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Updated: Sep 13, 2025
Isolation and Analysis of Brain-sequestered Leukocytes from Plasmodium berghei ANKA-infected Mice
Published on: January 2, 2013
Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by Plasmodium
Peyton J Murin1, Cláudio Tadeu Daniel-Ribeiro2, Leonardo José Moura Carvalho2
1Department of Neurology, Saint Louis University School of Medicine, St. Louis, MO 63104, USA.
Background:
Experimental models using Plasmodium berghei ANKA (PbA)-infected mice have been essential for uncovering cerebral malaria (CM) pathogenesis. However, variability in experimental CM (ECM) incidence, onset, and mortality introduce challenges when analyses rely solely on infection day, which may reflect different disease stages among animals.
Methods:
We applied machine learning to predict ECM risk and onset in a cohort of 153 C57BL/6, 164 CBA, and 53 Swiss Webster mice. First, we fitted a logistic regression model to estimate the risk of ECM at any day using parasitemia data from day 1 to day 4. Next, we developed and trained a Random Forest Regressor model to predict the exact day of symptom onset.
Results:
A total of 64.5% of the cohort developed ECM, with onset ranging between 5 and 11 days. Early increases in parasitemia were strong predictors for the development of ECM, with an increase in parasitemia equal to or greater than 0.05 between day 1 and day 3 predicting the development of ECM with 97% sensitivity. The Random Forest model predicted the day of ECM onset with high precision (mean absolute error: 0.43, R2: 0.64).
Conclusion:
Parasitemia dynamics can effectively identify mice at high risk of ECM, enabling more accurate modeling of early pathological processes and improving the consistency of experimental analyses.
Insights
Machine learning accurately predicts experimental cerebral malaria (ECM) onset and risk in mice using early parasitemia data. This improves the consistency of experimental analyses in malaria research.
Area of Science:
- * Malaria research
- * Infectious disease modeling
- * Computational biology
Background:
- * Experimental cerebral malaria (ECM) models using *Plasmodium berghei* ANKA (PbA)-infected mice are crucial for understanding pathogenesis.
- * Variability in ECM incidence, onset, and mortality complicates analyses reliant on infection day.
- * Disease stage inconsistencies among animals hinder reliable experimental outcomes.
Purpose of the Study:
- * To develop machine learning models for predicting ECM risk and onset in mice.
- * To improve the accuracy and consistency of experimental analyses in malaria research.
- * To leverage parasitemia dynamics for early identification of high-risk individuals.
Main Methods:
- * Logistic regression model fitted to estimate ECM risk using parasitemia data from day 1 to day 4.
- * Random Forest Regressor model developed and trained to predict the exact day of ECM symptom onset.
- * Analysis conducted on a cohort of 153 C57BL/6, 164 CBA, and 53 Swiss Webster mice.
Main Results:
- * 64.5% of the mouse cohort developed ECM, with onset between days 5 and 11.
- * Early parasitemia increases (≥0.05 between days 1-3) predicted ECM development with 97% sensitivity.
- * The Random Forest model achieved high precision in predicting ECM onset (MAE: 0.43, R²: 0.64).
Conclusions:
- * Parasitemia dynamics are effective predictors of ECM risk in experimental models.
- * Machine learning models enhance the accuracy of predicting ECM onset and progression.
- * Improved predictive accuracy leads to more consistent and reliable experimental analyses in malaria research.
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