Search Results (2)

Cerebrospinal fluid analysis is an important diagnostic test when assessing a neurological canine patient. For this analysis, the total nucleated cell count and differential cell counts are routinely taken, but both involve time-consuming manual methods. To investigate faster automated methods, in this study, the Sysmex XN-V body fluid mode and the deep-learning-based algorithm generated by the Olympus VS200 slide scanner were compared with the manual methods in 161 canine cerebrospinal fluid samples for the total nucleated cell count and in 65 samples with pleocytosis for the differential counts. Following incorrect gating by the Sysmex body fluid mode, all samples were reanalyzed with manually set gates. The Sysmex body fluid mode then showed a mean bias of 15.19 cells/μL for the total nucleated cell count and mean biases of 4.95% and −4.95% for the two-part differential cell count, while the deep-learning-based algorithm showed mean biases of −7.25%, −0.03% and 7.27% for the lymphocytes, neutrophils and monocytoid cells, respectively. Based on our findings, we propose that the automated Sysmex body fluid mode be used to measure the total nucleated cell count in canine cerebrospinal fluid samples after making adjustments to the predefined settings from the manufacturer. However, the two-part differential count of the Sysmex body fluid mode and the deep-learning-based algorithm require some optimization. Full article

The enumeration of nRBCs (nucleated red blood cells) by manual counting is time-consuming and imprecise. As the first veterinary hematology analyzer, Sysmex XN-V provides automated nRBC counts. This study aimed to evaluate the performance of Sysmex XN-V in the enumeration of nRBCs for cats and dogs by comparing automated nRBC counts to manual counts from a total of 3810 canine and 2844 feline specimens. Repeatability, reproducibility, stability, carry-over, and linearity were assessed. The repeatability and reproducibility of Sysmex XN-V were good, with mean coefficients of variation (CV) of 4.5% and 5.4%, respectively. Bland–Altman difference analysis revealed mean biases shown as nRBCs/100 WBCs of 0.01 in dogs and 0.11 in cats with low nRBCs (<5/100 WBCs), mean biases of −1.27 in dogs and −0.24 in cats with moderate nRBC counts (5–20 nRBCs/100 WBCs), and mean biases of −7.76 in dogs and −1.31 in cats with high nRBC counts (>20 nRBCs/100 WBCs). The total observable error was below 9% in both species and at all ranges. Overall concordance between methods was high (91% in canine and 93% in feline samples). The automated nRBC count by Sysmex XN-V was found to be accurate and precise and can replace manual counts for cat and dog samples. Non-statistical quality assurance by scattergram evaluation, re-gating, and confirmation by blood smear evaluation is, however, recommended, especially in cases with severe normoblastosis. This advancement will save time, reduce errors, and add prognostic value to hematological results for animal patients. Full article