Infection remains an important cause of morbidity and mortality. Natural defenses to infection are mediated by intrinsic/innate and adaptive immune responses. While our understanding is considerable it is incomplete and emerging areas of research such as those related to the immune-metabolic axis are only beginning to be appreciated. There is increasing evidence showing a connection between immune signalling and the regulation of sterol and fatty acid metabolism. In particular, metabolic intermediates of cholesterol biosynthesis and its oxidized metabolites (oxysterols) have been shown to regulate adaptive immunity and inflammation and for innate immune signalling to regulate the dynamics of cholesterol synthesis and homeostasis. The side-chain oxidized oxysterols, 25-hydroxycholesterol (25HC) and vitamin D metabolites (vitamin D and vitamin D), are now known to impart physiologically profound effects on immune responses. Macrophages play a frontline role in this process connecting immunity, infection and lipid biology, and collaterally are a central target for infection by a wide range of pathogens including viruses and bacteria, especially intracellular bacteria such as mycobacteria. Clinical manifestations of disease severity in the infected host are likely to pay tribute to perturbations of the metabolic-immune phenomena found in lymphocytes and myeloid cells. Historically and consistent with this notion, vitamin D based oxysterols have had a long association with promoting clinical improvements to patients infected with Mycobacterium tuberculosis. Hence understanding the role of early metabolic mediators of inflammatory responses to infection in particular oxysterols, will aid in the development of urgently needed host directed therapeutic and diagnostic design innovation to combat adverse infection outcomes and antibiotic resistance.

The global burden of rheumatic heart disease (RHD) has triggered a World Health Organization response to drive forward development of a vaccine against the causative human pathogen Streptococcus pyogenes . This burden stems primarily from low- and middle-income settings where there are high levels of S. pyogenes skin and soft tissue infections, which can lead to RHD.

Background Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters. Methods We obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers. Results The multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location. Conclusion The study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings.