NIHRDataInsights/HRCSData huggingface dataset:
Title: Therapies for Long COVID in non-hospitalised individuals: From symptoms, patient-reported outcomes and immunology to targeted therapies (The TLC Study)
Funder: Medical Research Council

Abstract: This COVID-19 Long COVID award is jointly funded (50:50) between UKRI/Medical Research Council and the National Institute for Health Research. The figure displayed is the UKRI/MRC amount only, each partner is contributing equally towards the project so the Total Fund Amount is £2,257,157. Background Approximately 1 in 20 individuals with COVID-19 experience symptoms and impaired quality of life beyond 12 weeks (‘Long COVID’). Long COVID may comprise several distinct syndromes yet to be fully characterised. Aim: To evaluate symptoms and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals, to coproduce a remotely-delivered intervention, and to establish a virtual trial platform. Objectives and Methods WP1 Establish a representative population-based cohort of individuals with Long COVID. A representative population-based cohort of non-hospitalised individuals with Long COVID will be established using Clinical Practice Research Datalink (CPRD) primary care records. Individuals with a diagnosis of COVID-19 at least 12 weeks prior and matched controls will be identified. We will invite them to report symptoms/quality of life through the Aparito Atom5™ digital platform. WP2 Characterise and immunologically phenotype Long COVID syndromes. Machine learning clustering techniques will be used to identify distinct Long COVID syndromes. Individuals from each cluster(n=50) and controls will be invited to undertake biosampling to measure inflammatory markers, autoantibodies, and T cell function, and wearable devices to provide data on heart rate, oxygen saturation, physical activity and sleep quality to identify potential pharmacological and supportive therapies. WP3 Provide evidence-based recommendations on targeted pharmacological and supportive therapies for Long COVID syndromes. We will review existing evidence on post-viral inflammatory syndromes and existing evidence on Long COVID to prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulated evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers.WP4 Co-produce and evaluate a virtual targeted supportive intervention for Long COVID. A digital trial platform will be established to evaluate the effectiveness, cost-effectiveness, and acceptability of the coproduced virtual supportive intervention in a randomised clinical trial, and for future research.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;6.1 PHARMACEUTICALS;6.6 PSYCHOLOGICAL AND BEHAVIOURAL;7.1 INDIVIDUAL CARE NEEDS
Health categories: INFECTION
Title: Characterisation, determinants, mechanisms and consequences of the long-term effects of COVID-19: providing the evidence base for health care
Funder: Medical Research Council

Abstract: This COVID-19 Long COVID award is jointly funded (50:50) between UKRI/Medical Research Council and the National Institute for Health Research. The figure displayed is the UKRI/MRC amount only, each partner is contributing equally towards the project so the Total Fund Amount is £9,593,946. Long-term health consequences of C-19 (long-COVID) occur frequently. Most infections are not hospitalised; population studies are the place to understand individual and societal challenges of long-COVID. We will address the following questions: 1. How do we define and diagnose the sub-phenotypes of long-COVID? 2. What are the predictors of long-COVID, and what are the mechanisms of the sub-phenotypes? 3. What are the long-term health (physical and mental), and socioeconomic consequences? What factors enhance recovery? 4. What is the level of GP adherence to NICE diagnosis and management guidelines? Can a pop-up tool in medical records enhance adherence? We have an established consortium of experts and platforms uniting linked national primary care registries and population cohorts. The national coverage of primary care registries captures all individuals presenting to their GP, with linked prescribing, consulting, referral and outcome data. Many with long-COVID do not seek care. Population cohorts, with repeat C-19 related questionnaires, overcome this limitation. Further, the standardised pre-pandemic health data enables dissection of the effects of infection versus progression of co-morbidity. Questionnaires will identify long-COVID cases across cohorts. A subgroup of 200 cases will be matched to three sets of controls (C-19 +, long-COVID-), (C-19-, long-COVID+), and (C-19-, long-COVID-). They will wear a device capturing exercise capacity, heart rate and respiration, and complete regular online questionnaires on mental health and cognition. They will attend clinic for imaging to assess target organ damage. Qualitative work with people with long-COVID will inform diagnostic criteria and understanding of the lived experience. Parallel analysis of cohorts and registries will address each question. With NICE, we will quantify adherence to diagnostic and management guidelines in GP records, and pilot a pop-up intervention to enhance adherence. Our findings will enhance diagnostic criteria, identify pathways for bespoke sub-phenotype intervention, and inform plans for health service delivery.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;7.1 INDIVIDUAL CARE NEEDS;8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: INFECTION
Title: Non-hospitalised Children &amp; young people (CYP) with Long Covid (The CLoCk Study)
Funder: Medical Research Council

