摘要：A better understanding of disease-specific biomarker profiles during acute infections could guide the development of innovative diagnostic methods to differentiate between malaria and alternative causes of fever. We investigated autoantibody (AAb) profiles in febrile children (≤ 5 years) admitted to a hospital in rural Ghana. Serum samples from 30 children with a bacterial bloodstream infection and 35 children with Plasmodium falciparum malaria were analyzed using protein microarrays (Protoplex Immune Response Assay, ThermoFisher). A variable selection algorithm was applied to identify the smallest set of AAbs showing the best performance to classify malaria and bacteremia patients. The selection procedure identified 8 AAbs of which IFNGR2 and FBXW5 were selected in repeated model run. The classification error was 22%, which was mainly due to non-Typhi Salmonella (NTS) diagnoses being misclassified as malaria. Likewise, a cluster analysis grouped patients with NTS and malaria together, but separated malaria from non-NTS infections. Both current and recent malaria are a risk factor for NTS, therefore, a better understanding about the function of AAb in disease-specific immune responses is required in order to support their application for diagnostic purposes.
其他摘要：Abstract A better understanding of disease-specific biomarker profiles during acute infections could guide the development of innovative diagnostic methods to differentiate between malaria and alternative causes of fever. We investigated autoantibody (AAb) profiles in febrile children (≤ 5 years) admitted to a hospital in rural Ghana. Serum samples from 30 children with a bacterial bloodstream infection and 35 children with Plasmodium falciparum malaria were analyzed using protein microarrays (Protoplex Immune Response Assay, ThermoFisher). A variable selection algorithm was applied to identify the smallest set of AAbs showing the best performance to classify malaria and bacteremia patients. The selection procedure identified 8 AAbs of which IFNGR2 and FBXW5 were selected in repeated model run. The classification error was 22%, which was mainly due to non-Typhi Salmonella (NTS) diagnoses being misclassified as malaria. Likewise, a cluster analysis grouped patients with NTS and malaria together, but separated malaria from non-NTS infections. Both current and recent malaria are a risk factor for NTS, therefore, a better understanding about the function of AAb in disease-specific immune responses is required in order to support their application for diagnostic purposes.
