Awards

Christopher Love with Hadley Sikes of the Massachusetts Institute of Technology in the U.S. will develop a biomanufacturing platform for low-cost production of monoclonal antibodies based on a multidomain synthetic protein enabling both capture and purification of the antibody in a chromatography-free process. The synthetic protein will concentrate and recover antibodies in a single, mobile fluid phase, based on studies of the liquid-liquid phase transition of proteins into condensates that occur naturally in key cellular processes. They will design and test protein agents for affinity-based capture and condensation of monoclonal antibodies including the antimalarial MAM01, assess the co-expression of the synthetic protein and the target antibody product in a microbial expression system, and determine conditions for continuous recovery of the product. They will also create models of the technical and economic factors required for low-cost production from either microbial or mammalian cell expression systems.

This grant is one of three grants that are funded and administered by LifeArc.

The team at Isolere Bio, a Donaldson Life Sciences business in the U.S. will develop a biomanufacturing platform for low-cost production of monoclonal antibodies based on a multidomain synthetic protein enabling both capture and purification of the antibody in a chromatography-free process. The synthetic protein includes an antibody-binding affinity tag, and it enables liquid-liquid phase separation and selective concentration of the bound antibody. Key steps in the biomanufacturing process will be targeted to decrease costs and improve performance. This includes high-throughput experiments to identify conditions that improve the recycling of the synthetic protein, as well as tests to optimize the removal of viral contaminants during the purification process. Subsequently, larger-scale production will be piloted to identify the critical parameters required to scale up the platform.

Kelvin Lee of the University of Delaware in the U.S. will develop components of a biomanufacturing platform for low-cost production of monoclonal antibodies. The project will be implemented through the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) headquartered at the University of Delaware, with John Erickson of NIIMBL. It will focus on economic modeling, development of a mammalian cell line for expressing the antimalarial monoclonal antibody MAM01, and a head-to-head comparison of two antibody purification approaches, including isoelectric point purification (IPP) and continuous precipitation operations that reduce the number of process steps. Based on these tests, the platform components could be integrated into an end-to-end continuous processing system and co-developed with a next-generation facility design and regulatory strategy.

Antti Aalto with Pedro Gonçalves of VTT Technical Research Centre of Finland Ltd in Finland will develop a biomanufacturing platform for low-cost production of the antimalarial monoclonal antibody MAM01 using the filamentous fungus Trichoderma reesei as the protein expression system. They will create candidate production strains, incorporating different expression cassettes for synthetic MAM01 sequences and pairing them with different host strain genetic backgrounds optimized for expression. The resulting strains will be cultivated in small-scale bioreactors, testing multiple bioprocess conditions from cultivation through sequential steps for antibody capture and purification. They will create models of the technical and economic factors required for low-cost production, as well as an analysis of environmental impact, including water usage and waste generation of the production process, comparing this impact with available data for antibody production using mammalian cell culture systems.

This grant is one of three grants that are funded and administered by LifeArc.

Ashutosh Chilkoti of Duke University in the U.S. will develop a biomanufacturing platform for low-cost production of the antimalarial monoclonal antibody MAM01 based on a fusion protein enabling both capture and purification of the antibody in a chromatography-free process. The fusion protein will comprise an antibody-binding affinity tag fused to an elastin-like polypeptide (ELP) enabling liquid-liquid phase separation of the protein. It will be engineered to optimize its secretion by Chinese Hamster Ovary (CHO) cells and its reversible phase separation via its ELP domain. Protein co-expression strategies for antibody production will also be optimized, including comparing genomic integration of the fusion protein and MAM01 sequences in the same cell line versus in separate cell lines in the same bioreactor. These tests will be used to determine the purification strategy that maximizes MAM01 yield while minimizing process cost.

This grant is one of three grants that are funded and administered by LifeArc.

Anurag Rathore of the Indian Institute of Technology (IIT) Delhi with Abhishek Mathur of Enzene Biosciences Limited, both in India, will pilot test a continuous processing platform for monoclonal antibody biomanufacturing for its advantages compared to batch processing. The pilot will build on lessons from the platform operating at the Center of Excellence for Biopharmaceutical Technology at IIT, Delhi. It will demonstrate that the existing biomanufacturing platform in an academic setting can be scaled up in a commercial setting. It will validate the decreased cost of goods and increased production relative to batch manufacturing, and it will provide technical and economic data, with details on integrating operations from cell culture through final formulation into a seamless, automated process. This data will guide efforts to increase access and affordability of monoclonal antibody products by manufacturing them in low- and middle-income settings.

