In vitro quantitation of Theileria parva sporozoites for use - TropMed ...

Chapter 1: Quantitation of Theileria parva sporozoites: Review of literature 39
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a) Proportions of infected cells
Comparison of proportions of schizont infected lymphoblasts from pools of bovine peripheral
blood mononuclear cells (PBMC) incubated with different T. parva stabilates or dilutions of the
same stabilate (Gray and Brown, 1981) are made. A variation of this is practiced in SNA's in
which stabilate concentration is kept constant and anti-sera are diluted serially. Neutralisation is
then calculated from percentages of schizont bearing cells (Musoke et al., 1992). The main
drawbacks are that the distribution of the counts is not clear and depending on time of reading,
relative proportions of infected cells may vary.
b) Proportion of infected culture wells
This involves calculating and comparing Effective or Infective Doses (ED or ID, respectively) of
serially diluted stabilates that result in a given proportion of lymphocyte-plated wells being
infected in microtitration plates (Wilkie et al., 2002; Marcotty et al., 2004). This has the advantage
of presenting a known distribution (binomial) and the effect of time is insignificant because wells
are either negative or positive at time of reading.
1.3.3.2. Analysis of in vitro data
Statistical rendering of the data has been varied. Various analytical studies have been published,
based mostly on analysis of variance (ANOVA) and regression analyses. Wilkie and colleagues
(2002) compared effective dilutions in tick equivalents (ED 50 ) calculated by the Spearman-Karber
method. Marcotty et al. (2004) using a stratified or a robust logistic regression model compared
effective doses (ED 50 ) in a manner similar to Wilkie et al. (2002) but developed a multi-sessional
approach in which more repetitions (sessions) of each experiment were done. Each session was
then treated as a primary sampling unit in the analysis. This approach not only reduces confidence
intervals when the data are stratified but captures the clustering effect of the sessions which may
be overlooked in other models. For instance, classical ANOVA may not adequately take into
account this inter-sessional variation and the associated standards errors.
Other weaknesses of fixed models include a lack of consideration for interactions between
variables and for random factors. Explanatory variables are simply assumed to be independent i.e.
there is no covariance between them. Random factors that may be present due to stratifying or
multistage sampling as the case may be in in vitro titrations, can give erroneous results if not taken
into account (Speybroeck et al., 2003). These can be addressed by more robust mixed effect
modelling such as Generalized Linear Latent and Mixed Models (GLLAMM) (Rabe-Hesketh et
al., 2002), which capture these effects.