Establishment of calculated panel reactive antibody and its potential benefits in improving the kidney allocation strategy in Taiwan

Shen, Ssu-Wen; Chang, Chun-Kai; Gao, Yi-Shun; Hsu, Pin-Jou; Liu, Fei-Yun; Lo, Su Shun

doi:10.1016/j.jfma.2017.09.008

Cited by 4 publications

(1 citation statement)

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“…There has been significant progress in automated data analysis in recent years with multiple efforts in developing supervised (and non-supervised) machine learning algorithms to analyse FC data through cluster analysis and pattern recognition [17][18][19][20]. Whilst these provide significant and varied toolsets for analysing highly dimensional data in a shorter time (reducing human input and human factored measurement variation), the potential for intrinsic variability of these methods results in the need for an independent evaluation through a validation process.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Protocol Impact on Subjectivity Uncertainty When Analyzing Peripheral Blood Mononuclear Cell Flow Cytometry Data Files

Grant

Coopman

Silva‐Gomes

et al. 2021

MPs

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Measured variability of product within Cell and Gene Therapy (CGT) manufacturing arises from numerous sources across pre-analytical to post-analytical phases of testing. Operators are a function of the manufacturing process and are an important source of variability as a result of personal differences impacted by numerous factors. This research uses measurement uncertainty in comparison to Coefficient of Variation to quantify variation of participants when they complete Flow Cytometry data analysis through a 5-step gating sequence. Two study stages captured participants applying gates using their own judgement, and then following a diagrammatical protocol, respectively. Measurement uncertainty was quantified for each participant (and analysis phase) by following Guide to the Expression of Uncertainty in Measurement protocols, combining their standard deviations in quadrature from each gating step in the respective protocols. When participants followed a diagrammatical protocol, variation between participants reduced by 57%, increasing confidence in a more uniform reported cell count percentage. Measurement uncertainty provided greater resolution to the analysis processes, identifying that most variability contributed in the Flow Cytometry gating process is from the very first gate, where isolating target cells from dead or dying cells is required. This work has demonstrated the potential for greater usage of measurement uncertainty within CGT manufacturing scenarios, due to the resolution it provides for root cause analysis and continuous improvement.

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Section: Introductionmentioning

confidence: 99%