Abstract ▼ AI-Helper

The blood picture of 85 healthy race horses in Korea was investigated. The ranges and mean values of erythrocyte, hemoglobin, hematocrit value, mean corpuscular volume, mean corpuscular hemoglobin concentration, and total white blood cell count in the blood picture were determine. The respective mea...

The blood picture of 85 healthy race horses in Korea was investigated. The ranges and mean values of erythrocyte, hemoglobin, hematocrit value, mean corpuscular volume, mean corpuscular hemoglobin concentration, and total white blood cell count in the blood picture were determine. The respective mean value and standard deviation and age differences were as follows: 1. The erythrobyte count was shown as range of 6.20 to $11.32{\times}10^6/mm^3$ with mean of $8.61{\pm}1.92{\times}10^6/mm^3$(SD). The leucocyte count was shown as range 5.0 to $18.0{\times}10^3/mm^3$ with mean of $8.25{\pm}1.51{\times}10^3/mm^3$(SD). There were not significant. differences in age, 2. The mean value of hemoglobin was shown $13.9{\pm}1.7g/100ml(SD)$ ranging 9.8 to 16.8g/100ml. The mean value of hematocrit was shown $40.9{\pm}3.94ml/100ml(SD)$ ranging 26 to 54. There were not significant differences in age. 3. The mean corpuscular hemoglobin was shown as range of 11.8 to 22.2pg with mean of $16.9{\pm}4.69$(SD). The mean corpuscular volume was shown as range of 34.5 to $71.3cu{\mu}$ with mean of $49.0{\pm}7.32cu{\mu}$(SD). The mean corpuscular hemoglobin concentration was shown as range of 30.6 to 39.4 g/100 ml with mean of $34.6{\pm}2.36$(SD). There were not significant differences in age. 4. The correlation among erythrocyte count, hemoglobin and hematocrit value were observed as follows: Erythrocyte count and hemoglobin (+0.328), rythrocyte count and hematocrit vague (+0.319). A linear regression equation was shown as follows: Erythrocyte count and hemoglobin (Y=0.336x+10.977), erythrocyte count and hematocrit value (Y=0.655x+35.274). 5. The high correlation between hemoglobin and hematocrit vague was observed (r= +0.836). A linear regression equation was shown: (Y=1.948x+13.895).