A 青瓜视频1m funding award from the Medical Research Council in collaboration with UKRI Innovate UK will accompany 青瓜视频1.2 million of in-kind support from 85 partners to fund the pilot phase of the UK Centre of Excellence on In-Silico Regulatory Science and Innovation (UK CEiRSI). This Centre will collaborate globally to address some of the sector's most pressing challenges and target unmet patient outcomes and safety needs. 

The consortium will work with computational modelling and simulation and AI techniques青瓜视频攁ll of which are poised to revolutionise the healthcare landscape. The UK CEiRSI will contribute to making the UK the best milieu for delivering medical innovations using in silico evidence and regulatory science. 

The Centre will consist of leading universities from the UK青瓜视频檚 four nations, world-class companies, and health systems and regulatory bodies, including the UK青瓜视频檚 Medicines and Healthcare Products Regulatory Agency (MHRA), National Institute for Health and Care Excellence (NICE) and the Health Research Authority (HRA) but will also collaborate with colleagues in the Food and Drug Administration (FDA) in the US, and the European Medicines Agency (EMA) in mainland Europe.

Professor Alex Frangi, Bicentennial Turing Chair in Computational Medicine at The University of Manchester, will direct the Centre.

He said: 青瓜视频淗uman and animal trials often face high failure rates resulting in delays, increased costs, and potential risks to patients.

青瓜视频淥n average, pharmaceutical products take 12  years to develop, with testing consuming up to 30% of costs.

青瓜视频淗owever, we will seek to address these critical inefficiencies by developing in-silico technologies that produce digital evidence for the digital age. Our aim is to reflect engineering practices in other sectors where physical testing is complemented by virtual testing and product optimisation. This will result in improved medical products (drugs or devices), faster and more affordable lifesaving therapies for patients, and innovative regulatory approval processes.青瓜视频�

He added: 青瓜视频淭hese cutting-edge tools can greatly enhance reliability in testing, while substantially reducing development time and costs, as well as improving the diversity of testing conditions, leading to more equitable care.青瓜视频�

青瓜视频淎nd that will benefit patients through reduced failure rates and recalls, while fostering economic growth by driving innovation in pharmaceuticals and medical technologies.青瓜视频�

However, despite their transformative potential, a regulatory deadlock for in-silico technologies means the technologies face barriers to adoption. Regulators lack frameworks to assess in-silico evidence, while developers hesitate to invest without clear pathways to approval.

The UK CEiRSI aims to break the deadlock and position in-silico technology and virtual trials as a mainstream approach to eliminate risk from future medical and pharmaceutical innovation developments. To tackle this impasse, the Pilot phase will implement an In Silico Airlock Initiative where actors from industry, academia and regulatory bodies will explore 10 industry-led pre-commercial regulatory pilots and assess the opportunities and limitations of current credibility frameworks.

Building on the success of a six-month discovery phase, UK CEiRSI will bring together industry leaders, regulators, Health Technology Assessment (HTA) and standardisation bodies, academics, and patient representatives - to test and refine frameworks for assessing in-silico evidence.

Reports from the project will address key issues such as regulatory frameworks, legal and ethical implications, and patient risk reduction, paving the way for in-silico technologies to make a real impact on our lives.

• "in silico"  is a term used to describe experiments or studies that are performed using computer simulations or software. 

Led by researchers at The University of Manchester and Manchester University NHS Foundation Trust (MFT), alongside researchers in Australia and United States, the study will enable researchers to track changes in brain structure over time in children and young people with NF1.

The research is funded by a 青瓜视频2.2 million award from the US Department of Defence and is the largest investigation into brain development in NF1 to date. Using advanced machine-learning techniques, the team will analyse the brain structure of over 10,000 MRI scans, comparing them to healthy individuals of the same age.

By doing that, they will shine a light on how specific genetic changes affect the brain and how alterations in brain structure may predict learning difficulties outcomes.

The Children青瓜视频檚 Hospital of Philadelphia, the Murdoch Research Institute in Melbourne and the Complex NF1 Service hosted by the Manchester Centre for Genomic Medicine at Saint Mary青瓜视频檚 Hospital, part of MFT, which is a world leading centre for clinical care and research in NF1, have all signed up to the project.

NF1 affects approximately 1 in 2,500 children. Although the severity of the condition varies from person to person, about half of all children affected by the condition may have difficulties with learning, autism or ADHD.

Dr Shruti Garg, Senior Lecturer at The University of Manchester and National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (BRC) Mental Health Theme Capacity Development Lead, is leading the international research.

Dr Garg, who is also Honorary Consultant Child and Adolescent Psychiatrist at the Royal Manchester Children青瓜视频檚 Hospital, part of MFT said: 青瓜视频淟earning and behavioural difficulties in NF1 can profoundly impact the quality of lives of affected children and young people. This funding provides a crucial opportunity for researchers to deepen our understanding of how changes in the NF1 gene impact brain development.

青瓜视频淛ust like 青瓜视频榞rowth-charts青瓜视频� are widely used to monitor children青瓜视频檚 physical growth, our research will enable us to create NF1-specific 青瓜视频榖rain charts青瓜视频� to serve as a reference for age-related changes in brain structure.青瓜视频�

Dr Nils Muhlert, Senior Lecturer in Psychology and Neuroanatomy at the University of Manchester said: 青瓜视频淭his project is a powerful illustration of collaboration across the world, and we are tremendously excited about what it might achieve.青瓜视频�

Karen Cockburn, Charity Director of Nerve Tumours UK, said: "We fully endorse this extremely important global project, and the work of Dr Shruti Garg, who is also a member of the charity's Medical Advisory Board. This research and its potential findings will be of huge benefit for the NF1 community.青瓜视频�

Dr Grace Vassallo, Consultant Paediatric Neurologist and Clinical Lead for the Complex NF1 Service at the Manchester Centre for Genomic Centre for Medicine at Saint Mary青瓜视频檚 Hospital, said: 青瓜视频淲e are incredibly grateful for this unique opportunity to collaborate in cutting edge research into the developing NF1 Brain charts which will in future improve the clinical care for children and young people with NF1.青瓜视频�

• Feature-Enhanced Representation with Transformers for Multi-View Stereo 
• High-Frequency Channel Attention and Contrastive Learning for Image Super-Resolution 
• A Divide-and-Conquer Machine Learning Approach for Modelling Turbulent Flows 
•  
• DRLFluent: A distributed co-simulation framework coupling deep reinforcement learning with Ansys-Fluent on high-performance computing systems 
• Manifold-enhanced CycleGAN for facial expression synthesis 

To discuss this research or potential partnerships, contact Professor Yin at hujun.yin@manchester.ac.uk.
 

The funding package from the University青瓜视频檚 Strategic Investment Reserve Fund (SIRF) is being matched by 青瓜视频2 million from the Faculty itself. The investment will go towards appointing an interdisciplinary team of six senior lecturer or lecturer-level academics, six post-doctoral research associates and six PhD students. They will form a cross-cutting research cluster with the (CDTS) at the University.

The investment will also leverage further research and industry funding, and help develop new teaching and executive education programmes, strengthening the University青瓜视频檚 capability in ethical and responsible AI.

Professor Fiona Devine, Vice-President and Dean of the Faculty of Humanities, said: 青瓜视频淚 am absolutely delighted that the Faculty has been successful in securing this funding to significantly expand and enhance our research and teaching capabilities in this emerging field. The investment is designed to retain our status as a UK leader in cyber security and responsible AI research and teaching.青瓜视频�

Richard Allmendinger, Professor of Applied Artificial Intelligence at Alliance Manchester Business School (AMBS), and Faculty Associate Dean for Business Engagement, Civic and Cultural Partnerships, said: 青瓜视频淭his investment comes at a critical juncture and gives the Faculty of Humanities a critical mass in social science-led approaches to AI which will enable us to maximise external research funding opportunities.

青瓜视频淭he demand from industry is clear. International partners wish to collaborate on issues of AI governance and responsible AI, as do various strategic partners. As a city-region, Manchester also has the by number of jobs outside London.青瓜视频�

Professor Nick Lord, Director of the CDTS, and Professor of Criminology in the School of Social Sciences, added: 青瓜视频淎I is already having a profound effect on society and will continue to do so, and that means impacting everything we do as a University, too. To mitigate risks and ensure the benefits of AI technologies we must consider the social, environmental and economic contexts they will operate in, and the consequences of their deployment.

青瓜视频淭here is an urgent need to drive approaches to AI that are secure, safe, reliable and trustworthy. It is also vital that they operate in a way that enables us to understand and investigate when they fail.青瓜视频�

Enhancing Faculty of Humanities research power in AI trust and security will also catalyse new collaborations with the Faculty of Biology, Medicine and Health at the University, most notably with the for health technology research and innovation.

Added Professor Devine: 青瓜视频淭he complexity and rise of data in healthcare means that AI will increasingly be applied within the field and has the potential to speed up diagnostics and make healthcare operations more efficient.

青瓜视频�Humanities research has much to contribute to this truly inter-disciplinary agenda and this investment will establish the University of Manchester as a leader in ethical, assessable, inclusive and responsible AI. It aligns not only with our commitment to cutting-edge research and innovation but also with our commitment to social responsibility.青瓜视频�

The AI Trust and Security team will form a cross-cutting research cluster within the CDTS. The new initiative follows the recent announcement that the University of Manchester was awarded the status of by the National Cyber Security Centre and the Engineering and Physical Sciences Research Council. The Centre is distinctive as it is the only cyber and digital security and trust research centre in the UK led from social science, rather than computer science or engineering.

Meanwhile, demand for new teaching programmes in the area of AI is also soaring, as demonstrated by the recent review of the .

Data from April 2020 to March 2023 shows 7,600 students have enrolled on AI and data science postgraduate conversion courses across the UK, helping to address a critical digital skills gap in the AI and data science industries.

The University of Manchester has been awarded 青瓜视频30 million funding by the Engineering and Physical Sciences Research Council (EPSRC) for four Centres for Doctoral Training as part of the UK Research and Innovation青瓜视频檚 (UKRI) 青瓜视频500 million investment in engineering and physical sciences doctoral skills across the UK.

Building on Manchester青瓜视频檚 long-standing record of sustained support for doctoral training, the new CDTs will boost UK expertise in critical areas such as advanced materials, AI, and nuclear energy.

The CDTs include:

• EPSRC Centre for Doctoral Training in 2D Materials of Tomorrow (2DMoT) - with cross-disciplinary research in the science and applications of two-dimensional materials, this CDT will focus on a new class of advanced materials with potential to transform modern technologies, from clean energy to quantum engineering. Led by , Professor of Physics at The University of Manchester.
   
• EPSRC Centre for Doctoral Training Developing National Capability for Materials 4.0 - this CDT will bring together students from a range of backgrounds in science and engineering to drive forward the digitalisation of materials research and innovation. Led by , Professor of Applied Mathematics at The University of Manchester and the Henry Royce Institute.
   
• EPSRC Centre for Doctoral Training in Robotics and AI for Net Zero - this CDT will train and develop the next generation of multi-disciplinary robotic systems engineers to help revolutionise lifecycle asset management, in support of the UK青瓜视频檚 Net Zero Strategy. Led by , Reader in the Department of Electrical and Electronic Engineering at The University of Manchester.
   
• EPSRC Centre for Doctoral Training in SATURN (Skills And Training Underpinning a Renaissance in Nuclear) - the primary aim of SATURN is to provide high quality research training in science and engineering, underpinning nuclear fission technology. Led by , Professor of Nuclear Chemistry at The University of Manchester.

Manchester received joint-third most awards across UK academia, and will partner with University of Cambridge, University of Glasgow, Imperial College London, Lancaster University, University of Leeds, University of Liverpool, University of Oxford, University of Sheffield, University of Strathclyde and the National Physical Laboratory to prepare the next generation of researchers, specialists and industry experts across a wide range of sectors and industries.

In addition to leading these four CDTs, The University of Manchester is also collaborating as a partner institution on the following CDTs:

• EPSRC Centre for Doctoral Training in Fusion Power, based at University of York.
• EPSRC Centre for Doctoral Training in Aerosol Science: Harnessing Aerosol Science for Improved Security, Resilience and Global Health, based at University of Bristol.
• EPSRC Centre for Doctoral Training in Compound Semiconductor Manufacturing, based at Cardiff University.

Along with institutional partnerships, all CDTs work with industrial partners, offering opportunities for students to develop their skills and knowledge in real-world environments which will produce a pipeline of highly skilled researchers ready to enter industry and take on sector challenges.

Professor Scott Heath, Associate Dean for Postgraduate and Early Career Researchers at The University of Manchester said of the awards: 青瓜视频淲e are delighted that the EPSRC have awarded this funding to establish these CDTs and expose new cohorts to the interdisciplinary experience that researching through a CDT encourages. By equipping the next generation of researchers with the expertise and skills necessary to tackle complex issues, we are laying the groundwork for transformative solutions that will shape industries and societies for generations to come.青瓜视频�

Announced by Science, Innovation and Technology Secretary Michelle Donelan, this round of funding is the largest investment in engineering and physical sciences doctoral skills to-date, totalling more than 青瓜视频1 billion. Science and Technology Secretary, Michelle Donelan, said: 青瓜视频淎s innovators across the world break new ground faster than ever, it is vital that government, business and academia invests in ambitious UK talent, giving them the tools to pioneer new discoveries that benefit all our lives while creating new jobs and growing the economy.

青瓜视频淏y targeting critical technologies including artificial intelligence and future telecoms, we are supporting world class universities across the UK to build the skills base we need to unleash the potential of future tech and maintain our country青瓜视频檚 reputation as a hub of cutting-edge research and development.青瓜视频�

These CDTs join the already announced . This CDT led by , Senior Lecturer in Machine Learning at The University of Manchester, will train the next generation of AI researchers to develop AI methods designed to accelerate new scientific discoveries 青瓜视频� specifically in the fields of astronomy, engineering biology and material science.

The first cohort of AI CDT, SATURN CDT and Developing National Capability for Materials 4.0 CDT students will start in the 2024/2025 academic year, recruitment for which will begin shortly. 2DMoT CDT and RAINZ CDT will have their first cohort in 2025/26.

For more information about the University of Manchester's Centres for Doctoral Training, please visit:

The framework combines dimension reduction techniques and a new explainable clustering algorithm called CLASSIX, developed by mathematicians at The University of Manchester. This enables the quick identification of groups of viral genomes that might present a risk in the future from huge volumes of data.

, presented this week in the journal PNAS, could support traditional methods of tracking viral evolution, such as phylogenetic analysis, which currently require extensive manual curation.

Like many other RNA viruses, COVID-19 has a high mutation rate and short time between generations meaning it evolves extremely rapidly. This means identifying new strains that are likely to be problematic in the future requires considerable effort.

Currently, there are almost 16 million sequences available on the GISAID database (the Global Initiative on Sharing All Influenza Data), which provides access to genomic data of influenza viruses.

Mapping the evolution and history of all COVID-19 genomes from this data is currently done using extremely large amounts of computer and human time.

The described method allows automation of such tasks. The researchers processed 5.7 million high-coverage sequences in only one to two days on a standard modern laptop; this would not be possible for existing methods, putting identification of concerning pathogen strains in the hands of more researchers due to reduced resource needs.

