Immune-mediated competition in rodent malaria is most likely caused by induced changes in innate immune clearance of merozoites

Malarial infections are often genetically diverse, leading to competitive interactions between parasites. A quantitative understanding of the competition between strains is essential to understand a wide range of issues, including the evolution of virulence and drug resistance. In this study, we use dynamical-model based Bayesian inference to investigate the cause of competitive suppression of an avirulent clone of Plasmodium chabaudi (AS) by a virulent clone (AJ) in immuno-deficient and competent mice. We test whether competitive suppression is caused by clone-specific differences in one or more of the following processes: adaptive immune clearance of merozoites and parasitised red blood cells (RBCs), background loss of merozoites and parasitised RBCs, RBC age preference, RBC infection rate, burst size, and within-RBC interference. These processes were parameterised in dynamical mathematical models and fitted to experimental data. We found that just one parameter μ, the ratio of background loss rate of merozoites to invasion rate of mature RBCs, needed to be clone-specific to predict the data. Interestingly, μ was found to be the same for both clones in single-clone infections, but different between the clones in mixed infections. The size of this difference was largest in immuno-competent mice and smallest in immuno-deficient mice. This explains why competitive suppression was alleviated in immuno-deficient mice. We found that competitive suppression acts early in infection, even before the day of peak parasitaemia. These results lead us to argue that the innate immune response clearing merozoites is the most likely, but not necessarily the only, mediator of competitive interactions between virulent and avirulent clones. Moreover, in mixed infections we predict there to be an interaction between the clones and the innate immune response which induces changes in the strength of its clearance of merozoites. What this interaction is unknown, but future refinement of the model, challenged with other datasets, may lead to its discovery

GAP between knowledge and skills for the implementation of the ACCM/PALS septic shock guidelines in India: Is the bridge too far?

Objective: To determine whether physicians were aware of and had the skills to implement the American College of Critical Care Medicine/Pediatric Advanced Life Support Course septic shock protocol. Design: A cross-sectional questionnaire survey. Setting: Four academic institutions in Chennai, Manipal, Mangalore, and Trivandrum - cities representing the three southern states of Tamil Nadu, Karnataka, and Kerala, respectively, between February and April 2006. Interventions: Pre and post lecture questions. They were evaluated using 11 questions testing knowledge and 10 questions testing their comfort level in performing interventions related to the initial resuscitation in septic shock. Measurement and Main Result: The ACCM/PALS sepsis guidelines were taught during the PALS course conducted in the four academic institutions. A total of 118 delegates participated, of whom 114 (97%) were pediatricians and four (3%) were anaesthetists. The overall mean number of correct responses for the 11 questions testing knowledge before and after the lecture was 2.1 and 4.07, respectively P=0.001(paired t test). Although, 42% of the respondents (n=50) were aware of the ACCM guidelines, 88% (n=104) did not adhere to it in their practice. A total of 86% (n=101) and 66% (n=78) did not feel comfortable titrating inotropes or intubating in the ED; 78% (n=92) and 67% (n=78), respectively felt that central venous access (CVA) and arterial pressure (AP) monitoring were unimportant in the management of fluid refractory shock. Of the physicians, 20% (n=24) had never intubated a patient, 78% (n=92) had not introduced a central venous catheter, and 76% (n=90) had never introduced an intra-arterial catheter. Conclusions: In view of the lack of skills and suboptimal knowledge, the ACCM/PALS sepsis guidelines may be inappropriate in its current format in the Indian setting. More emphasis needs to be placed on educating community pediatricians with a simpler clinical protocol, which has the potential to save many more children

Statistical comparison of possible causes of competition.

<p>Marginal against maximum likelihoods on a scale of the all-cause model and all single-cause models. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003416#pcbi-1003416-t001" target="_blank">Table 1</a>. As all mice are independent, the marginal and maximum likelihoods of a model are summed over all mice in all treatment groups. Competitive suppression of the AS clone by the AJ clone can be solely explained by differences in the parameter (Hypothesis H₅). No other single cause of competition adequately predicts the data. Circles show mean, and error bars show 2 standard errors from 5 independent fits.</p

Estimates of , in reconstituted (6 mice), nude (7 mice) and wildtype (6 mice) in single and mixed infections. Bars represent the means of the means of the marginal posteriors.

<p>Error bars represent 1 standard error.</p

Model fits to parasite densities.

<p>Fits of the single-cause model (H₅) with clone-specific to AS (green) and AJ (blue) parasite densities in reconstituted (top panels), nude (middle panels) and wildtype (bottom panels) mice during mixed infections. Crosses are data. The solid lines give the median fits. Grey regions correspond to the 95% posterior intervals of model uncertainty. These plots show that the model fits the data quite well for each individual in all treatment groups.</p

Assessment of functional forms of the adaptive immune responses.

1<p>Mean2 standard errors of five independent fits.</p

Model fits to RBC densities.

<p>Fits of the single-cause model (H₅) with clone-specific to RBC densities in reconstituted (top panels), nude (middle panels) and wildtype (bottom panels) mice during mixed infections. Crosses are data. The solid lines give the median fits. Grey regions correspond to the posterior intervals of model uncertainty. These plots show that the model fits the data quite well for each individual in all treatment groups.</p