Abstract: This COVID-19 Long COVID award is jointly funded (50:50) between UKRI/Medical Research Council and the National Institute for Health Research. The figure displayed is the UKRI/MRC amount only, each partner is contributing equally towards the project so the Total Fund Amount is £1,869,949. Uncertainties relate to diagnosis, prevalence, duration and treatment of post-COVID syndrome (‘long COVID’) in children and young people (CYP). There is no diagnostic test nor definition for long COVID. We do not know the physical, psychological and social consequences of long COVID and we need to define the clinical phenotype and longer-term physical/physiological/psychological changes to target therapeutic interventions. Risk factors for acute COVID include: obesity, pre-existing co-morbidities, learning and neurological disabilities, mental health(MH) problems, BAME status. The CYP likely to be most at risk of long COVID are teenagers as they represent the majority of CYP pre-COVID with persistent physical and MH symptoms post-viral infection. Ludwigsson (2020) reported five COVID positive CYP with median age 12 yrs with symptoms longer than 6 months. The research questions: 1. To describe the clinical phenotype of post-COVID symptomatology in test-positive CYP, using test-negative comparators 2. Use these data to produce an operational definition of long COVID, necessary for any future epidemiological or interventional study 3. Use this definition to establish the prevalence and natural course of long COVID in CYP to inform NHS services and health policy HYPOTHESIS: the type, prevalence and trajectory of symptoms in COVID positive CYP will differ significantly from the comparator group, allowing us to construct an operational definition of long COVID. We plan a longitudinal cohort study of non-hospitalised CYP aged 11-17 yrs. We will approach 30,000 CYP, half of whom with proven COVID. We expect 6,000 to consent to help us. We will ask them whether they still have physical or mental health problems at 3, 6,12 and 24 months afterwards. The 3,000 CYP with a positive SARS-CoV-2 tests will be compared with 3,000 test-negative controls. DELIVERABLES: we will access large numbers with proven COVID quickly to generate early outputs to inform future intervention studies and health policy.


Research activity codes: 2.4 SURVEILLANCE AND DISTRIBUTION;4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: INFECTION
Title: Characterisation of a novel class of natural killer cell immunoevasin
Funder: Medical Research Council

Abstract: The aims of this project are to systematically evaluate the contribution of the interaction between HCMV UL1 and the KIR3D receptors to virus pathogenesis, through defining their impact on NK activation and expansion. Prior to this application, the genome and transcriptome of intact human cytomegalovirus (HCMV) strain Merlin was defined (with Davison, Glasgow). Bacterial Artificial Chromosome (BAC)-cloned Merlin DNA provides a stable source of infectious virus. Recombineering technology has been optimised to genetically modify Merlin, including generation of gene-specific tagged viruses and HCMVs with single gene deletions. A replication-deficient adenovirus (RAd) vector library expressing all HCMV ORFs was constructed and used for screening for KIR3DL1 interacting genes. A number of KIR reporter cell lines (KIR3DL1, KIR3DS1, KIR2DL3) were also generated and used for these screens. The HCMV deletion mutant (dUL1) has been generated, validated by whole genome sequencing, and used to confirm NK imodulatory functions. Several variants of UL1 have been cloned into replication-deficient adenovirus (RAd) and used to generate preliminary data. Soluble KIR3DL1 and UL1 were made and used in SPR analysis and soluble UL1 used to generate tetramers for flow cytometric analysis. NK cell techniques are well established, including NK cloning, ex vivo NK functional assays, multicolour phenotyping with an in-house 14-colour flow cytometer. An expansion assay has been developed that measures NK subset division through cell trace labelling. 50+ healthy donors have been recruited, including 15 that have contributed skin fibroblasts allowing autologous assays. An HLA-I negative HFFF cell line has been generated and allows greater differential for NK inhibition and NK expansion assays when combined with HCMV infection.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: INFECTION
Title: Microbiota Intervention Strategies Limiting Selection and Transmission of Antibiotic Resistance burden in the One Health domain
Funder: Medical Research Council

Abstract: Antimicrobial resistance (AMR) has become an endemic and increasing problem, partially under the radar and depriving future generations of effective therapies. A main AMR driver is antimicrobial overuse in humans, animals and environment, facilitating selection and spread of resistant bacteria and genes between the different One Health domains. The goal of MISTAR is to ameliorate the AMR pollution in "hothouses" (hospitals and farms) and thus, to curtail the selection and transmission of AMR within different One Health settings. We will develop and implement intervention strategies to promote microbiota preservation using cutting-edge omics technologies. To reach this goal we will design, implement and quantify (i) the effect of gut microbiota-based interventions (fecal microbiota transplantation and microbiota-based index at point-of care) in hospitalized patients to reduce AMR, (ii) the effects of interventions aimed at reducing airborne dustbound pollution and transmission of AMR and (iii) design and develop novel experimental intervention strategies to modulate the microbiota to eradicate AMR. MISTAR delivers important scientific knowledge, technical skills and new interventions to curtail AMR emergence that can be applied in both high- and low income countries. A targeted communication plan (scientific, high-quality grey literature and social media) will make the findings of MISTAR accessible to a wide range of stakeholders. Ultimately, the deliverables of this project will guide policymakers to implement novel strategies to control this Global Health challenge.


