scholarly journals Optimization on reagent-loading manner for modular clinical chemistry analyzer series: simulations and verifications

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mingyang Wang ◽  
Liang Ming
Keyword(s):  
Quality Control ◽  
Clinical Chemistry ◽  
Turnaround Time ◽  
Laboratory System ◽  
Related Factors ◽  
Loading Mode ◽  
Mode Pattern ◽  
On Line ◽  
And Performance ◽  
The Cost

AbstractObjectivesThe pre- and post-analytical processes have been discussed both in total laboratory system (TLA) and modular automation (MA). The analytical process, especially reagent-related factors influences on the integrated clinical chemistry analyzer, demonstrates a significant effect on clinical chemistry analyzer. Modular analyzer reagent-loading mode influences two mainly factors, testing turnaround time (tTAT) and the cost. Furthermore, how to definite the different reagent loading manners and verify the best reagent loading manner is big challenge.MethodsWe focus on tTAT, and study how the reagent-related factors effect TAT by simulations and verifications. Parameters were simulated by cobas 8000 workflow simulator for reagent-loading manner with at least three positions (Pattern 1), the module-parallel reagent-loading manner (Pattern 2) and the single-position loading mode (Pattern 3).ResultstTAT, reagent on-line time, quality control (QC) cost and performance verification times all declined by 43%. Tuesday effect solved the repetitive problem for verification. Pattern 2 shows optimal performance in Tuesday effect-based verification.ConclusionsThe optimization of reagent-loading manner saved much workforce, and reduced the QC cost.

Optimization on reagent-loading manner for modular clinical chemistry analyzer series: simulations and verifications

2020 ◽  
Author(s):  
Mingyang Wang ◽  
Liang Ming
Keyword(s):  
Quality Control ◽  
Clinical Chemistry ◽  
Turnaround Time ◽  
Internal Quality ◽  
Laboratory System ◽  
Control Cost ◽  
Related Factors ◽  
Health Care Intervention ◽  
Loading Mode ◽  
And Performance

Abstract Background: The analytical process, especially reagent-related factors, demonstrates a significant effect on clinical chemistry analyzer. Therefore, it should have our attention both in total laboratory system (TLA) and modular automation (MA). The reagent-loading mode of modular analyzer influences testing turnaround time (tTAT) and the cost.Methods: Parameters were simulated by cobas 8000 workflow simulator for reagent-loading manner with at least three positions (Pattern 1), the module-parallel reagent-loading manner (Pattern 2) and the single-position loading mode (Pattern 3). Then, we chose Pattern 2 for Tuesday effect-based optimization verification.Results: tTAT, reagent on-line time, quality control cost and performance verification times all declined by 43%. Tuesday effect solved the repetitive problem for verification. With comprehensive comparison between Pattern 1, Pattern 2 and Pattern 3, Pattern 2 shows optimal performance in subsequent verification.Conclusions: The optimization of reagent-loading manner saved much workforce, and reduced the quality control cost and internal quality assessment consumption.Trial registration: This article did not report the results of a health care intervention on human participants. Trial registration was not needed here.

Enzymatic Kinetic Rate and End-Point Analyses of Substrate, by Use of a GeMSAEC Fast Analyzer

1972 ◽  
Vol 18 (8) ◽  
pp. 829-840 ◽  
Author(s):  
T O Tiffany ◽  
J M Jansen ◽  
C A Burtis ◽  
J B Overton ◽  
C D Scott
Keyword(s):  
Clinical Chemistry ◽  
Fixed Time ◽  
Small Computer ◽  
Time Analysis ◽  
Kinetic Rate ◽  
End Point ◽  
Unique Method ◽  
On Line ◽  
The Cost ◽  
Multiple Samples

Abstract Enzymatic substrate analysis is an attractive means of analysis in clinical chemistry because of its sensitivity and specificity. The GeMSAEC Fast Analyzer, in conjunction with a small computer, provides a means of performing routine enzymatic substrate analysis and offers the following advantages: (a) selectivity of approaches to enzymatic analysis, i.e., end-point or kinetic; (b) essentially parallel analyses of multiple samples, yielding a unique method for performing kinetic fixed-time analysis; (c) on-line data reduction, resulting in rapid calculation and output of results and the minimization of data handling errors; and (d) a small reagent volume per test (400 µl), which reduces the cost of analysis. The analysis of substrate with enzymatic end-point and kinetic procedures is examined by use of a computer-interfaced Fast Analyzer. Computer programs were written to facilitate this study. Glucose (hexokinase/GPD), urea (urease/GMD), and uric acid (uricase) have been used as examples in evaluating both end-point and kinetic analyses. The advantages and limitations of each type of analysis are presented, with the emphasis being placed on enzymatic substrate analysis and means by which the computer-interfaced Fast Analyzer can facilitate both end-point and kinetic analyses.