James Brown of Bondi Bio Pty Ltd with Jake Baum at UNSW Sydney, both in Australia, will develop a biomanufacturing platform for low-cost production of the antimalarial monoclonal antibody MAM01 using the photosynthetic cyanobacterium Synechococcus as the protein expression system. They will engineer a cyanobacterial strain to express MAM01, grow it in high-density batch cultivation, optimize cell lysis and clarification to ensure maximum product yield and integrity, and purify fully assembled MAM01 by standard column chromatography. Purified MAM01 will be analyzed to confirm it has the correct mass, folding, and assembly, including complete disulfide bond formation and the expected glycosylation, and the strain will be engineered further where required. They will also use these experimental results to outline a facility design and an economic model for MAM01 biomanufacturing, focusing on the initial process steps of batch cultivation, centrifugation, and cell lysis.

Michael Betenbaugh with Honggang Cui of Johns Hopkins University in the U.S. will develop a biomanufacturing platform for low-cost production of the antimalarial monoclonal antibody MAM01, combining a fungal expression system with a nanofiber-bound peptide technology for antibody capture and purification. Collaborating with Dyadic International and with Thermo Fisher Scientific, they will optimize the fermentation media and bioprocess conditions in the expression system, which uses the thermophilic filamentous fungus Thermothelomyces heterothallica C1. They will also optimize conditions for the selective capture, separation, and recovery of the antibody along with recycling of the antibody-binding peptide. They will integrate these conditions, assess and further optimize them to reduce production costs, and demonstrate the scalability of the platform.

Cristiana Boi with Ruben Carbonell of North Carolina State University in the U.S. will develop a purification system for the antimalarial monoclonal antibody MAM01 that uses an all-membrane chromatography process with single-use membranes made from low-cost nonwoven materials. This system will be combined with a protein expression system using the thermophilic filamentous fungus Thermothelomyces heterothallica C1 to create a low-cost biomanufacturing platform. Collaborating with Dyadic International, they will obtain MAM01-containing supernatants from the expression system and analyze its components to guide development of the purification system. Based on these results, small-scale purification experiments will be performed, testing suitable membrane-coupled ligands, membrane configurations, purification conditions and steps, and integration of the system into single-use cassettes. They will also determine the process requirements to scale up the platform.

Vijayalakshmi Ramshankar of the Cancer Institute (WIA) in India will perform a study of air pollution's effects on lung cancer in India. To focus on the links specifically with air pollution they will recruit non-smoking lung cancer patients in the city of Chennai. They will screen these patients for EGFR driver mutations, known to promote air pollution-related lung cancer, and measure the cytokine and miRNA profiles in their blood through periodic sampling. They will also perform this blood analysis in patients' asymptomatic household family members, who will be offered further testing (low-dose spiral CT scanning) for early cancer detection. Air pollution will be assessed in these households using a device for continuously monitoring indoor exposure. They will perform statistical analysis combining the biological and environmental data to better understand how air pollution affects lung cancer risk and to identify a high-risk signature to guide early screening.

Ani Varghese of ZeroEarth Private Limited in India will pilot test a climate-smart dairy farming platform supporting rural women farmers in the Indian state of Tamil Nadu. Through partnerships with financial institutions, they will set up mechanisms facilitating farmers' access to credit to sustainably increase their farming income. They will launch a pilot business center, which will provide training in entrepreneurship and climate-smart farming practices; centralized access to veterinary services; and coordination between dairy, calf-rearing, and fodder farmers. Farming practices supported by the center will include those to improve the health of soil for growing fodder; to optimize feed to minimize greenhouse gas emissions and enhance milk quality; and to efficiently manage manure, with the launch of a biogas plant to generate electricity.

Satadal Saha of the Foundation for Innovations in Health in India will develop an AI-based platform to support public health interventions for women living in the Sundarban Biosphere Reserve, focusing on anemia, urogenital tract infections, and anxiety and depression. The Reserve is a river delta region highly susceptible to climate change-driven severe weather. The project will build on the team's existing digital platform for health data that supports community health workers deliver primary care to island communities. They will collect environmental data, including data for weather and air and water quality, and expand the platform with software enabling regular monitoring and integrated analysis of health and environmental data. This includes incorporating an AI-based predictive model to guide the proactive design and implementation of public health interventions for vulnerable women in this region.

Raghuram Dharmaraju of the I-Hub for Robotics and Autonomous Systems Innovation Foundation in India will improve heatwave forecasting using AI approaches and develop an early warning system for the Indian state of Karnataka to enhance preparedness for heat-induced health risks. The improvements in forecasting will encompass increases in accuracy, lead time, and spatial resolution. The early warning system will use web-based dashboards, mobile apps, and social media platforms to communicate heatwave alerts in local languages. It will include messages tailored to particularly vulnerable groups as well as alerts to healthcare providers to actively monitor these groups. It will also send notifications to relevant government agencies about the potential severity of health impacts. This system will guide public health interventions while helping establish data collection mechanisms for ongoing improvement of the system.

Tavpritesh Sethi of Indraprastha Institute of Information Technology Delhi in India will develop an AI-based platform to support responses to vector-borne diseases in the face of climate change in the Indian state of Uttar Pradesh. They will establish a comprehensive database that integrates climate data with data from existing programs for the control of vector-borne diseases (malaria, dengue, chikungunya, Zika, and Japanese encephalitis). Data will be at the block level of local government in Uttar Pradesh and will include real-time data. For analysis, they will develop an AI-based platform, named Sanketak, that includes modules to capture data, provide automated alerts, visualize changes in disease incidence, and identify early warning signs that predict disease hotspots. They will pilot test the platform, evaluating its potential to preempt, detect, and manage vector-borne disease outbreaks in a timely and effective manner.

Tavpritesh Sethi of Indraprastha Institute of Information Technology Delhi in India will develop an AI-based platform for AMR surveillance and management across a broad network of public and private hospitals in India. The platform will extract weekly data on AMR from the All India Institute of Medical Sciences, New Delhi (AIIMS Delhi) hospital and the Max Healthcare hospital network, including patterns of antibiotic prescriptions across the network. It will use a federated data analysis approach (joint analysis without sharing the data itself), and they will develop and integrate AI-based models to identify and predict trends in AMR. They will also create applications driven by these models to widely and effectively communicate the analyses to healthcare professionals. This will support antibiotic stewardship and data-driven AMR management at both the local and regional levels.

Sai Raj Reddy of Daia Tech Private Limited in India will develop a program to increase access to health education and related resources for adolescent girls in rural areas of the Indian state of Karnataka. The program will be developed in partnership with the Karnataka Health Promotion Trust, building on their ongoing work with local schools, healthcare providers, community leaders, and government agencies. After engaging community members to understand the local context, they will develop resources for adolescent girls including life skills courses and health education workshops and pilot test the program in selected villages. They will integrate AI tools across the program to broaden participation and to broaden the range of health outcomes improved for adolescent girls.

Chiranjay Mukhopadhyay of the Manipal Institute of Virology in India will perform a two-year longitudinal study in the Western Ghats region of India, focusing on sentinel surveillance of tick-borne pathogens and their transmission dynamics. This mountain range region is known for its high biological diversity, and they will sample across 12 sites representing diverse ecological habitats where people and wild and domestic animals interact most frequently. They will collect host-seeking ixodid ticks, screen them for eight tick-borne pathogen groups, and perform whole-genome sequencing for the pathogens identified. Corresponding weather data will be collected from the Indian Meteorological Department. They will combine this longitudinal data to develop statistical models that predict the spatial and temporal transmission of tick-borne pathogens and the corresponding disease risk, which will guide public health interventions.

Bruno Mmbando of Kampala International University in Tanzania will model the combined impact of malaria chemoprevention strategies and vaccines on the burden of childhood malaria in Africa. A modeling focus will be on determining the level of vaccine uptake at which chemoprevention strategies cease to be cost-effective in settings with moderate to high malaria transmission. Data modeling will include the short- and long-term effects of parasite antimalarial drug resistance on the combined impact of chemoprevention and vaccines. The multidisciplinary team will include data modelers, health economists, clinical trialists, and epidemiologists. They will work closely with malaria decision-making organizations, leading to tools and processes that better support the use of malaria data modeling to inform public health interventions.

This grant is funded by The Wellcome Trust.

Karthik Sasihithlu of the Indian Institute of Technology Bombay in India will map urban heat islands across India and test radiative cooling paint on building rooftops to reduce temperatures, heat-related illnesses, and energy consumption for cooling. Urban centers in India face extreme summer temperatures, worsened by the heat island effect in developed areas relative to rural areas and by heatwaves increasing in frequency because of climate change. Radiant cooling paint, because it does not require structurally modifying buildings, could be an affordable and sustainable intervention to reduce the negative health and economic impacts of heat. Heat islands will be mapped using satellite imagery, and radiative cooling paint will be tested in the severe heat islands identified.