Research activity codes: 2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;5.1 PHARMACEUTICALS
Health categories: INFECTION
Title: Phage treatment and wetland technology as intervention strategy to prevent dissemination of antibiotic resistance in surface waters
Funder: Medical Research Council

Abstract: PhageLand aims to develop a novel intervention strategy combining the low-cost and eco-friendly purification capacity of passive treatment systems, namely constructed wetlands (CW), with a dedicated phage-based treatment to prevent the transmission of antibiotic resistance (AR) from wastewater into surface waters. PhageLand includes public health investigations targeting multidrug-resistant bacterial (ARB) pathogens in low-middle income countries (LMICs) in Eastern Europe. The phage-based treatment will be specifically tailored to eliminate these multidrug-resistant pathogens from wastewater. In parallel, PhageLand aims to assess: a) the self-purification capacity of model full-scale CWs operating in Spain and Moldova in removing antibiotic residues, ARB and antibiotic resistance genes (ARGs); and b) the potential risk associated with the dissemination of ARB and ARGs within indigenous bacterial communities and among animals inhabiting CWs. Experimental tasks will include field and laboratory experiments involving cutting-edge chemical, biotechnological and (meta)genomic techniques as well as animal testing. Finally, a pilot-scale infrastructure will be used to scale-up the PhageLand technology and to assess its performance under real environmental conditions. This proof-of-concept will be used to demonstrate the efficacy and scalability of this nature-based technology for combating AR and to encourage stakeholders for its implementation in wastewater treatment, particularly in LMICs where costly and power-demanding treatment plants are difficult to set up.


Research activity codes: 5.1 PHARMACEUTICALS
Health categories: INFECTION
Title: COMplex Biofilms and AMR Transmission
Funder: Medical Research Council

Abstract: COMBAT represents a multidisciplinary consortium which aims to understand and control AMR emergence and dissemination in complex biofilms. COMBAT will achieve this aim by gaining a better understanding of the nature of complex biofilms and their control in 3 One Health areas: healthcare, domiciliary and veterinary. There is currently limited understanding of the composition of complex biofilms, natural abundance of AMR genes and gene transfer occurrence in these ecological niches. With such a paucity of information, the efficacy of interventions (disinfection) in controlling the emergence and dissemination of AMR cannot be evaluated in situ, whilst evidence to date identified biofilms as a source for AMR that requires targeted countermeasures. By using a newly established small scale complex biofilm model, COMBAT will provide new information on microbial diversity, AMR gene abundance and transfer in complex biofilms of importance to 3 One Health areas. Further COMBAT will provide a robust platform to evaluate disinfectant product interventions designed to deliver a measurable impact in limiting AMR dissemination. In doing so, COMBAT will optimise cost-effective interventions appropriate to minimise the spread of AMR in 3 One health areas. COMBAT is composed of experienced research groups with significant international reputations in the fields of AMR and biocidal products. Industrial, infection control, veterinarian, farmer stakeholders will provide COMBAT with a translational aspect ensuring the outputs are effective and commercially viable.


Research activity codes: 2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT
Health categories: INFECTION
Title: Phage Therapy to Reduce AMR Enterobacteria Spread from a One Health Perspective
Funder: Medical Research Council

Abstract: The 'One Health' approach recognises the close connection of human and animal health and the environment. Multidrug resistant (MDR) pathogens, e.g. Salmonella and E. coli in poultry and related foods are increasingly becoming a global public health concern. Antibiotic use in animal husbandry has to be reduced to counteract spread of MDR plasmids among zoonotic pathogens in food-producing animals and the risk of transmission of MDR bacteria to humans and the environment. We will demonstrate the potential of a bacteriophage-based intervention to control and reduce the incidence of MDR zoonotic pathogens in broilers in a commercial farm environment and their release into environmental reservoirs. The use of an advanced targeted phage delivery system via animal feed will impact on reduced spread of MDR bacteria and relevant plasmids in broilers as well as their transmission to humans or to the environment. Conjugative plasmid-encoded pilus-specific phages will help to selectively target MDR plasmid-carrying bacteria without markedly affecting the overall intestinal microbiome composition. We will study the circulation of MDR plasmids among coliform enterobacteria and the effects of phage therapy, by initially using a novel ex-vivo chicken gut model and then on a chicken farm. For early detection, assessment of treatment or prevention of spread of MDR Salmonella or E. coli in broiler flocks, we will develop easy to use on-site detection tools based on PCR/LAMP/CRISPR-Cas assays for determining the presence of conjugative plasmids, antibiotic resistance genes and virulence markers.


