Laboratory protocol for the digital multiplexed gene expression analysis of nasopharyngeal swab samples using the NanoString nCounter system.

This paper describes a laboratory protocol to perform the NanoString nCounter Gene Expression Assay from nasopharyngeal swab samples.  It is urgently necessary to identify factors related to severe symptoms of respiratory infectious diseases, such as COVID-19, in order to assess the possibility of establishing preventive or preliminary therapeutic measures and to plan the services to be provided on hospital admission. At present, the samples recommended for microbiological diagnosis are those taken from the upper and/or the lower respiratory tract.  The NanoString nCounter Gene Expression Assay is a method based on the direct digital detection of mRNA molecules by means of target-specific, colour-coded probe pairs, without the need for mRNA conversion to cDNA by reverse transcription or the amplification of the resulting cDNA by PCR. This platform includes advanced analysis tools that reduce the need for bioinformatics support and also offers reliable sensitivity, reproducibility, technical robustness and utility for clinical application, even in RNA samples of low RNA quality or concentration, such as paraffin-embedded samples. Although the protocols for the analysis of blood or formalin-fixed paraffin-embedded samples are provided by the manufacturer, no corresponding protocol for the analysis of nasopharyngeal swab samples has yet been established. Therefore, the approach we describe could be adopted to determine the expression of target genes in samples obtained from nasopharyngeal swabs using the nCOUNTER technology.

Use of quality indicators to compare point-of-care testing errors in a neonatal unit and errors in a STAT central laboratory.

BACKGROUND: Point-of-care testing (POCT), like other laboratory tests, can be affected by errors throughout the total testing process. To evaluate quality error rates, the use of quality indicators (QIs) is recommended; however, little information is available on the quality error rate associated with POCT. The objective of this study was to investigate quality error rates related to POCT and compare them with central laboratory (CL) testing. METHODS: We studied standardized QIs for POCT in comparison to CL testing. We compared error rates related to requests, collection, and handling of samples and results from external quality assessment program (EQAP) and internal quality control (IQC). RESULTS: The highest difference between POCT and CL testing was observed for QI related to patient identification, 45.3% vs. 0.02% (p<0.001). Regarding specimen collection and handling, the QI related to samples without results was also higher in POCT than in CL testing, 15.8% vs. 3.3% (p<0.001). For the QI related to insufficient sample volume, we obtained 2.9% vs. 0.9% (p=0.27). Unlike QIs for the preanalytical phase, QIs for the analytical phase had better results in POCT than CL testing. We obtained 8.3% vs. 16.6% (p=0.13) for QI related to unacceptable results in EQAP and 0.8% vs. 22.5% (p<0.001) for QI related to unacceptable results in IQC. CONCLUSIONS: Our results show that the preanalytical phase remains the main problem in POCT like in CL testing and that monitoring of quality indicators is a very valuable tool in reducing errors in POCT.

DB4US: A Decision Support System for Laboratory Information Management.

BACKGROUND: Until recently, laboratory automation has focused primarily on improving hardware. Future advances are concentrated on intelligent software since laboratories performing clinical diagnostic testing require improved information systems to address their data processing needs. In this paper, we propose DB4US, an application that automates information related to laboratory quality indicators information. Currently, there is a lack of ready-to-use management quality measures. This application addresses this deficiency through the extraction, consolidation, statistical analysis, and visualization of data related to the use of demographics, reagents, and turn-around times. The design and implementation issues, as well as the technologies used for the implementation of this system, are discussed in this paper. OBJECTIVE: To develop a general methodology that integrates the computation of ready-to-use management quality measures and a dashboard to easily analyze the overall performance of a laboratory, as well as automatically detect anomalies or errors. The novelty of our approach lies in the application of integrated web-based dashboards as an information management system in hospital laboratories. METHODS: We propose a new methodology for laboratory information management based on the extraction, consolidation, statistical analysis, and visualization of data related to demographics, reagents, and turn-around times, offering a dashboard-like user web interface to the laboratory manager. The methodology comprises a unified data warehouse that stores and consolidates multidimensional data from different data sources. The methodology is illustrated through the implementation and validation of DB4US, a novel web application based on this methodology that constructs an interface to obtain ready-to-use indicators, and offers the possibility to drill down from high-level metrics to more detailed summaries. The offered indicators are calculated beforehand so that they are ready to use when the user needs them. The design is based on a set of different parallel processes to precalculate indicators. The application displays information related to tests, requests, samples, and turn-around times. The dashboard is designed to show the set of indicators on a single screen. RESULTS: DB4US was deployed for the first time in the Hospital Costa del Sol in 2008. In our evaluation we show the positive impact of this methodology for laboratory professionals, since the use of our application has reduced the time needed for the elaboration of the different statistical indicators and has also provided information that has been used to optimize the usage of laboratory resources by the discovery of anomalies in the indicators. DB4US users benefit from Internet-based communication of results, since this information is available from any computer without having to install any additional software. CONCLUSIONS: The proposed methodology and the accompanying web application, DB4US, automates the processing of information related to laboratory quality indicators and offers a novel approach for managing laboratory-related information, benefiting from an Internet-based communication mechanism. The application of this methodology has been shown to improve the usage of time, as well as other laboratory resources.