Arup Ghosh of CSIR-Central Salt and Marine Chemicals Research Institute in India will design a feed additive for Indian cattle that combines algae and bacteria to reduce enteric methane emission, improve livestock health, and enhance agricultural productivity. From Indian sources, they will select seaweed, marine microalgae, and bacteria, screening them for their ability to inhibit methanogenesis in vitro and in the rumen of cattle. They will also assess their effect on the rumen microbiota and on cattle physiology and productivity. Candidate products made from these additives will be evaluated for their economic viability, including their potential for large-scale cost-effective production, storage, and distribution, as well as for their benefits to farmers relative to existing solutions.

Belehalli Pavan of Strideaide Private Limited in India will develop an AI-based platform to increase timely access to diagnosis and treatment for diabetic foot ailments. Early detection of peripheral neuropathy, peripheral arterial disease, and diabetic foot ulcers would enable interventions that reduce the need for limb amputation. The platform will build on their existing network of podiatry clinics located in a variety of public spaces. These clinics are staffed with a paramedic providing treatment guided by diagnostic tools including a foot mat with a plantar pressure sensor to help predict and assess foot-bottom ulceration. Existing and new digital data from these clinics will be used to build a diabetic foot registry, to improve automated assessment through AI-based analysis, and to train an LLM with a chatbot interface for clinical decision support across the podiatry clinics.

Amrita Mahale of ARMMAN in India will incorporate an LLM-based chatbot into the Kilkari mobile health service to answer questions about pregnancy and infant care. Kilkari currently provides weekly pre-recorded messages on preventive care, and it is implemented in partnership with India's Ministry of Health and Family Welfare. They will assess different available LLMs and train the best candidate with the Kilkari database of vetted content. They will pilot test the model with Kilkari users through WhatsApp, engaging users in Delhi and in the state of Jharkhand to encompass diverse participants spanning urban, rural, and tribal populations. The test will include regular surveys to measure the service's accuracy, relevance, and usability.

Mohanasankar Sivaprakasam of the Healthcare Technology Innovation Centre at the Indian Institute of Technology Madras in India will develop an AI-based platform to support diagnosis and report generation for endoscopic gastrointestinal exams. They will use curated datasets of annotated endoscopic images to develop an AI-based model for diagnosing gastrointestinal abnormalities, such as polyps and ulcers. The data will also be used to train a Large Language Model (LLM) to generate diagnostic reports of endoscopic exams including representative images and text descriptions. This will improve report quality and consistency for better diagnosis and more accurate, detailed patient records, with the report automation reducing the time and personnel required. Together, these platform components will improve patient care in gastroenterology, including more efficient care across more patients.

Ntombifikile Mtshali of Shout-It-Now with Elona Toska of the University of Cape Town, both in South Africa, will pilot test use of ChatGPT to provide information on sexual and reproductive health that helps young women and adolescent girls in South Africa make informed decisions and effectively access health services. They will test integration of the chatbot into Shout-It-Now's existing platforms: a tablet-based platform in mobile clinics staffed by young women and a mobile phone app. They will train ChatGPT to provide information on sensitive topics, including gender-based violence, HIV infection risk, and pregnancy, using existing materials and guided by a workshop with mobile clinic staff. Users' perception of the chatbot and the chatbot's effectiveness in increasing the use of health services will be assessed using on-line questionnaires and phone surveys across five demographically different districts.

Vijay Sai Trap of OnionDev Technologies Pvt. Ltd. in India will develop an AI-based platform to provide accurate and private automated answers to questions on sexual health and mental well-being for youth and women in rural India. They will generate a dataset of community-generated questions on these topics during a mental health awareness campaign in the Indian states of Uttar Pradesh, Madhya Pradesh, and Tamil Nadu. With the support of subject matter experts, they will add answers to these questions from the relevant literature. They will then create a master dataset of questions and answers, including translations using an existing LLM trained on local Indian languages. This dataset will be used to compare different AI-based models to identify the one best able to effectively answer questions on these sensitive topics.

Himanshu Sinha of the Indian Institute of Technology Madras in India will develop an LLM-based chatbot to provide personalized reliable guidance on antenatal care in multiple Indian languages, particularly for mothers without regular access to health care. The chatbot will use an open-source LLM and will be incorporated into a mobile phone application. The project will begin by surveying pregnant women seeking care in a variety of health care settings and across all three trimesters, asking them what features they would want in a pregnancy app. The LLM will be trained with information from textbooks, clinical manuals, government health resources, and guidelines from professional organizations. Initially the chatbot will function in Hindi and Tamil, with additional Indian languages to follow. The app will track pregnancy milestones and deliver relevant evidence-based advice.

