Digital morphology (DM) analyzers are widely applied in clinical practice. It is necessary to evaluate performances of DM analyzers by focusing on leukopenic samples. We evaluated the analytical performance, including precision, of a Sysmex DI-60 system (Sysmex, Kobe, Japan) on white blood cell (WBC) differentials in leukopenic samples. In a total of 40 peripheral blood smears divided into four groups according to WBC count (normal, mild, moderate, and severe leukopenia; each group n = 10), we evaluated precision of WBC preclassificaiton by DI-60. %coefficients of variation (%CVs) of precision varied for each sample and for each cell class; the fewer cells per slide, the higher %CV. The overall specificity and efficiency were high for all cell classes except plasma cells (95.9–99.9% and 90.0–99.4%, respectively). The largest absolute value of mean difference between DI-60 and manual count in each group was: 10.77, normal; 10.22, mild leukopenia; 19.09, moderate leukopenia; 47.74, severe leukopenia. This is the first study that evaluated the analytical performance of DI-60 on WBC differentials in leukopenic samples as the main subject. DI-60 showed significantly different performance depending on WBC count. DM analyzers should be evaluated separately in leukopenic samples, even if the overall performance was acceptable.

Objectives CellaVision DC-1 (DC-1, Sysmex, Kobe, Japan) is a newly launched digital morphology analyzer that was developed mainly for small to medium-volume laboratories. We evaluated the precision, qualitative performance, comparison of cell counts between DC-1 and manual counting, and turnaround time (TAT) of DC-1. Methods Using five peripheral blood smear (PBS) slides spanning normal white blood cell (WBC) range, precision and qualitative performance of DC-1 were evaluated according to the Clinical and Laboratory Standards Institute (CLSI) EP15-A3, EP15-Ed3-IG1, and EP12-A2 guidelines. Cell counts of DC-1 and manual counting were compared according to the CLSI EP 09C-ED3 guidelines, and TAT of DC-1 was also compared with TAT of manual counting. Results DC-1 showed excellent precision (%CV, 0.0–3.5%), high specificity (98.9–100.0%), and high negative predictive value (98.4–100.0%) in 18 cell classes (12 WBC classes and six non-WBC classes). However, DC-1 showed 0% of positive predictive value in seven cell classes (metamyelocytes, myelocytes, promyelocytes, blasts, plasma cells, nucleated red blood cells, and unidentified). The largest absolute mean differences (%) of DC-1 vs. manual counting was 2.74. Total TAT (min:s) was comparable between DC-1 (8:55) and manual counting (8:55). Conclusions This is the first study that comprehensively evaluated the performance of DC-1 including its TAT. DC-1 has a reliable performance that can be used in small to medium-volume laboratories for assisting PBS review. However, DC-1 may make unnecessary workload for cell verification in some cell classes.

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.

ABO incompatibility is not considered a contraindication for hematopoietic stem cell transplantation (HSCT). We hypothesized that recipient-derived isoagglutinin (RDI) levels could play a critical role in clinical outcomes. In this study, we compared clinical outcomes such as survival, GVHD, infection, relapse, transfusion, and engraftment, among ABO-compatible patients (ABOc), ABO-incompatible patients (ABOi) with low RDI, and ABOi patients with high RDI. The ABOi with high RDI group was defined as recipients with more than 1:16 RDI levels. We analyzed 103 recipients (ABOc, 53; ABOi with low RDI, 36; ABOi with high RDI, 14). The ABOi with high RDI group showed a decreased 1-year survival and increased acute GVHD grade IV and RBC transfusion (p = 0.017, 0.027, and 0.032, respectively). The ABOi with high RDI group was an independent risk factor for increased death, RBC transfusion, and poor platelet (PLT) engraftment (odds ratio (OR) = 3.20, p = 0.01; OR = 8.28, p = 0.02; OR = 0.18, p = 0.03, respectively). The ABOi with high RDI group showed significantly delayed PLT engraftment. In conclusion, this is the first study underscoring high RDI levels as a marker predicting unfavorable outcomes in ABOi HSCT.

The DHL began test and research on delivery system by drone. Because of growing demand about comercialized and personal-use drone business, we needed a real-time unmanned drone control system that individuals can afford. In this paper, we propose an autonomous flight system for drone. We used marker recognition technique on that system because it doesn't require high-spec device which cannot be afforded by ordinary people. The proposed system maintains distance between drone and marker in flight. The system estimates the distance between marker and drone by calculating area of recognized marker image. Performance is validated by result of the proposed system experiment with Parrot's aerial vehicle AR.drone 2.0 and Android device. The proposed system is expected to be used in promising areas like chase camera, unmanned transport and etc.