Bmc Med
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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. ⋯ 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.
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Cortisol, a steroid hormone frequently used as a biomarker of stress, is associated with cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM). To clarify whether cortisol causes these outcomes, we assessed the role of cortisol in ischemic heart disease (IHD), ischemic stroke, T2DM, and CVD risk factors using a bi-directional Mendelian randomization (MR) study. ⋯ Contrary to observational studies, genetically predicted cortisol was unrelated to IHD, ischemic stroke, T2DM, or CVD risk factors, or vice versa. Our MR results find no evidence that cortisol plays a role in cardiovascular risk, casting doubts on the cortisol-related pathway, although replication is warranted.
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In dengue-endemic countries, targeting limited control interventions to populations at risk of severe disease could enable increased efficiency. Individuals who have had their first (primary) dengue infection are at risk of developing more severe secondary disease, thus could be targeted for disease prevention. Currently, there is no reliable algorithm for determining primary and post-primary (infection with more than one flavivirus) status from a single serum sample. In this study, we developed and validated an immune status algorithm using single acute serum samples from reporting patients and investigated dengue immuno-epidemiological patterns across the Philippines. ⋯ Our dengue immune status algorithm can equip surveillance operations with the means to target dengue control efforts. The algorithm accurately identified primary dengue infections who are at risk of future severe disease.
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Patients with coeliac disease (CD) commonly report a variety of adverse symptoms to gluten, but descriptions of the symptomatic response in the literature may have been confounded by the presence of food components such as fermentable carbohydrates (FODMAPs) causing symptoms of irritable bowel syndrome independent of gluten. In recent unmasked and masked low FODMAP gluten challenge studies in small groups of treated CD patients, nausea and vomiting were shown to be the key symptoms associated with serum interleukin (IL)-2 release. Our objective was to utilise a large and diverse cohort of people with CD undertaking a standardised gluten food challenge to characterise the demographic, genetic and clinical factors influencing the severity and timing of acute gluten reactions and IL-2 production. ⋯ Gluten-induced symptoms and cytokine release is common in adults with treated CD. Age, genetics and previous response to gluten predict these acute reactions to gluten challenge. Structured symptom assessment and serum IL-2 after standardised gluten challenge may inform on patient diagnosis, the role of gluten in symptomatology and the need for adjunctive treatment.