Yingda Xie of Rutgers, The State University of NJ and JoAnne Flynn of the University of Pittsburgh, both in the U.S., will develop a non-invasive approach for testing candidate anti-tuberculosis compounds in animal models and patients using positron emission tomography-x-ray computed tomography (PET/CT). Tuberculosis (TB) is a leading cause of death in developing countries, and rates are sustained by the causative bacterium, Mycobacterium tuberculosis, developing resistance to current drugs. To circumvent this, new drugs are being designed to target human cells and proteins rather than those of the bacteria. To test these drugs, new tools are also needed to monitor TB in patients. 18 Fluorodeoxyglucose (FDG)-PET/CT is a non-invasive imaging tool that uses radioactively-labelled glucose to light up areas of metabolic activity in the body such as the lesions formed by M. tuberculosis and immune cells that play a critical role in infection. They have histopathological sections and cell and chemical data of TB lesions from non-human primate models and will use them to quantify the different lesions. Then, by using the available PET-CT scans of the lesions, they will search for quantitative signatures that can predict a specific type of lesion. The accuracy of these PET/CT signatures will be tested in a separate group of animals. Their study will reveal details of the TB immune response across different lesions, which could help design new treatments, and the signatures can be used to test the activity of new drug candidates in animal models and humans.

Paul de Figueiredo and Daniel Alge of Texas A&M University in the U.S. will develop a portable, disposable bioreactor for the low-cost production of gut microbial biotherapeutics at an estimated $0.09 per dose in low-resource settings. Dysfunction of the human gut microbiome is a common consequence of malnutrition in poor countries. It may be effectively treated with live biotherapeutics, yet current production methods are complicated and expensive. Glucose oxidase consumes oxygen as a co-substrate in glucose oxidation and has been shown to create hypoxic microenvironments in vitro, similar to that in the human gastrointestinal tract. They will engineer an inexpensive bioreactor by immobilizing glucose oxidase in a hydrogel placed in dialysis tubing and incubated in liquid media; the glucose oxidation reaction will deplete the bioreactor of oxygen and create an oxygen gradient to mimic the intestinal lumen. This will enable growth of a consortium of anaerobic bacteria, after which the microparticles will be removed by filtration. They will optimize the system using an artificial consortium of at least ten strains of common gut bacteria.

Jessica Manning and Fabiano Oliveira of the National Institute of Allergy and Infectious Diseases along with Daniel Parker of the University of California Irvine, all in the U.S., will develop a combinatorial approach to better define the incidence of vector-borne diseases in Cambodia in order to limit their transmission. The global incidence of vector-borne diseases such as malaria and dengue are increasing globally. However, accurately defining the disease-burden is challenging, particularly in resource-poor settings where diagnostics and expertise are limited. They will perform an exploratory clinical study in a peri-urban referral hospital with a catchment area of over 60,000 people in Kampong Speu Province, Cambodia, using a combination of approaches to define the high-resolution landscape of vector-borne diseases in the heart of the Greater Mekong Subregion. The study will involve next generation sequencing to identify pathogens in prospectively collected samples from infants with fever in their existing pediatric cohort as well as in new samples to be collected from febrile individuals aged between 0 and 45 arriving at the hospital; ELISA to generate antibody reactivity profiles of vector saliva, which plays a critical but often overlooked role in disease transmission; and geolocating cases using open-source (Quantum GIS) software. These data will be used to model transmission risk and identify target areas for interventions.

Joyce Nakatumba-Nabende of Makerere University in Uganda will use artificial intelligence to mine data from local village radio stations to generate timely data on crop pests and disease in sub-Saharan Africa. Crop loss due to pests and disease threatens the economic survival of smallholder farmers, and access to surveillance data is critically important yet often unaffordable. Local radio shows are a powerful source of information flow in rural African villages: they cover topics including politics, policy, climate, and social circumstances, in addition to crop concerns. Collectively, this information provides a holistic representation of current events in these communities. They will analyze local broadcasts to generate crop surveillance data that is linked to the local community situation. Radio content will be collected at low cost through a collaboration with Pulse Labs Kampala, and they will build artificial intelligence models based on deep neural networks and keyword identification to mine the data. The results will be combined with photographs of diseased crops provided by local farmers and used to train machine learning models to ultimately extract radio information in multiple languages and with diverse accents. This project will provide near real-time crop surveillance data and allow for timely responses to threats.