which 10 were strongly positive. In the “NGS cohort,” we identified FGFR3 fusions in 9/88 cases, FGFR3 amplification in 2/88 cases, and FGFR3 gene mutations in 7/88 cases in targeted sequencing. All FGFR3 fusions and amplifications and a novel FGFR3 K649R missense mutation were associated with FGFR3 overexpression (sensitivity and specificity of 93% and 95%, respectively, at cutoff IHC score > 7). Modeling of these data indicated that Tyr647, a residue phosphorylated as a part of FGFR3 activation, is affected by the K649R mutation. Conclusions FGFR3 IHC is a useful screening tool for the detection of FGFR3 alterations and could be included in the workflow for isocitrate dehydrogenase (IDH) wild-type glioma diagnostics. Samples with positive FGFR3 staining could then be selected for NGS-based diagnostic tools.

scholarly journals Cooperative Localization for Mobile Networks: A Distributed Belief Propagation–Mean Field Message Passing Algorithm

2016 ◽  
Vol 23 (6) ◽  
pp. 828-832 ◽  
Author(s):  
Burak Cakmak ◽  
Daniel N. Urup ◽  
Florian Meyer ◽  
Troels Pedersen ◽  
Bernard H. Fleury ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Message Passing ◽  
Belief Propagation ◽  
Mean Field ◽  
Cooperative Localization ◽  
Message Passing Algorithm

scholarly journals Early detection of superspreaders by mass group pool testing can mitigate COVID-19 pandemic

2020 ◽  
Author(s):  
Maxim B Gongalsky
Keyword(s):  
New York ◽  
Early Detection ◽  
Group Testing ◽  
Scale Model ◽  
Rt Pcr ◽  
Infected People ◽  
Group Matrix ◽  
New Strategy ◽  
Mass Group ◽  
Log Normal

Background Most of epidemiological models applied for COVID-19 do not consider heterogeneity in infectiousness and impact of superspreaders, despite the broad viral loading distributions amongst COVID-19 positive people (1-1 000 000 per mL). Also, mass group testing is not used regardless to existing shortage of tests. I propose new strategy for early detection of superspreaders with reasonable number of RT-PCR tests, which can dramatically mitigate development COVID-19 pandemic and even turn it endemic. Methods I used stochastic social-epidemiological SEIAR model, where S-suspected, E-exposed, I-infectious, A-admitted (confirmed COVID-19 positive, who are admitted to hospital or completely isolated), R-recovered. The model was applied to real COVID-19 dynamics in London, Moscow and New York City. Findings Viral loading data measured by RT-PCR were fitted by broad log-normal distribution, which governed high importance of superspreaders. The proposed full scale model of a metropolis shows that top 10% spreaders (100+ higher viral loading than median infector) transmit 45% of new cases. Rapid isolation of superspreaders leads to 4-8 fold mitigation of pandemic depending on applied quarantine strength and amount of currently infected people. High viral loading allows efficient group matrix pool testing of population focused on detection of the superspreaders requiring remarkably small amount of tests. Interpretation The model and new testing strategy may prevent thousand or millions COVID-19 deaths requiring just about 5000 daily RT-PCR test for big 12 million city such as Moscow. Though applied to COVID-19 pandemic the results are universal and can be used for other infectious heterogenous epidemics. Funding No funding

scholarly journals False Negative Mitigation in Group Testing for COVID-19 Screening

2021 ◽  
Vol 8 ◽  
Author(s):  
Amir Reza Alizad Rahvar ◽  
Safar Vafadar ◽  
Mehdi Totonchi ◽  
Mehdi Sadeghi
Keyword(s):  
Viral Load ◽  
False Negative ◽  
Group Testing ◽  
False Negative Rate ◽  
Screening Strategy ◽  
Web Interface ◽  
Rt Pcr ◽  
Promising Candidate ◽  
Testing Method ◽  
High False Negative Rate

After lifting the COVID-19 lockdown restrictions and opening businesses, screening is essential to prevent the spread of the virus. Group testing could be a promising candidate for screening to save time and resources. However, due to the high false-negative rate (FNR) of the RT-PCR diagnostic test, we should be cautious about using group testing because a group's false-negative result identifies all the individuals in a group as uninfected. Repeating the test is the best solution to reduce the FNR, and repeats should be integrated with the group-testing method to increase the sensitivity of the test. The simplest way is to replicate the test twice for each group (the 2Rgt method). In this paper, we present a new method for group testing (the groupMix method), which integrates two repeats in the test. Then we introduce the 2-stage sequential version of both the groupMix and the 2Rgt methods. We compare these methods analytically regarding the sensitivity and the average number of tests. The tradeoff between the sensitivity and the average number of tests should be considered when choosing the best method for the screening strategy. We applied the groupMix method to screening 263 people and identified 2 infected individuals by performing 98 tests. This method achieved a 63% saving in the number of tests compared to individual testing. Our experimental results show that in COVID-19 screening, the viral load can be low, and the group size should not be more than 6; otherwise, the FNR increases significantly. A web interface of the groupMix method is publicly available for laboratories to implement this method.

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