On-Line Quality Control in Clinical Chemistry

1982 ◽  
pp. 124-128
Author(s):  
Roy Baker ◽  
John Upham ◽  
Joseph Schroeder ◽  
W. B. Stewart
Keyword(s):  
Quality Control ◽  
Clinical Chemistry ◽  
On Line

Time series modeling for quality control in clinical chemistry.

1988 ◽  
Vol 34 (7) ◽  
pp. 1396-1406 ◽  
Author(s):  
L C Alwan ◽  
M G Bissell
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Control Charts ◽  
Current Practice ◽  
Clinical Chemistry ◽  
Statistical Quality Control ◽  
New Approach ◽  
Basic Assumptions ◽  
Control Rules ◽  
And Performance

Abstract Autocorrelation of clinical chemistry quality-control (Q/C) measurements causes one of the basic assumptions underlying the use of Levey-Jennings control charts to be violated and performance to be degraded. This is the requirement that the observations be statistically independent. We present a proposal for a new approach to statistical quality control that removes this difficulty. We propose to replace the current single control chart of raw Q/C data with two charts: (a) a common cause chart, representing a Box-Jenkins ARIMA time-series model of any underlying persisting nonrandomness in the process, and (b) a special cause chart of the residuals from the above model, which, being free of such persisting nonrandomness, fulfills the criteria for use of the standard Levey-Jennings plotting format and standard control rules. We provide a comparison of the performance of our proposed approach with that of current practice.

Return on Invesment (ROI) of Software Product: A Systematic Literature Review

2015 ◽  
Vol 6 (1) ◽  
pp. 50-57
Author(s):  
Rizqa Raaiqa Bintana ◽  
Putri Aisyiyah Rakhma Devi ◽  
Umi Laili Yuhana
Keyword(s):  
Return On Investment ◽  
Assessment Criteria ◽  
Software Product ◽  
And Performance ◽  
Rational Consideration ◽  
Actual Return ◽  
The Cost ◽  
Investment Models ◽  
The Impact

The quality of the software can be measured by its return on investment. Factors which may affect the return on investment (ROI) is the tangible factors (such as the cost) dan intangible factors (such as the impact of software to the users or stakeholder). The factor of the software itself are assessed through reviewing, testing, process audit, and performance of software. This paper discusses the consideration of return on investment (ROI) assessment criteria derived from the software and its users. These criteria indicate that the approach may support a rational consideration of all relevant criteria when evaluating software, and shows examples of actual return on investment models. Conducted an analysis of the assessment criteria that affect the return on investment if these criteria have a disproportionate effort that resulted in a return on investment of a software decreased. Index Terms - Assessment criteria, Quality assurance, Return on Investment, Software product

scholarly journals The influence of chemical parameters on the in-situ metal carbonyl complex formation studied with the fast on-line reaction apparatus (FORA)

2021 ◽  
Vol 109 (4) ◽  
pp. 243-260 ◽  
Author(s):  
Yves Wittwer ◽  
Robert Eichler ◽  
Dominik Herrmann ◽  
Andreas Türler
Keyword(s):  
Metal Carbonyl ◽  
Ambient Air ◽  
Single Atom ◽  
Carbonyl Complex ◽  
Carbonyl Complexes ◽  
On Line ◽  
Carrier Gases ◽  
And Performance ◽  
Atom Chemistry

Abstract A new setup named Fast On-line Reaction Apparatus (FORA) is presented which allows for the efficient investigation and optimization of metal carbonyl complex (MCC) formation reactions under various reaction conditions. The setup contains a 252Cf-source producing short-lived Mo, Tc, Ru and Rh isotopes at a rate of a few atoms per second by its 3% spontaneous fission decay branch. Those atoms are transformed within FORA in-situ into volatile metal carbonyl complexes (MCCs) by using CO-containing carrier gases. Here, the design, operation and performance of FORA is discussed, revealing it as a suitable setup for performing single-atom chemistry studies. The influence of various gas-additives, such as CO2, CH4, H2, Ar, O2, H2O and ambient air, on the formation and transport of MCCs was investigated. O2, H2O and air were found to harm the formation and transport of MCCs in FORA, with H2O being the most severe. An exception is Tc, for which about 130 ppmv of H2O caused an increased production and transport of volatile compounds. The other gas-additives were not influencing the formation and transport efficiency of MCCs. Using an older setup called Miss Piggy based on a similar working principle as FORA, it was additionally investigated if gas-additives are mostly affecting the formation or only the transport stability of MCCs. It was found that mostly formation is impacted, as MCCs appear to be much less sensitive to reacting with gas-additives in comparison to the bare Mo, Tc, Ru and Rh atoms.

scholarly journals A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Wendell Jones ◽  
Binsheng Gong ◽  
Natalia Novoradovskaya ◽  
Dan Li ◽  
Rebecca Kusko ◽  
...  
Keyword(s):  
Quality Control ◽  
Allele Frequency ◽  
Liquid Biopsy ◽  
Limit Of Detection ◽  
Reference Sample ◽  
Digital Pcr ◽  
Detection Sensitivity ◽  
Analytical Accuracy ◽  
And Performance ◽  
Reference Samples

Abstract Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

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