Ananda Kumar Mishra of Cowbit Technologies Pvt Ltd in India will pilot test the Cowbit, a wearable device for real-time monitoring of dairy cow health. They will test the device across farms in different states in India, along with a mobile app in local languages for user-friendly readout of the data. The device's sensors will measure elements of cow health particularly relevant for guiding farm management. This includes measurements such as udder temperature for early diagnosis of mastitis and prompt veterinary treatment. It also includes behavioral measurements to identify cows without typical signs of estrus (silent heat) who are ready for insemination to maximize breeding efficiency. They will collaborate with farmers and other stakeholders to ensure the device is relevant for increasing farm productivity and has potential for broad uptake.

Kavitha Sairam of FIB-SOL Life Technologies Private Limited in India will develop mulch sheets that are biodegradable and can be tilled into the soil or composted. Mulch sheets can improve crop yields while reducing the need for irrigation and addition of agricultural chemicals. Traditional polyethylene mulch sheets, however, need to be removed each crop cycle, which is labor intensive, and they are a source of plastic pollution. To develop a biodegradable mulch product, they will explore various materials and fabrication technologies. They will characterize the physical and chemical properties of the candidate product and pilot test its performance and rate of degradation in the field with two different crops as compared to a commercial polyethylene sheet.

Anandkumar Naorem of the ICAR-Central Arid Zone Research Institute, Jodhpur in India will perform polyhouse farming experiments in the Indian state of Rajasthan to identify soil conditions that reduce emission of the greenhouse gas nitrous oxide. Polyhouses are greenhouse-style but enclosed in polyethylene rather than glass. They will grow tomato plants as a vegetable test crop, adjusting soil properties by varying factors including the type of mulch, irrigation frequency, the type of nitrogen compound added, and whether biochar is added. They will assess soil health through analysis of its physical, chemical, and biological properties, focusing on how they relate to nitrous oxide emission. The results will guide improvements in polyhouse farming particularly relevant as a sustainable agricultural strategy for arid regions.

Sujatha Sunil of the International Center of Genetic Engineering and Biotechnology in India will determine the temperature preference of Indian Aedes aegypti mosquitoes to build a mathematical model that predicts their prevalence across the country as well as hotspots for the arboviral diseases they transmit. To understand temperature preference, they will study laboratory-adapted Aedes aegypti, assessing their physiology and development in the laboratory across the mosquito life-cycle at a range of relevant temperatures. They will also sample mosquitoes across sites in Delhi, recording their physical and physiological features and their carriage of arboviruses, together with weather data from nearby weather stations and arboviral disease incidence data from nearby hospitals. The laboratory and field data will be integrated to build predictive models to identify areas in India most at risk of arboviral epidemics.

Sean Stowell of Brigham and Women's Hospital in the U.S. will analyze the human antibody repertoires targeting microbes in the female genital track (FGT) to guide the design and use of live biotherapeutic products for bacterial vaginosis. They will use their microarray platform, consisting of an array of antigens from FGT microbes, to analyze genital tract samples from a cohort of women in an HIV drug clinical study in South Africa. They will define the association between FGT antibody levels and specificity with FGT microbial colonization and inflammation. They will also perform experiments to explore potential mechanisms for antibody-mediated microbial attachment and colonization, focusing on antibody interactions with FGT mucin proteins. Together, the results will set the stage for using the microarray platform to identify patient-specific variables as biomarkers to predict the success of live biotherapeutic products.

Saliou Fall of the Institut Senegalais de Recherches Agricoles in Senegal will develop techniques to estimate biological nitrogen fixation (BNF) by legume crops to guide their use as alternatives to nitrogen fertilizers for more sustainable agriculture. They will assess BNF by estimating three underlying components. Crop biomass and the proportion that is nitrogen will be estimated by AI-based models, and the nitrogen fraction that comes from BNF will be estimated by measuring the levels of a stable isotope of nitrogen in the soil and in the plants. As test crops for data to train the AI models, they will grow groundnut and cowpea as staple legumes, with an adjacent non-nitrogen-fixing crop, and crotalaria as a cover crop. They will acquire images of the crops from drones or mobile phone applications, and perform laboratory analyses, including measuring biomass, analysis by near-infrared spectroscopy and wet chemistry, and measuring the natural isotope of nitrogen.

