quality control
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2022 ◽  
Vol 8 (4) ◽  
pp. 253-259
Juby Sara Koshy ◽  
Afsheen Raza

The clinical laboratory in today’s world is a rapidly evolving field which faces a constant pressure to produce quick and reliable results. Sigma metric is a new tool which helps to reduce process variability, quantitate the approximate number of analytical errors, and evaluate and guide for better quality control (QC) practices.To analyze sigma metrics of 16 biochemistry analytes using ERBA XL 200 Biochemistry analyzer, interpret parameter performance, compare analyzer performance with other Middle East studies and modify existing QC practices.This study was undertaken at a clinical laboratory for a period of 12 months from January to December 2020 for the following analytes: albumin (ALB), alanine amino transferase (SGPT), aspartate amino transferase (SGOT), alkaline phosphatase (ALKP), bilirubin total (BIL T), bilirubin direct (BIL D), calcium (CAL), cholesterol (CHOL), creatinine (CREAT), gamma glutamyl transferase (GGT), glucose (GLUC), high density lipoprotein (HDL), triglyceride (TG), total protein (PROT), uric acid (UA) and urea. The Coefficient of variance (CV%) and Bias % were calculated from internal quality control (IQC) and external quality assurance scheme (EQAS) records respectively. Total allowable error (TEa) was obtained using guidelines Clinical Laboratories Improvement Act guidelines (CLIA). Sigma metrics was calculated using CV%, Bias% and TEa for the above parameters. It was found that 5 analytes in level 1 and 8 analytes in level 2 had greater than 6 sigma performance indicating world class quality. Cholesterol, glucose (level 1 and 2) and creatinine level 1 showed >4 sigma performance i.e acceptable performance. Urea (both levels) and GGT (level 1) showed <3 sigma and were therefore identified as the problem analytes. Sigma metrics helps to assess analytic methodologies and can serve as an important self assessment tool for quality assurance in the clinical laboratory. Sigma metric evaluation in this study helped to evaluate the quality of several analytes and also categorize them from high performing to problematic analytes, indicating the utility of this tool. In conclusion, parameters showing lesser than 3 sigma need strict monitoring and modification of quality control procedure with change in method if necessary.

Shao Hua Lu ◽  
Ming Cai Zhang ◽  
Hong Lin Zhai ◽  
Ke Xin Bi ◽  
Bing Qiang Zhao

JOM ◽  
2022 ◽  
Daniel Rodrigues ◽  
Donald Picard ◽  
Carl Duchesne ◽  
Julien Lauzon-Gauthier

RNA ◽  
2022 ◽  
pp. rna.078994.121
Haina Huang ◽  
Melissa D Parker ◽  
Katrin Karbstein

Ribosome assembly is an intricate process, which in eukaryotes is promoted by a large machinery comprised of over 200 assembly factors (AF) that enable the modification, folding, and processing of the ribosomal RNA (rRNA) and the binding of the 79 ribosomal proteins. While some early assembly steps occur via parallel pathways, the process overall is highly hierarchical, which allows for the integration of maturation steps with quality control processes that ensure only fully and correctly assembled subunits are released into the translating pool. How exactly this hierarchy is established, in particular given that there are many instances of RNA substrate “handover” from one highly related AF to another remains to be determined. Here we have investigated the role of Tsr3, which installs a universally conserved modification in the P-site of the small ribosomal subunit late in assembly. Our data demonstrate that Tsr3 separates the activities of the Rio kinases, Rio2 and Rio1, with whom it shares a binding site. By binding after Rio2 dissociation, Tsr3 prevents rebinding of Rio2, promoting forward assembly. After rRNA modification is complete, Tsr3 dissociates, thereby allowing for recruitment of Rio1. Inactive Tsr3 blocks Rio1, which can be rescued using mutants that bypass the requirement for Rio1 activity. Finally, yeast strains lacking Tsr3 randomize the binding of the two kinases, leading to the release of immature ribosomes into the translating pool. These data demonstrate a role for Tsr3 and its modification activity in establishing a hierarchy for the function of the Rio kinases.

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