Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by <i>Plasmodium berghei</i> ANKA
Background: Experimental models using <i>Plasmodium berghei</i> 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...
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2025-07-01
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| author | Peyton J. Murin Cláudio Tadeu Daniel-Ribeiro Leonardo José Moura Carvalho Yuri Chaves Martins |
| author_facet | Peyton J. Murin Cláudio Tadeu Daniel-Ribeiro Leonardo José Moura Carvalho Yuri Chaves Martins |
| author_sort | Peyton J. Murin |
| collection | DOAJ |
| description | Background: Experimental models using <i>Plasmodium berghei</i> 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, R<sup>2</sup>: 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. |
| format | Article |
| id | doaj-art-e7bb075246aa4365880d27392721a348 |
| institution | Kabale University |
| issn | 2076-0817 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Pathogens |
| spelling | doaj-art-e7bb075246aa4365880d27392721a3482025-08-20T03:56:45ZengMDPI AGPathogens2076-08172025-07-0114767610.3390/pathogens14070676Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by <i>Plasmodium berghei</i> ANKAPeyton J. Murin0Cláudio Tadeu Daniel-Ribeiro1Leonardo José Moura Carvalho2Yuri Chaves Martins3Department of Neurology, Saint Louis University School of Medicine, St. Louis, MO 63104, USALaboratório de Pesquisa em Malária, Instituto Oswaldo Cruz and Centro de Pesquisa, Diagnóstico e Treinamento em Malária, Fundação Oswaldo Cruz, Rio de Janeiro 21040-360, RJ, BrazilLaboratório de Pesquisa em Malária, Instituto Oswaldo Cruz and Centro de Pesquisa, Diagnóstico e Treinamento em Malária, Fundação Oswaldo Cruz, Rio de Janeiro 21040-360, RJ, BrazilDepartment of Anesthesiology, Saint Louis University School of Medicine, St. Louis, MO 63110, USABackground: Experimental models using <i>Plasmodium berghei</i> 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, R<sup>2</sup>: 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.https://www.mdpi.com/2076-0817/14/7/676cerebral malaria<i>Plasmodium berghei</i> ANKAparasitemia dynamicsmachine learning predictionexperimental mouse model |
| spellingShingle | Peyton J. Murin Cláudio Tadeu Daniel-Ribeiro Leonardo José Moura Carvalho Yuri Chaves Martins Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by <i>Plasmodium berghei</i> ANKA Pathogens cerebral malaria <i>Plasmodium berghei</i> ANKA parasitemia dynamics machine learning prediction experimental mouse model |
| title | Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by <i>Plasmodium berghei</i> ANKA |
| title_full | Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by <i>Plasmodium berghei</i> ANKA |
| title_fullStr | Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by <i>Plasmodium berghei</i> ANKA |
| title_full_unstemmed | Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by <i>Plasmodium berghei</i> ANKA |
| title_short | Temporal Parasitemia Trends Predict Risk and Timing of Experimental Cerebral Malaria in Mice Infected by <i>Plasmodium berghei</i> ANKA |
| title_sort | temporal parasitemia trends predict risk and timing of experimental cerebral malaria in mice infected by i plasmodium berghei i anka |
| topic | cerebral malaria <i>Plasmodium berghei</i> ANKA parasitemia dynamics machine learning prediction experimental mouse model |
| url | https://www.mdpi.com/2076-0817/14/7/676 |
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