Daniel Kwaro of CREATES in Kenya will develop an early warning system for malaria outbreaks, floods, and heatwaves in Siaya County in Kenya, co-designing it with the local community. They will incorporate health and demographic surveillance system data, including a specific focus on maternal health indicators and birth outcomes, as well as data from automated weather stations, wearable devices, and mosquito traps. Through secondary data analyses, they will assess the probability and consequences of climate-related hazards, including identifying vulnerable communities, high-risk geographical areas, and occurrence patterns of climate-sensitive diseases. They will actively involve Siaya County residents, healthcare providers, and relevant local authorities in co-designing the early warning system paired with multiple mechanisms for communication to ensure the system is accessible and effective in responding to local needs.

Smit Gade of the Good Business Lab Foundation in India will perform a study in India to better understand the constraints for working mothers in accessing employer-provided childcare and the effects of increasing uptake of this childcare on working mothers and their children. They will perform a randomized controlled trial, recruiting sewing machine operators at a garment factory and unemployed women that will be offered job interviews at the factory. The factory offers free on-site childcare, but uptake is low. The trial arms will test the effect of subsidizing the cost of traveling with children to work, of providing information on the quality of the free creches at the factory, or of both combined. They will determine if the study treatments increase working mothers' uptake of childcare services and encourage unemployed women to interview for work. Trial outcome measures will include assessment of women's quality of life and of their children's welfare.

Avishek Banik of Presidency University in India will identify a bacterial consortium that can protect tea crops from Fusarium fungal pathogens. The global rise in temperature threatens tea crops in part through increased humidity that favors proliferation of disease-causing Fusarium. To develop a biocontrol strategy for these pathogens, they will isolate Fusarium species from tea plants in tea plantations in the Dooars region of West Bengal as well as bacteria growing on and in the plants. They will use these isolates to characterize the fungal disease process in high-humidity growth conditions in the laboratory and to screen for bacteria that can inhibit it. They will characterize the mechanism of inhibition, including analysis of plant gene regulation, to guide development of an antifungal bacterial product.

Anurag Agrawal of Ashoka University in India will develop an LLM-based platform to support medical decision making by home healthcare workers in India who are meeting the growing demand for home-based palliative care. The platform will use an existing proprietary LLM to extract and summarize relevant clinical information, connecting it with an existing open-source AI chatbot to generate advice in a conversational format for healthcare workers. They will test the platform using a dataset they will build of palliative care scenarios, focused initially on care for lung diseases, and they will compare outputs from several different open-source LLMs to guide the platform's final configuration. Expert clinicians will evaluate the clinical advice generated by the platform for its factual accuracy and relevance to the Indian sociocultural context.

Shraddha Jaybhave of DruFarm Technology Private Limited in India will test the DairyGuard wearable device for real-time monitoring of dairy cow health by small-scale farmers. They will test the system in dairy farms in the Indian states of Maharashtra and Gujarat. They will collect and analyze test data on cows, including identifying patterns in movement, feeding, rumination (cud chewing), and body temperature. They will validate the accuracy, reliability, and usefulness of the data and the associated alerts for farmers by comparing the monitoring system results to traditional livestock management methods and veterinary assessment. They will evaluate the impact of the monitoring system on participating farmers' productivity and income and identify opportunities to broaden its uptake and integration with existing agricultural extension services.

Aline Rocha of Fiocruz in Brazil will link datasets through the Center for Data Integration and Knowledge in Health (CIDACS) to measure the impact of extreme climate events on maternal and infant nutritional outcomes across diverse ecological settings and population groups in Brazil. They will integrate longitudinal data from two datasets, the 100 Million Brazilian Cohort and the Climate and Health Data Platform, connecting them through the municipality where mothers reside. The cohort database links data from social protection programs to administrative and health databases to assess the social determinants of health. The data platform extracts and links climate and environmental data from the year 2000 onwards from existing open-source databases. The integration of these two datasets will guide evidence-based programs to enhance the resilience of health services and mitigate the effects of climate change on maternal and child health, particularly for those most vulnerable.

This grant is funded by Grand Challenges Brazil.

Livia Casseb of Evandro Chagas Institute in Brazil will develop models to understand and predict the impact of climate change on the Aedes mosquito-transmitted arboviral diseases dengue, chikungunya, and Zika in Brazil. The models will integrate a variety of existing data for the different geographic regions of Brazil, including historical data on climate, landscape characteristics, population density, mosquito distribution, and public health. They will also incorporate structured and unstructured data from community networks, teaching and research institutions, and state government entities. The models will reveal interdependent relationships and interactions, including spatial correlations between the arboviral diseases over time. They will develop distinct models for individual geographic regions to serve as early warning systems for arboviral disease outbreaks and to guide local interventions.

This grant is funded by Grand Challenges Brazil.

