Canine Cerebrospinal Fluid Analysis Using Two New Automated Techniques: The Sysmex XN-V Body Fluid Mode and an Artificial-Intelligence-Based Algorithm.

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.

Comparison of Sysmex XN-V body fluid mode and deep-learning-based quantification with manual techniques for total nucleated cell count and differential count for equine bronchoalveolar lavage samples.

BACKGROUND: Bronchoalveolar lavage (BAL) is a diagnostic method for the assessment of the lower respiratory airway health status in horses. Differential cell count and sometimes also total nucleated cell count (TNCC) are routinely measured by time-consuming manual methods, while faster automated methods exist. The aims of this study were to compare: 1) the Sysmex XN-V body fluid (BF) mode with the manual techniques for TNCC and two-part differential into mononuclear and polymorphonuclear cells; 2) the Olympus VS200 slide scanner and software generated deep-learning-based algorithm with manual techniques for four-part differential cell count into alveolar macrophages, lymphocytes, neutrophils, and mast cells. The methods were compared in 69 clinical BAL samples. RESULTS: Incorrect gating by the Sysmex BF mode was observed on many scattergrams, therefore all samples were reanalyzed with manually set gates. For the TNCC, a proportional and systematic bias with a correlation of r = 0.79 was seen when comparing the Sysmex BF mode with manual methods. For the two-part differential count, a mild constant and proportional bias and a very small mean difference with moderate limits of agreement with a correlation of r = 0.84 and 0.83 were seen when comparing the Sysmex BF mode with manual methods. The Sysmex BF mode classified significantly more samples as abnormal based on the TNCC and the two-part differential compared to the manual method. When comparing the Olympus VS200 deep-learning-based algorithm with manual methods for the four-part differential cell count, a very small bias in the regression analysis and a very small mean difference in the difference plot, as well as a correlation of r = 0.85 to 0.92 were observed for all four cell categories. The Olympus VS200 deep-learning-based algorithm also showed better precision than manual methods for the four-part differential cell count, especially with an increasing number of analyzed cells. CONCLUSIONS: The Sysmex XN-V BF mode can be used for TNCC and two-part differential count measurements after reanalyzing the samples with manually set gates. The Olympus VS200 deep-learning-based algorithm correlates well with the manual methods, while showing better precision and can be used for a four-part differential cell count.

Validation of the Sysmex XN-V Automated Nucleated Red Blood Cell Enumeration for Canine and Feline EDTA-Anticoagulated Blood.

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.

Acidification is required for calcium and magnesium concentration measurements in equine urine.

BACKGROUND: Acidification of equine urine to promote dissociation of ion complexes is a common practice for urine ion concentration measurements. The objective of this study was to evaluate the effect of acidification and storage after acidification on calcium (Ca), magnesium (Mg) and phosphate (P) concentrations and on fractional excretion (FE) of these electrolytes. Thirty-two fresh equine urine samples were analysed between December 2016 and July 2020. Complete urinalysis (stick and sediment) was performed on all samples. Ca, Mg, P and creatinine concentrations were measured in supernatant of centrifuged native urine, urine directly centrifuged after acidification and urine centrifuged 1 hour after acidification. Urine was acidified with hydrochloric acid to reach a pH of 1-2. Ca, Mg, P and creatinine concentrations were also measured in blood plasma, and fractional excretion of each electrolyte was calculated. Equality of medians was tested with Friedman tests and Bland-Altman bias plots were used to show the agreement between conditions. RESULTS: Acidification had a statistically significant effect on Ca and Mg concentrations, FECa and FEMg. Bland-Altman plot revealed a strong positive proportional bias between Ca concentration in native and acidified urine with a mean bias of 17.6 mmol/l. For Mg concentration, the difference between native and acidified urine was small with a mean bias of 1.8 mmol/l. The increase in FECa was clinically relevant. Storage of acidified urine had no effect on any of the measured ion concentrations. All P concentrations in native urine samples were below the detection limit of the assay and statistical analysis and calculation of FEP was not possible. CONCLUSIONS: Urine acidification is essential for accurate measurement of Ca and Mg concentrations and therefore FE calculations in equine urine. Storage time of 1 hour after acidification does not significantly change Ca and Mg concentrations.

Findings Related to Cerebrospinal Fluid and Central Nervous System Disorders in Small Ruminants-A Retrospective Study on Sheep and Goats.

BACKGROUND: Small ruminants often suffer from central nervous system (CNS) disorders, and cerebrospinal fluid (CSF) analysis can be used as a diagnostic tool in this regard. In small animals and cattle, specific CSF patterns have been defined for specific disease categories. No data exist regarding CSF results obtained from small ruminants and their association with certain CNS diseases. OBJECTIVES: The objective of this study was to retrospectively investigate CSF findings obtained from sheep and goats and to identify possible CSF patterns associated with disease categories. METHODS: CSF samples and medical records from 44 sheep and 27 goats were included in this study. All animals were presented to the Veterinary Teaching Hospital Zurich of the Veterinary Teaching Hospital Zurich of the Vetsuisse Faculty of the University of Zurich between 2003 and 2016 and had either a confirmed CNS diagnosis or showed CSF changes without a specific CNS diagnosis. RESULTS: Mixed mononuclear pleocytosis was the most common CSF pattern in sheep (25%), followed by monocytic pleocytosis (21%). Lymphocytic pleocytosis was most frequently found in goats (37%). In 75% of sheep and 56% of goats, infectious CNS diseases were diagnosed, with listeriosis being the most common infectious disease in both species, followed by parasitic disorders (nematodiasis and coenurosis). CONCLUSIONS: The cytologic CSF patterns in small ruminants are mainly based on the increased presence of monocytic and lymphocytic cells with variable quantitative expression, whereas neutrophilic pleocytosis and cytoalbuminologic dissociation were rare findings. Infectious diseases of bacterial origin were the most common underlying causes for CSF alterations in sheep and goats, followed by parasitic disorders. The pleocytosis type is not helpful for differentiating disease types.