Development and evaluation of the automated multipurpose molecular testing system PCRpack for high-throughput SARS-CoV-2 testing.

IMPORTANCE: Accurate and fast molecular testing is important for the diagnosis and control of COVID-19. During patient surges in the COVID-19 pandemic, laboratories were challenged by a higher demand for molecular testing under skilled staff shortages. We developed an automated multipurpose molecular testing system, named PCRpack, for the rapid, high-throughput testing of infectious pathogens, including SARS-CoV-2. The system is provided in an all-in-one package, including a liquid handling instrument, a laboratory information management system, and other materials needed for testing operation; is highly customizable; and is easily implemented. PCRpack showed robust liquid handling performance, high clinical diagnostic performance, a shorter turn-around time with minimal hands-on time, and a high testing capacity. These features contribute to the rapid implementation of the high-performance and high-throughput molecular testing environment at any phase of the pandemic caused by SARS-CoV-2 or future emerging pathogens.

Implementation of a personal identification system using alveolar bone images.

OBJECTIVE: In recent years, personal identification has been performed using antemortem panoramic X-ray images and postmortem-CT images. Using these, we have developed a personal identification method that focuses on the alveolar bone. This study examined the effectiveness of this method and aimed to implement a reproducible system. MATERIALS AND METHODS: For personal identification, a total of 633 CT images and panoramic X-ray images belonging to three groups with different conditions were used.　These images were 160 sets in the same person group and 96,820 in the other groups. The similarity of alveolar bone images was calculated using the landmark method of Procrustes analysis. The processes were system implemented and the methodology was validated. RESULTS: The ability to identify between the same person group and other person groups showed 0.9769 as the area under the curve (AUC: ROC curve). At the cutoff value of 4.978, there was no false rejection rate, but false acceptance rate was slightly higher. CONCLUSION: This method was useful as a screening method for personal identification. In addition, system implementation was efficient and reduced human error. In the future, we aim to realize a more efficient personal identification method using distortion-corrected images and including auto-detective landmarks using deep learning.