Experiential relationship between malaria parasite density and some haematological parameters in malaria infected male subjects in Port Harcourt, Nigeria.
Experiential relationship between malaria parasite density and some haematological parameters in malaria infected male subjects in Port Harcourt, Nigeria.
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DOI：
10.5539/gjhs.v4n4p139
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发表时间：
2012-06-15
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影响因子：
--
通讯作者：
Ukaji, D C
中科院分区：
文献类型：
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作者：
M, Eze Evelyn;Ezeiruaku, F C;Ukaji, D C
本研究调查了尼日利亚哈科特港向疟疾诊所报告的男性恶性疟原虫感染患者的寄生虫密度与血液学参数之间的经验关系。总共招募了136名男性患者。采用QBC血液学分析、QBC疟原虫种类鉴定与定量、血膜鉴别白细胞计数法。采用Microsoft Excel统计软件包测定各四分位寄生虫密度血液学参数的平均值。采用回归分析对寄生虫密度与血液学参数之间的经验关系进行建模。对所有回归关系进行检验，接受决定系数（R2）最高的关系为有效关系。测试的关系包括线性关系、多项式关系、指数关系、对数关系和幂关系。回归图的X轴代表寄生虫密度，y轴代表各自的血液学参数。中性粒细胞计数与寄生虫密度呈负指数关系，与寄生虫密度的关系为多项式方程模型：ynm = - 7e -07x2 - 0.0003x + 56.685。决定系数（R2）为0.6140。这意味着寄生虫病的变化率将取决于中性粒细胞的初始值。随着中性粒细胞的增加，寄生虫病呈两倍、三倍、四倍的减少趋势。淋巴细胞计数、单核细胞计数和嗜酸性粒细胞计数与寄生虫密度呈对数关系，依次用线性方程模型表示：ylm = - 2.3711 ln(x) + 37.296, ymm = 0.6965ln(x) + 5.7692, yem = 0.9334ln(x) + 4.1718。其高决定系数（R2）分别为0.8027、0.8867和0.9553。这种对数关系意味着，单核细胞计数和嗜酸性粒细胞计数每增加一倍，就会导致寄生虫病增加相同数量，而淋巴细胞计数每增加一倍，就会导致寄生虫病减少相同数量。总白细胞计数（WBC）、血红蛋白浓度、红细胞压积（PCV）和平均细胞血红蛋白浓度（MCHC）与寄生虫密度的最佳拟合回归模型为线性模型，用线性方程模型表示：yWBCm = 1.2314x + 8533.8, yHbm = -0.0014x + 13.004, yPCVm = -0.0046x + 41.443, yMCHCm = -0.0008x + 32.336。它们的决定系数分别为0.7397、0.6248、0.9758和0.8584。这种线性关系意味着随着寄生虫密度的增加，血红蛋白浓度、PCV和MCHC相应降低，白细胞总数相应增加。血小板计数与寄生虫密度之间的最佳拟合回归模型是决定系数非常高的幂模型（R2=0.9938），表示为：yPltm = 278047x-0.122。这些方程模型在可能没有功能显微镜或有能力的显微镜师的地区和医疗紧急情况下可能非常有用。
This study examined the experiential relationship between the parasite density and haematological parameters in male patients with Plasmodium falciparum infection in Port Harcourt, Nigeria reporting to malaria clinics. A total of one hundred and thirty-six (136) male patients were recruited. QBC haematological analysis, QBC malaria parasite specie identification and quantification and thin blood film for differential leucocytes count was used. The mean values of the haematological parameters in each quartile of parasite densities were determined using Microsoft Excel statistical package. Regression analysis was employed to model the experiential relationship between parasite density and haematological parameters. All regression relationships were tested and the relationship with the highest coefficient of determination (R2) was accepted as the valid relationship. The relationships tested included linear, polynomial, exponential, logarithmic and power relationships. The X- axis of the regression graphs stand for the parasite density while Y-axis stands for the respective haematological parameters Neutrophil count had a negative exponential relationship with the parasite density and is related to the parasite density by a polynomial equation model: ynm = -7E-07x2 - 0.0003x + 56.685.The coefficient of determination (R2) was 0.6140. This means that the rate of change of the parasitemia will depend on the initial value of the neutrophil. As the neutrophil increases, the parasitemia will tend to decrease in a double, triple and quadruple manner. The relationship between lymphocyte count, monocyte count and eosinophil count and parasite density was logarithmic and expressed by the following linear equation models: ylm = -2.371ln(x) + 37.296, ymm = 0.6965ln(x) + 5.7692 and yem = 0.9334ln(x) + 4.1718 in the same order. Their respective high coefficients of determination (R2) were 0.8027, 0.8867 and 0.9553. This logarithmic relationship means that each doubling of monocyte count and eosinophil count will cause the same amount of increase in parasitemia whereas each doubling of lymphocyte count will cause the same amount of decrease in parasitemia. The best fitting regression model for total white cell count (WBC), haemoglobin concentration, packed cell volume (PCV)(haematocrit) and mean cell haemoglobin concentration (MCHC) and parasite density was a linear model and expressed by the following linear equation models: yWBCm = 1.2314x + 8533.8, yHbm = -0.0014x + 13.004, yPCVm = -0.0046x + 41.443 and yMCHCm = -0.0008x + 32.336. Their respective coefficients of determination are 0.7397, 0.6248, 0.9758 and 0.8584. This linear relationship means that as the parasite density is increasing that there is a corresponding decrease in haemoglobin concentration, PCV and MCHC and a corresponding increase in total white cell count. The best fitting regression model between platelet count and parasite density is a power model with a very high coefficient of determination (R2=0.9938) and expressed by: yPltm = 278047x-0.122. These equation models could be very useful in areas where there may not be functional microscopes or competent microscopists and in medical emergencies.