James Armstrong, Bristol Medical School
Developing a framework for AI-guided chemotherapy
Cancer is a major healthcare challenge, and although there are many different drugs that can be used, we do not always select the right combination. With this funding from the Elizabeth Blackwell Institute, we will explore how artificial intelligence can be used to guide the selection of drugs for treating cancer.
Co-Investigators
Alessandro Masullo, Computer Science, Electrical & Electronic Engineering and Engineering Maths (SCEEM)
Emma Vincent, Bristol Medical School
Rodolfo Bezerra Nobrega, Geographical Sciences
COSPACE: CO-designing Sustainable Pathways to Adaptative Communities and Ecosystems in the Brazilian Amazon
This project aims to yield novel and fundamental insights into how ecosystems and traditional communities in the Amazon rainforest are vulnerable to climate change. Together with these communities, the team will co-create climate mitigation and adaptation solutions that are socially fair, locally acceptable, and predicted to impact human health positively.
Co-Investigator
Filipe Machado Franca, School of Biological Sciences
Chiara De Sio, School of Physics
TIDE: Temporal Impact on DNA Effects - Exploring Cell Survival in Radiation Experiments
The project is about measuring short-time effects and radiation damage to cells at DNA level. We irradiate cell cultures with two types of radiation (protons and photons) at different doses, to better understand the DNA damage and DNA repair mechanisms. This will improve our current simulations and, hopefully, treatment planning for localised cancer treatments such as brachytherapy and targeted alpha treatments.
Co-Investigators
Anu Goenka, School of Cellular and Molecular Medicine
Innovative human tonsil organoid system for unlocking mucosal immunogenicity of a nasal nanoparticle SARS-CoV-2 vaccine
This project will evaluate the human immune response to a new COVID-19 vaccine produced in Bristol, called the ADDoCOV. This vaccine could work well if it is administered through the mouth or nose. An 'organoid model' has been developed using cells from tonsils which are removed from patients at surgery in the hospital, and can measure their immune response after adding the ADDoCOV vaccine in the laboratory. It is hoped it will help open the door to future clinical trials as well as vaccine development for other infections.
Co-Investigators
Darryl Hill, School of Cellular and Molecular Medicine
Aerosol Survival of Antimicrobial Resistant Bacteria
Knowledge of how pathogens survive in exhaled aerosols is critical for understanding transmission, as demonstrated during the COVID-19 pandemic, ultimately this will lead to more effective mitigation strategies. Using cutting edge techniques developed in Bristol, this project aims to determine whether factors allowing bacteria to become resistant to antibiotics also facilitate their survival during transmission.
Co-Investigators
Yi Liu, Bristol Medical School
Data acquisition and pilot study on BioRxiv and MedRxiv full text data to facilitate comprehensive data mining on biomedical literature
The aim of this project is to curate and explore the rich and comprehensive information of biomedical literature from the BioRxiv and MedRxiv preprint servers, in order to investigate the under-explored areas in the mining of health science literature for follow-up projects in addressing public health research questions with data mining; and facilitate future joint research opportunities from collaborators with their own expertise.
Co-Investigator
Tom Gaunt, Bristol Medical School
Luis Moran Lara, School of Physiology, Pharmacology and Neuroscience
Comprehensive quantitative phosphoproteomics of platelet generation
Platelets are the smallest cell in the blood circulation and play an important role preventing bleeding and maintaining the vasculature integrity. When the platelet count is abnormally low it can be observed in different diseases, including cancer, and infectious diseases. These patients can suffer of a higher risk of bleeding, and severe cases highly rely on blood donations. Platelet generation in the laboratory is considered an extremely difficult task. New strategies, using different models to generate a significant number of platelets in the laboratory, would allow the team to identify the mechanisms involved in platelet formation, aiming to develop innovative strategies for treating patients who rely on platelet transfusions.
Co-Investigators
Tom Gaunt, Bristol Medical School
Samuel Okyere, Sociology, Politics and International Studies (SPAIS)
Strengthening the evidence base for responses to the effects of COVID-19 on sexual and reproductive health among Ghanaian women and youth
This project seeks to promote global health, gender equality and social justice by bringing together a multi-sectoral team of academics, policy makers, practitioners, and community leaders to co-develop a grant application for funding to study and articulate viable responses to the persistent impacts of COVID-19 on sexual and reproductive health (hereafter SRH) in Ghana.
Co-Investigators
Eleanora Baafour-Agyei, Cross sector partner - The Centre for Pregnancy and Childbirth Education (CePaCE)
Jim Spencer, Cellular and Molecular Medicine
Elucidating the binding target(s) of novel antibacterial G-quadruplex ligands
This project investigates the possibility of killing bacteria by targeting specific regions of their DNA with small molecules. New antibiotics for bacteria such as E. coli (the leading cause of bloodstream infections in the UK, which may be severe or life-threatening) are desperately needed. Molecules, such as those being studied as part of this project, that kill bacteria by a mechanism distinct from those of existing drugs offer a route to new antibiotics that may be less susceptible to known types of resistance.
Co-Investigator
Carmen Galan, School of Chemistry
Bangdong Zhi, Business School
Enhancing Hospital Capacity Management for Surge Demands by Integrating Machine Learning and Queueing Theory Approaches
Our project intends to predict and manage patient demand in hospitals during busy periods. By using advanced data analysis and machine learning, we can better understand how many patients to expect and allocate resources accordingly. This helps hospitals provide more efficient and timely care, improving overall health outcomes for patients.