Hernan Argibay of Fiocruz in Brazil will support a participatory research approach for communities in urban slums in Salvador, Brazil to develop and monitor the impact of interventions to reduce the risk of vector-borne and zoonotic diseases. Guided by local needs, new community-led projects will focus on environmentally transmitted diseases (e.g., leptospirosis and enteric infections) and vector-borne diseases (e.g., leishmaniasis, rickettsiosis, and those caused by the arboviruses dengue, chikungunya, and Zika), all of whom could increase in incidence due to climate change. Intervention projects will include environmental clean-up to reduce disease transmission by mosquitos and rats, planting to improve drainage and provide additional food sources, and using an app to map potential risk factors and guide new projects. They will measure intervention impact, including community-led pathogen surveillance using vector traps, water sampling, and metagenomic sequencing.

This grant is funded by Grand Challenges Brazil.

Andréia Santo of the Associação Redes de Desenvolvimento da Maré in Brazil will collect temperature, humidity, and air quality data together with associated health data for residents in the Maré favelas in Rio de Janeiro to better understand the causes of respiratory diseases and reduce their burden. They will also train high school girls as citizen scientists to work alongside health professionals in collecting and analyzing data and developing practical technologies to mitigate the health effects of heat and poor air quality. This participatory science approach will serve as a sustainable mechanism to understand the impacts of climate change on the health of particularly vulnerable communities in Brazil and to guide the development of innovative solutions. In selected residences in Maré, they will pilot an intervention consisting of a bio-concrete wall coating to reduce indoor relative humidity as a cause of heat stress for occupants.

This grant is funded by Grand Challenges Brazil.

Cari van Schalkwyk of Stellenbosch University in South Africa will expand and support the African community of polio and measles disease modelers. The work will be done through the South African Centre for Epidemiological Modelling and Analysis (SACEMA), which is hosted at Stellenbosch University. It will build on an ongoing fellowship program for health policy modeling by recruiting and supporting two new cohorts. Modelers will use available data to develop, validate, and package modeling methods for polio and measles that help African decision-makers assess the risk of outbreaks and the impact of vaccination programs. Additional work will target research goals that include developing enhanced methods to estimate population immunity to polio, comparing existing methods to determine how consistent they are, and extending ongoing modeling work evaluating wastewater-based surveillance of polio and measles as an early-warning signal for outbreaks and to monitor outbreak dynamics.

Sheetal Silal of the University of Cape Town in South Africa will establish a research consortium to analyze how climate change affects the transmission and control of mosquito-borne diseases, focusing on how to optimize interventions for malaria, chikungunya and dengue in Southern Africa. The consortium will integrate research projects led by local data scientists working closely with local decision-makers. Through mathematical and statistical modeling together with climate science, these projects will determine climate scenarios across time scales relevant for management of mosquito-borne diseases. These time scales will encompass short-term windows (6-12 months) as well as longer windows (5-10 years) relevant for policy planning and that incorporate the predicted impact and costs of new interventions. The consortium will also explore even longer windows (over 30 years) to provide predictions useful to initiate policy discussions and bring attention to the long-term implications of climate change on disease control strategies.

This grant is funded by The Wellcome Trust.

Corine Ngufor of the Centre de Recherche Entomologique de Cotonou in Benin will evaluate insecticide-based strategies that can complement insecticide-treated bed nets for improved malaria control. In the laboratory, they will test the killing ability of combinations of insecticides, using pyrethroid-susceptible as well as pyrethroid-resistant laboratory-maintained mosquitoes. In experimental hut trials, they will test different strategies, including a combination of a spatial repellent (a transfluthrin passive emanator) with dual active-ingredient bed nets that are two years into their three-year product lifespan. They will also use hut trials with controlled release of insecticide-susceptible and -resistant mosquitoes to determine how different strategies are affected by resistance and by environmental factors such as temperature and humidity. Data modeling will be performed to assess the relative importance of different variables, helping identify the most effective insecticide-based strategies to accomplish malaria control goals.

Roman Thibeaux of the Institut Pasteur de Nouvelle Calédonie in New Caledonia will examine how climate-driven soil changes and societal and behavioral factors can affect the incidence of leptospirosis to develop community-centered prevention strategies. The causal agent of the disease is the bacterium Leptospira, which can be found in water or soil contaminated with the urine of infected animals and thus can spread following heavy rainfall. Leptospirosis is endemic in the New Caledonia archipelago in the South Pacific, with potential climate-driven increases in incidence. Using soil microcosms in the laboratory, they will explore the effects of temperature, rainfall, and soil structure on Leptospira survival and dispersion. Through interviews and focus groups with New Caledonia community members together with ethnographic fieldwork, they will record local perceptions and knowledge relevant to leptospirosis and its transmission. In partnership with local community members and health authorities, they will then identify sustainable strategies to reduce leptospirosis incidence.

