scholarly journals Sample Collection and Storage

Author(s):  
Bradley J. Till ◽  
Joanna Jankowicz-Cieslak ◽  
Owen A. Huynh ◽  
Mayada M. Beshir ◽  
Robert G. Laport ◽  
...  
1999 ◽  
Vol 45 (5) ◽  
pp. 692-694 ◽  
Author(s):  
Estrid VS Høgdall ◽  
Claus K Høgdall ◽  
Susanne K Kjaer ◽  
Fengji Xu ◽  
Yinhua Yu ◽  
...  

Cryobiology ◽  
2013 ◽  
Vol 67 (3) ◽  
pp. 418-419
Author(s):  
Sushmita Mimi Roy ◽  
Geuncheol Gil ◽  
Bich Nguyen ◽  
Daniel Lopez-Ferrer ◽  
Xiaolei Xie ◽  
...  

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Muriel De Bock ◽  
Dominique de Seny ◽  
Marie-Alice Meuwis ◽  
Jean-Paul Chapelle ◽  
Edouard Louis ◽  
...  

Protein profiling using SELDI-TOF-MS has gained over the past few years an increasing interest in the field of biomarker discovery. The technology presents great potential if some parameters, such as sample handling, SELDI settings, and data analysis, are strictly controlled. Practical considerations to set up a robust and sensitive strategy for biomarker discovery are presented. This paper also reviews biological fluids generally available including a description of their peculiar properties and the preanalytical challenges inherent to sample collection and storage. Finally, some new insights for biomarker identification and validation challenges are provided.


2012 ◽  
Vol 31 (4) ◽  
pp. 265-270 ◽  
Author(s):  
Mario Plebani

Summary Laboratory medicine, as a specialty that had prioritised quality control, has always been at the forefront of error reduction. In the last decades, a dramatic decrease of analytical errors has been experienced, while a relatively high frequency of errors has been documented in the pre-analytical phase. Most pre-analytical errors, which account for up to 70% of all mistakes made in laboratory diagnostics, arise during patient preparation, and sample collection, transportation, preparation for analysis and storage. However, while it has been reported that the pre-analytical phase is error-prone, only recently has it been demonstrated that most of these errors occur in the »pre-pre-analytical phase«, which comprises the initial procedures of the testing process performed outside the laboratory walls by healthcare personnel outside the direct control of the clinical laboratory. Developments in automation and information technologies have played a major role in decreasing some pre-analytical errors and, in particular, the automation of repetitive, errorprone and bio-hazardous pre-analytical processes performed within the laboratory walls has effectively decreased errors in specimen preparation, centrifugation, aliquot preparation, pipetting and sorting. However, more efforts should be made to improve the appropriateness of test request, patient and sample identification procedures and other pre-analytical steps performed outside the laboratory walls.


Author(s):  
Jenna Khan ◽  
Joshua A. Lieberman ◽  
Christina M. Lockwood

Abstract:microRNAs (miRNAs) hold promise as biomarkers for a variety of disease processes and for determining cell differentiation. These short RNA species are robust, survive harsh treatment and storage conditions and may be extracted from blood and tissue. Pre-analytical variables are critical confounders in the analysis of miRNAs: we elucidate these and identify best practices for minimizing sample variation in blood and tissue specimens. Pre-analytical variables addressed include patient-intrinsic variation, time and temperature from sample collection to storage or processing, processing methods, contamination by cells and blood components, RNA extraction method, normalization, and storage time/conditions. For circulating miRNAs, hemolysis and blood cell contamination significantly affect profiles; samples should be processed within 2 h of collection; ethylene diamine tetraacetic acid (EDTA) is preferred while heparin should be avoided; samples should be “double spun” or filtered; room temperature or 4 °C storage for up to 24 h is preferred; miRNAs are stable for at least 1 year at –20 °C or –80 °C. For tissue-based analysis, warm ischemic time should be <1 h; cold ischemic time (4 °C) <24 h; common fixative used for all specimens; formalin fix up to 72 h prior to processing; enrich for cells of interest; validate candidate biomarkers with in situ visualization. Most importantly, all specimen types should have standard and common workflows with careful documentation of relevant pre-analytical variables.


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