Publications by authors named "J N Petzing"

Flow cytometry is widely used within the manufacturing of cell and gene therapies to measure and characterise cells. Conventional manual data analysis relies heavily on operator judgement, presenting a major source of variation that can adversely impact the quality and predictive potential of therapies given to patients. Computational tools have the capacity to minimise operator variation and bias in flow cytometry data analysis; however, in many cases, confidence in these technologies has yet to be fully established mirrored by aspects of regulatory concern.

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Application of synthetic datasets in training and validation of analysis tools has led to improvements in many decision-making tasks in a range of domains from computer vision to digital pathology. Synthetic datasets overcome the constraints of real-world datasets, namely difficulties in collection and labeling, expense, time, and privacy concerns. In flow cytometry, real cell-based datasets are limited by properties such as size, number of parameters, distance between cell populations, and distributions and are often focused on a narrow range of disease or cell types.

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In-situ metrology utilised for surface topography, texture and form analysis along with quality control processes requires a high-level of reliability. Hence, a traceable method for calibrating the measurement system's transfer function is required at regular intervals. This paper compares three methods of dimensional calibration for a spectral domain low coherence interferometer using a reference laser interferometer versus two types of single material measure.

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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.

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Automated flow cytometry (FC) data analysis tools for cell population identification and characterization are increasingly being used in academic, biotechnology, pharmaceutical, and clinical laboratories. The development of these computational methods is designed to overcome reproducibility and process bottleneck issues in manual gating, however, the take-up of these tools remains (anecdotally) low. Here, we performed a comprehensive literature survey of state-of-the-art computational tools typically published by research, clinical, and biomanufacturing laboratories for automated FC data analysis and identified popular tools based on literature citation counts.

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