This grant is funded by the Pasteur Network.

Bryan Ranger of Boston College in the U.S. will develop a cost-effective, portable, and automated ultrasound tool to monitor nutritional health of young pregnant women in Ethiopia. The tool will incorporate AI models that guide users in collecting high quality data, so the tool can be used by frontline and community healthcare workers without extensive ultrasound training, and the models will use this data to predict metrics of nutritional status. In a pilot study conducted at the Jimma Medical center, they will create a database of ultrasound scans, anthropometry, body composition measured by gold standard techniques, and the associated clinical data from a group of young pregnant women. Ultrasound measurements will incorporate data on user position to identify the most informative positions via machine learning. They will survey clinical users to guide the ultimate design of the ultrasound system.

Susan Rumisha of Ifakara Health Institute in Tanzania will support the establishment of data modeling hubs in the Democratic Republic of Congo, Nigeria, and Tanzania, linking them into a collaborative network to guide the control of mosquito-borne diseases in the face of climate change. The focus will be on the direct and indirect effects of environmental change on malaria, modeling the interplay of these effects with public health systems and mosquito vector and disease patterns. This will encompass modeling mosquito vector distribution, abundance, and seasonality using historical climate data together with new microclimate information. The models will be designed to support national programs in prioritizing vector surveillance activities, targeting interventions, and developing early warning systems for emerging health threats. The network will strengthen model-building expertise and could be adapted to address mosquito-borne arboviral diseases.

This grant is funded by The Wellcome Trust.

Bernard Bett of the International Livestock Research Institute in Kenya will develop disease transmission models for two Aedes mosquito-borne arboviral diseases, dengue and chikungunya, and use the models to design decision support tools to guide surveillance and control of these diseases in Kenya, Somalia and Ethiopia. The models will be validated with longitudinal field data, including mosquito population density, infection patterns, blood meal sources, and the incidence of Aedes-borne diseases in humans. The models will be used to estimate important metrics for disease management, such as time-to-disease outbreak, cost effectiveness of control, and spatial distribution of risk. They will also help identify how the ecological tipping points for outbreaks of dengue and chikungunya compare to each other and how existing control measures for the two diseases could be integrated for better health outcomes. The project will link institutions including the Ethiopia Public Health Institute, Kenya’s Department of Disease Surveillance and Epidemic Response, Somalia’s Federal Ministry of Health, Jomo Kenyatta University of Agriculture and Technology, Abrar University, the Kenya Medical Research Institute, Ohio State University, Global One Health Initiative, and the International Livestock Research Institute.

This grant is funded by The Wellcome Trust.

James Beeson of Burnet Institute in Australia, Melissa Kapulu of Health Research Operations Kenya Limited in Kenya, Isaac Ssewanyana of Infectious Diseases Research Collaboration in Uganda, Faith Osier of Imperial College London in the U.K. and Pras Jagannathan of Stanford University in the U.S., will analyze clinical samples using an antibody functional assay platform with malaria antigen arrays to identify antigens targeted by protective antibodies for next-generation malaria vaccines. They will identify antigen-specific functional antibodies that strongly correlate with protective immunity to malaria observed in clinical studies with two populations: Kenyan adults after controlled experimental challenge infection with Plasmodium falciparum and children followed longitudinally who were naturally exposed in Uganda and in Papua New Guinea. They will then use biostatistical modeling approaches to identify antigen and functional antibody types that most frequently occur in protective combinations, identifying additive and synergistic combinations of responses and responses most predictive of protective immunity across age groups and populations. This will enable prioritization of antigens and their combinations for malaria vaccine candidates.

Rajshekhar Karpoormath of the University of KwaZulu-Natal in South Africa will test a set of potential anti-TB hit compounds against clinically relevant TB strains, using the results to generate optimized hit compounds for development of new anti-TB drugs. They will screen the potential hits against susceptible, monodrug-resistant, multidrug-resistant, and extensively drug-resistant TB strains as well as other Mycobacterium strains. The screening results will inform structure-based drug design to generate optimized hit compounds. Potential lead hits will be screened again, with the most promising evaluated against intracellular bacteria in macrophages, tested for in vitro cytotoxicity, and evaluated for mechanism of action in bioassays including carbon-isotope tracing metabolomics and an in vitro granuloma assay.