High-Throughput Bacteriophage Testing with Potency Determination: Validation of an Automated Pipetting and Phage Drop-Off Method.

The development of bacteriophages (phages) as active pharmaceutical ingredients for the treatment of patients is on its way and regulatory agencies are calling for reliable methods to assess phage potency. As the number of phage banks is increasing, so is the number of phages that need to be tested to identify therapeutic candidates. Currently, assessment of phage potency on a semi-solid medium to observe plaque-forming units is unavoidable and proves to be labor intensive when considering dozens of phage candidates. Here, we present a method based on automated pipetting and phage drop-off performed by a liquid-handling robot, allowing high-throughput testing and phage potency determination (based on phage titer and efficiency of plaquing). Ten phages were tested, individually and assembled into one cocktail, against 126 Escherichia coli strains. This automated method was compared to the reference one (manual assay) and validated in terms of reproducibility and concordance (ratio of results according to the Bland and Altman method: 0.99; Lin's concordance correlation coefficient: 0.86). We found that coefficients of variation were lower with automated pipetting (mean CV: 13.3% vs. 24.5%). Beyond speeding up the process of phage screening, this method could be used to standardize phage potency evaluation.

Statistical approach for radioactivity detection: A brief review.

Radioactivity detection is a major research and development priority for many practical applications. Amongst the various technical challenges in this field is the need to carry out accurate low-level radioactivity measurements in the presence of a large fluctuations in the natural radiation background, while reducing the false alarm rates. The task becomes even more harder with high detection limits under low signal-to-background ratios. A detection method based on the statistical inference, following either a frequentist or a Bayesian paradigm, adopted to overcome these challenges as well as to ensure a reliable and accurate diagnosis with a competitive tradeoff between sensitivity, specificity and response time. With this respect, several research studies, addressing a range of applications from decommissioning and dismantling to homeland security, have been proposed. Our main goal in this paper is to present a succinct survey of these studies based on a frequentist and Bayesian approaches used to decision-making, uncertainty and risk evaluation, in the context of radioactive detection. In this prospect, a theoretical background of statistical frequentist and Bayesian inferences was presented. Then, a comparative study of both approaches was performed to determine the optimal approach in regards to accuracy and pros/cons. A case of study for low-level radioactivity detection in nuclear decommissioning operations was provided to validate the optimal approach. Results proved the efficiency and usefulness of Bayesian approach against frequentist one with respect to the most challenging scenarios in radiation detection applications.