Research activity codes: 2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT
Health categories: INFECTION
Title: FARM interventions to Control Antimicrobial ResistancE
Funder: Medical Research Council

Abstract: The project will develop innovative machine learning tools to identify predictors of stress and disease in new-born piglets. We will assess the relative merits of the different interventions from the perspective of the farmer and society using business case and cost-effectiveness analyses. Two different machine learning approaches will be used to identify high-risk piglets predisposed to Post-weaning diarrhoea (PWD) and other diseases with the intention of being able to separate them from healthy individuals and/or reduce their occurrence by breeding management. The first approach is based on facial feature analysis of stress in sows, whereas the second is based on integrated analysis of faecal metagenomics data and metadata collected longitudinally from individual piglets. In both approaches, data will be analysed retrospectively to identify behavioural and metagenomics markers that can be used to predict PWD and other diseases of interest. We will use machine learning to detect facial expressions in sows from image data captured during gestation and around farrowing to estimate the potential stress levels (as a percentage likelihood that a pig is stressed versus not stressed) the sows are experiencing. These estimates will then be correlated with their stress biomarkers and disease measurements (e.g. faecal scores) taken from their piglets. Similarly, metagenomics data will be integrated with collected metadata and analysed using a machine learning approach. A statistical model will be used to check for overdispersion and collinearity. The outcome of this task will be a score for how strongly these different measures are able to predict disease. The collaborative work on automated expression detection for biometrically identified animals, also has important wider application for observation and wellbeing monitoring of wildlife in the study of biological processes - a key component of NERC's remit in the UK.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: INFECTION
Title: Optimising community antibiotic use and infection control with behavioural interventions in rural Burkina Faso and DR Congo
Funder: Medical Research Council

Abstract: Emergence of antibiotic resistance (AMR) is a serious concern for Low and Middle Income Countries (LMICs). Unregulated use of antibiotics, a major AMR driver, is highly prevalent in LMICs, with medicine stores as key providers. Physical interactions between One Health compartments increase cross-domain transmission risks, although the relative importance of different reservoirs is uncertain, with community- level dynamics of AMR in LMICs largely unquantified. We will develop and evaluate a behavioural intervention bundle, targeting medicine stores and their communities, to optimise antibiotic use and improve hygiene, and hence reduce AMR prevalence and transmission. After a 6-month local co-development phase, the intervention will be implemented over 12 months in established health demographic surveillance sites in Burkina Faso and DR Congo with clinical microbiology facilities, and evaluated in a cluster RCT, comparing intervention and control villages. The primary outcome measure is the change in Watch antibiotic provision from medicine stores (where a formal prescription is not required), assessed via patient exit interviews and simulated client visits. Changes in hygiene practices and AMR pathogen and gene carriage will be assessed in repeated population surveys. Rodents, living in close proximity to humans in much of sub-Sahara Africa, provide a proxy estimate of environmental AMR pathogen and gene exposure. Using modelling and sequencing of selected human and rodent isolates, we will quantify how changes in antibiotic use and hygiene practices impact AMR transmission.


Research activity codes: 2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT;3.1 PRIMARY PREVENTION INTERVENTIONS TO MODIFY BEHAVIOURS OR PROMOTE WELL-BEING
Health categories: INFECTION
Title: Selecting Efficient Farm-level Antimicrobial Stewardship Interventions from a One Health perspective
Funder: Medical Research Council

Abstract: It is often postulated that interventions to prevent the development and transmission of antimicrobial resistance (AMR) should be targeted at the livestock sector. The potential costs to agriculture are often argued as "worth it" for the long-term human (and animal) health benefits. However, a quantification of the relative benefits and costs for each system is lacking. Through shared learning across partners and secondary use of existing data, we plan on answering the question 'What farm-level antimicrobial usage (AMU) interventions are most efficient at the national-level, given different scenarios of human health, AMU and AMR, for England, Denmark and Senegal?', defining efficiency as the optimisation of resource use, given a set budget and a set of desired outcomes. We will use statistical data analysis, transmission dynamic mathematical modelling and microeconomic economic modelling to construct a unified model that can assess (i) the interplay between animal, environment and human transmission of AMR, and (ii) the interplay between costs and benefits across One Health. The statistical data analysis will analyse the impact of previously implemented interventions to inform modelling of future interventions under different AMR scenarios. Settings, costs and benefits will be specifically defined via expert elicitation through a stakeholder-led 'knowledge hub'. This work will not only provide explicit impact estimates and ranking for a range of farm-level AMU interventions, it will also provide insight into uncertainty, highlighting where future research could be most valuable in understanding AMR intervention efficiency from a One Health perspective.


Research activity codes: 2.2 FACTORS RELATING TO PHYSICAL ENVIRONMENT
Health categories: INFECTION