The performance of DI-60 for WBC differential, RBC classification, and platelet estimation seems to be acceptable even in abnormal samples with improvement after verification. DI-60 would help optimize the workflow in hematology laboratory with reduced manual workload.

Highlights Approximately 17% of the patients with COVID-19 exhibited no symptoms. The median time from diagnosis to negative conversion was about 2 weeks in the natural course of asymptomatic COVID-19. Time until negative conversion was shorter in asymptomatic COVID-19 than in symptomatic COVID-19. Rebound of cycle threshold values was not uncommon in follow-up RT-PCR assays.

The C values successfully predicted toxin EIA positivity and could be used as a surrogate for toxin EIA positivity in the diagnostic algorithm and routine analysis. Further studies are needed to validate the clinical significance of tcdB PCR C value in toxigenic C. difficile colonization and infection.

Background: Digital morphology (DM) analyzers are increasingly being used for white blood cell (WBC) differentials. We assessed the laboratory efficiency of the Sysmex DI-60 system (DI-60; Sysmex, Kobe, Japan) in comparison with manual counting in leukopenic samples.Methods: In total, 40 peripheral blood smear samples were divided into normal, mild leukopenia, moderate leukopenia, and severe leukopenia groups based on WBC count. In each group, the risk and turnaround time (TAT) were compared between DI-60 and manual counting. Risk was determined by failure mode and effect analysis using the risk priority number (RPN) score, and TAT was recorded for the analytical phase.Results: Overall, DI-60 showed a five-fold lower risk (70 vs. 350 RPN) and longer TAT than manual counting. In severe leukopenic samples, DI-60 showed a shorter TAT/slide and a remarkably lower cell count/slide than manual counting. In all samples, the TAT/cell for DI-60 was substantially longer than that for manual counting (DI-60 vs. manual: total, 1.8 vs. 1.0 sec; normal, 1.5 vs. 0.7 sec; mild leukopenia, 1.9 vs. 0.9 sec; moderate leukopenia, 1.8 vs. 1.0 sec; severe leukopenia, 28.8 vs. 19.0 sec).Conclusions: This is the first comparative assessment of risk and TAT between DI-60 and manual counting in leukopenic samples. DI-60 decreases the laboratory risk and improves patient safety, but requires more time to count fewer cells, especially in severe leukopenic samples. DM analyzers should be applied selectively depending on the WBC count to optimize laboratory efficiency.

Since every single test has some limitations for detecting toxigenic Clostridium difficile, multistep algorithms are recommended. This study aimed to compare the current, representative diagnostic algorithms for detecting toxigenic C. difficile, using VIDAS C. difficile toxin A&B (toxin ELFA), VIDAS C. difficile GDH (GDH ELFA, bioMérieux, Marcy-l’Etoile, France), and Xpert C. difficile (Cepheid, Sunnyvale, California, USA). In 271 consecutive stool samples, toxigenic culture, toxin ELFA, GDH ELFA, and Xpert C. difficile were performed. We simulated two algorithms: screening by GDH ELFA and confirmation by Xpert C. difficile (GDH + Xpert) and combined algorithm of GDH ELFA, toxin ELFA, and Xpert C. difficile (GDH + Toxin + Xpert). The performance of each assay and algorithm was assessed. The agreement of Xpert C. difficile and two algorithms (GDH + Xpert and GDH+ Toxin + Xpert) with toxigenic culture were strong (Kappa, 0.848, 0.857, and 0.868, respectively). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of algorithms (GDH + Xpert and GDH + Toxin + Xpert) were 96.7%, 95.8%, 85.0%, 98.1%, and 94.5%, 95.8%, 82.3%, 98.5%, respectively. There were no significant differences between Xpert C. difficile and two algorithms in sensitivity, specificity, PPV and NPV. The performances of both algorithms for detecting toxigenic C. difficile were comparable to that of Xpert C. difficile. Either algorithm would be useful in clinical laboratories and can be optimized in the diagnostic workflow of C. difficile depending on costs, test volume, and clinical needs.