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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
Background:
Plasmodium berghei ANKA infection in C57Bl/6 mice induces cerebral malaria (CM), which reproduces, to a large extent, the pathological features of human CM. However, experimental CM incidence is variable (50-100%) and the period of incidence may present a range as wide as 6-12 days post-infection. The poor predictability of which and when infected mice will develop CM can make it difficult to determine the causal relationship of early pathological changes and outcome. With the purpose of contributing to solving these problems, algorithms for CM prediction were built.
Methods:
Seventy-eight P. berghei-infected mice were daily evaluated using the primary SHIRPA protocol. Mice were classified as CM+ or CM- according to development of neurological signs on days 6-12 post-infection. Logistic regression was used to build predictive models for CM based on the results of SHIRPA tests and parasitaemia.
Results:
The overall CM incidence was 54% occurring on days 6-10. Some algorithms had a very good performance in predicting CM, with the area under the receiver operator characteristic ((au)ROC) curve > or = 80% and positive predictive values (PV+) > or = 95, and correctly predicted time of death due to CM between 24 and 72 hours before development of the neurological syndrome ((au)ROC = 77-93%; PV+ = 100% using high cut off values). Inclusion of parasitaemia data slightly improved algorithm performance.
Conclusion:
These algorithms work with data from a simple, inexpensive, reproducible and fast protocol. Most importantly, they can predict CM development very early, estimate time of death, and might be a valuable tool for research using CM murine models.
Insights
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.
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.
