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Related Experiment Video

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Algorithms to predict cerebral malaria in murine models using the SHIRPA protocol.

Yuri C Martins1, Guilherme L Werneck, Leonardo J Carvalho

  • 1Laboratório de Pesquisas em Malária, Instituto Oswaldo Cruz, FIOCRUZ, Brasil, 4365 - Manguinhos, Cep: 21045-900 - Rio de Janeiro - RJ, Brasil. yuri@ioc.fiocruz.br

Malaria Journal
|March 26, 2010
PubMed
Summary

Predictive algorithms accurately forecast cerebral malaria (CM) in mice, enabling early intervention and improving research reliability. These tools forecast CM development and time of death up to 72 hours in advance.

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Area of Science:

  • * Parasitology and tropical diseases
  • * Neuroscience and neuroimmunology
  • * Biostatistics and predictive modeling

Background:

  • * Plasmodium berghei ANKA infection in C57Bl/6 mice serves as a model for human cerebral malaria (CM).
  • * Experimental CM incidence is variable (50-100%) and unpredictable, complicating research into early pathological changes and outcomes.
  • * Developing reliable predictive algorithms for CM is crucial for advancing research and understanding disease progression.

Purpose of the Study:

  • * To develop and validate algorithms for predicting the onset and timing of cerebral malaria (CM) in a murine model.
  • * To improve the predictability of CM development in experimental settings.
  • * To provide a tool for researchers to better understand the causal relationship between early changes and CM outcomes.

Main Methods:

  • * Seventy-eight mice infected with Plasmodium berghei ANKA were assessed daily using the primary SHIRPA protocol.
  • * Mice were categorized as CM+ or CM- based on neurological signs observed between days 6-12 post-infection.
  • * Logistic regression models were constructed using SHIRPA test results and parasitaemia data to predict CM.

Main Results:

  • * Overall CM incidence was 54%, occurring between days 6-10 post-infection.
  • * Predictive algorithms demonstrated high performance (Area Under the Receiver Operator Characteristic curve [au]ROC > 80%, Positive Predictive Value [PV+] > 95%).
  • * Algorithms accurately predicted the time of death due to CM 24-72 hours in advance (au)ROC = 77-93%; PV+ = 100% at high cutoff values), with slight improvement upon inclusion of parasitaemia data.

Conclusions:

  • * Developed algorithms utilize data from a simple, rapid, reproducible, and cost-effective protocol.
  • * These algorithms enable very early prediction of CM development and estimation of time of death.
  • * The predictive tools offer significant value for research utilizing CM murine models.