scholarly journals Validation Study for VERIPRO® SARS-CoV-2 Env Assay for the Detection of SARS-CoV-2 from Stainless Steel Environmental Surface Swabs: Emergency Response Validation-AOAC—AOAC Performance Tested MethodSM 122001

2021 ◽  
Author(s):  
Adam Joelsson ◽  
Shannon Green ◽  
Nicholas Siciliano ◽  
Vikrant Dutta ◽  
Ron Johnson
Keyword(s):  
Stainless Steel ◽  
Emergency Response ◽  
Causative Agent ◽  
In Silico ◽  
Reference Method ◽  
In Silico Analysis ◽  
Accurate Method ◽  
Rt Pcr ◽  
Environmental Surface ◽  
Silico Analysis

Abstract Background The VERIPRO SARS-CoV-2 Env assay uses reverse transcriptase PCR (RT-PCR) to detect SARS-CoV-2, the causative agent of COVID-19, from stainless steel environmental sample swabs. Objective To validate the VERIPRO SARS-CoV-2 Env assay as part of the AOAC Research Institute’s Emergency Response Validation Performance Tested MethodSM program. Methods The VERIPRO SARS-CoV-2 Env assay was evaluated for specificity using in silico analysis of 15 764 SARS-CoV-2 sequences and 65 exclusivity organisms (both near neighbors and background organisms). The candidate method was evaluated in an unpaired study design for one environmental surface (stainless steel) and compared to the U.S. Centers for Disease Control and Prevention 2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR Diagnostic Panel, Instructions for Use (Revision 4, Effective 6/12/2020). Results Results of the in silico analysis demonstrated the specificity of the method in being able to detect target sequences and discriminate them from near neighbors. In the matrix study, the candidate method demonstrated statistically significant better recovery of the target analyte than the reference method. Conclusions The VERIPRO SARS-CoV-2 Env assay is a rapid and accurate method that can be utilized by food producers to detect the causative agent of COVID-19 on food contact surfaces. Highlights The VERIPRO SARS-CoV-2 Env assay can be performed without the need for an optional RNA purification step to detect SARS-CoV-2 from environmental surfaces.

scholarly journals Evaluation of the effects of SARS-CoV-2 genetic mutations on diagnostic RT-PCR assays

2021 ◽  
Author(s):  
Takeru Nakabayashi ◽  
Yuki Kawasaki ◽  
Koichiro Murashima ◽  
Kazuya Omi ◽  
Satoshi Yuhara
Keyword(s):  
In Silico ◽  
In Silico Analysis ◽  
Detection Sensitivity ◽  
Genetic Mutations ◽  
Rt Pcr ◽  
Mutant Strains ◽  
Binding Regions ◽  
Control And Prevention ◽  
Pcr Assays ◽  
Silico Analysis

AbstractSeveral mutant strains of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are emerging. Mismatch(es) in primer/probe binding regions would decrease the detection sensitivity of the PCR test, thereby affecting the results of clinical testing. In this study, we conducted an in silico survey on SARS-CoV-2 sequence variability within the binding regions of primer/probe published by the Japan National Institute of Infectious Diseases (NIID) and Centers for Disease Control and Prevention (CDC). In silico analysis revealed the presence of mutations in the primer/probe binding regions. We performed RT-PCR assays using synthetic RNAs containing the mutations and showed that some mutations significantly decreased the detection sensitivity of the RT-PCR assays.Our results highlight the importance of genomic monitoring of SARS-CoV-2 and evaluating the effects of mismatches on PCR testing sensitivity.

A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

2020 ◽  
Vol 47 (6) ◽  
pp. 398-408
Author(s):  
Sonam Tulsyan ◽  
Showket Hussain ◽  
Balraj Mittal ◽  
Sundeep Singh Saluja ◽  
Pranay Tanwar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gallbladder Carcinoma ◽  
In Silico ◽  
In Silico Analysis ◽  
Transcriptomic Profile ◽  
Silico Analysis

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

In-Silico Analysis of Chromone Containing Sulfonamide Derivatives as Human Carbonic Anhydrase Inhibitors

2013 ◽  
Vol 9 (4) ◽  
pp. 608-616 ◽  
Author(s):  
Zaheer Ul-Haq ◽  
Saman Usmani ◽  
Uzma Mahmood ◽  
Mariya al-Rashida ◽  
Ghulam Abbas
Keyword(s):  
Carbonic Anhydrase ◽  
In Silico ◽  
In Silico Analysis ◽  
Carbonic Anhydrase Inhibitors ◽  
Human Carbonic Anhydrase ◽  
Silico Analysis

In-Vitro and In-Silico Characterization of Xylose Reductase from Emericella nidulans

2019 ◽  
Vol 13 (2) ◽  
pp. 159-170 ◽  
Author(s):  
Vishal Ahuja ◽  
Aashima Sharma ◽  
Ranju Kumari Rathour ◽  
Vaishali Sharma ◽  
Nidhi Rana ◽  
...  
Keyword(s):  
Production Process ◽  
In Silico ◽  
Xylose Reductase ◽  
In Silico Analysis ◽  
Emericella Nidulans ◽  
Higher Temperature ◽  
In Silico Characterization ◽  
Silico Analysis

Background: Lignocellulosic residues generated by various anthropogenic activities can be a potential raw material for many commercial products such as biofuels, organic acids and nutraceuticals including xylitol. Xylitol is a low-calorie nutritive sweetener for diabetic patients. Microbial production of xylitol can be helpful in overcoming the drawbacks of traditional chemical production process and lowring cost of production. Objective: Designing efficient production process needs the characterization of required enzyme/s. Hence current work was focused on in-vitro and in-silico characterization of xylose reductase from Emericella nidulans. Methods: Xylose reductase from one of the hyper-producer isolates, Emericella nidulans Xlt-11 was used for in-vitro characterization. For in-silico characterization, XR sequence (Accession No: Q5BGA7) was used. Results: Xylose reductase from various microorganisms has been studied but the quest for better enzymes, their stability at higher temperature and pH still continues. Xylose reductase from Emericella nidulans Xlt-11 was found NADH dependent and utilizes xylose as its sole substrate for xylitol production. In comparison to whole cells, enzyme exhibited higher enzyme activity at lower cofactor concentration and could tolerate higher substrate concentration. Thermal deactivation profile showed that whole cell catalysts were more stable than enzyme at higher temperature. In-silico analysis of XR sequence from Emericella nidulans (Accession No: Q5BGA7) suggested that the structure was dominated by random coiling. Enzyme sequences have conserved active site with net negative charge and PI value in acidic pH range. Conclusion: Current investigation supported the enzyme’s specific application i.e. bioconversion of xylose to xylitol due to its higher selectivity. In-silico analysis may provide significant structural and physiological information for modifications and improved stability.

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