, Professor of Mathematical Sciences at The University of Manchester, said: 青瓜视频淭he unprecedented amount of genetic data generated during the pandemic demands improvements to our methods to analyse it thoroughly. The data is continuing to grow rapidly but without showing a benefit to curating this data, there is a risk that it will be removed or deleted.

青瓜视频淲e know that human expert time is limited, so our approach should not replace the work of humans all together but work alongside them to enable the job to be done much quicker and free our experts for other vital developments.青瓜视频�

The proposed method works by breaking down genetic sequences of the COVID-19 virus into smaller 青瓜视频渨ords青瓜视频� (called 3-mers) represented as numbers by counting them. Then, it groups similar sequences together based on their word patterns using machine learning techniques.

, Professor of Applied Mathematics at The University of Manchester, said: 青瓜视频淭he clustering algorithm CLASSIX we developed is much less computationally demanding than traditional methods and is fully explainable, meaning that it provides textual and visual explanations of the computed clusters.青瓜视频�

Roberto Cahuantzi added: 青瓜视频淥ur analysis serves as a proof of concept, demonstrating the potential use of machine learning methods as an alert tool for the early discovery of emerging major variants without relying on the need to generate phylogenies.

青瓜视频淲hilst phylogenetics remains the 青瓜视频榞old standard青瓜视频� for understanding the viral ancestry, these machine learning methods can accommodate several orders of magnitude more sequences than the current phylogenetic methods and at a low computational cost.青瓜视频�

The highly interdisciplinary team used cutting-edge unsupervised deep learning to analyse over 50,000 three-dimensional magnetic resonance images of the heart from UK Biobank, a world-leading biomedical database and research resource.

The study, published in the leading journal , focused on uncovering the intricate genetic underpinnings of cardiovascular traits. The research team conducted comprehensive genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS), resulting in the discovery of 49 novel genetic locations showing an association with morphological cardiac traits with high statistical significance, as well as 25 additional loci with suggestive evidence.  

The study's findings have significant implications for cardiology and precision medicine. By elucidating the genetic basis of cardiovascular traits, the research paves the way for the development of targeted therapies and interventions for individuals at risk of heart disease.

The research was funded by the Royal Academy of Engineering (RAEng), The Royal Society, the British Heart Foundation (BHF), and the Argentinean National Scientific and Technical Research Council (CONICET) in an interdisciplinary collaboration involving a RAEng Chair, two BHF Professors, and an IBM Fellow.

The research was directed by , Director of the , the Bicentennial Turing Chair for Computational Medicine, and a Royal Academy of Engineering Chair in Emerging Technologies. The first author was Rodrigo Bonazzola, a PhD candidate, jointly co-supervised by Prof Frangi, (CONICET, Argentina) (IBM Fellow and Chief Scientist at IBM Research).

Prof Frangi said: 青瓜视频淭his is an achievement which once would have seemed like science fiction, but we show that it is completely possible to use AI to understand the genetic underpinning of the left ventricle, just by looking at three-dimensional images of the heart.

青瓜视频淧revious studies have only investigated association of traditional clinical phenotypes, such as left ventricular mass or stroke volume, limiting the number of gene associations detected for a given study size. However, this study used AI not only to delineate the cardiac chambers from three-dimensional medical images at pace but also to unveil novel genetic loci associated with various cardiovascular deep phenotypes.青瓜视频�

He added: 青瓜视频淭his research exemplifies the power of multidisciplinary teams and international collaborations, bolstered by UK Biobank's valuable data. By marrying genetic data with cardiac imaging through advanced machine learning, we've gained novel insights into the factors shaping cardiovascular health.青瓜视频�

Early career scientist and rising star, Bonazzola, the study's lead author said: 青瓜视频淥ur research reveals genes that harbour mutations known to be detrimental to other organisms, yet the impact of common variations within these genes on cardiac structure across the human population had not been previously documented. For instance, the STRN gene, recognised for its harmful variants leading to dilated cardiomyopathy in dogs, exhibits a common variant in humans that seems to induce a subtle but detectable change in mitral orientation.青瓜视频�

Dr Ferrante said: 青瓜视频淭he study's core achievement is a robust method based on geometric deep learning for large-scale genetic and cardiac imaging data analysis, leading to ground-breaking genetic insights related to heart structure. These discoveries could revolutionize our approach to disease understanding, drug development, and precision medicine in cardiology. The study's thorough analysis and ensemble-based methods also enhance the discovery rates and the reliability of our findings.青瓜视频�

Prof Keavney, BHF Professor of Cardiovascular Medicine at The University of Manchester, emphasised the transformative methodology. He said: 青瓜视频淓mploying cutting-edge deep learning to integrate genetic and imaging data has shed light on the genetic underpinnings of heart structure. This approach is a beacon for future organ studies and understanding genetic influences on organ anatomy.青瓜视频�

Prof Plein, BHF Professor of Cardiovascular Imaging in Leeds, said: "Cardiovascular MRI plays a crucial role in understanding disease phenotypes, allowing us to uncover genetic associations that help stratify cardiovascular diseases, ultimately leading to better treatments and precision medicine."

Professor Frangi added: 青瓜视频淥ur publication marks a significant stride in correlating deep cardiovascular imaging traits with genetic data. It paves the way for revolutionary progress in cardiovascular research, clinical practices, and tailored patient care.青瓜视频�

Professor Bryan Williams, Chief Scientific and Medical Officer at the British Heart Foundation, said: 青瓜视频淭his new research shows the huge power of big data linking genes to heart structure. Machine learning has made this possible by transforming how we process, analyse and gain insights from big data to tackle the biggest questions in cardiovascular research. This pioneering new method has uncovered many more genes that influence the structure and function of the heart, which will lead to new insights into why abnormal structure and function can lead to heart disease.

青瓜视频淗eart and circulatory diseases are still devastating millions of lives each year in the UK. AI could unlock more information about the genes that contribute to the structure of the heart. In future this could lead to real improvements for patients, including the development of tailored, precision treatments for people with heart problems.青瓜视频�

The paper Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology is published in

The evolution of solutions is creating new opportunities to transform patient care and personal health outcomes. From remote monitoring and wearables, to artificial intelligence and machine learning, digital technologies are enabling health data collection and analysis and offering new insights, diagnosis and therapies.

Here is an overview of the Citation Impact on Digital Health Top 25 Rankings. The complete list can be accessed in 青瓜视频檚 article.

Research into digital health has grown massively nowadays, whereas the scale of growth in digital health research is remarkable. Based on Clarivate data, publications on digital health topics 青瓜视频� which include everything from wearable devices and mobile apps to AI analytics, telemedicine and 3D printing of drugs 青瓜视频� have risen nearly 70-fold between 2013 and 2022, from a mere 39 Web of Science-indexed papers to 2,641 青瓜视频� while UK researchers were involved in 20 per cent of all papers.

The statistics demonstrate that the University currently has 75 digital health papers in the Web of Science, 1582 citations, 32 per cent of papers in the top 10 per cent by citation, scoring 2.50 category normalised citation impact (CNCI). It showcases Manchester青瓜视频檚 consistent efforts to advance digital health research that benefits the public.

Previously, the immense volumes of medical data from numerous wearable devices or mobile phones might have overwhelmed even the most data-savvy researcher. However, artificial intelligence now enables researchers to effectively navigate such vast amounts of information without requiring advanced coding skills. Likewise, hospitals and health centres worldwide are sharing patient records in a manner that allows algorithms to detect trends, including identifying emerging pandemics at their onset.

Recent University of Manchester research, alongside Oxford University and Cancer Research UK used Artificial Intelligence to reveal a new form of aggressive prostate cancer which could revolutionise how the disease is diagnosed and treated in the future.

For more information:

A new Cancer Research UK-funded study has revealed that prostate cancer, which affects one in eight men in their lifetime, includes two different subtypes termed evotypes.

The discovery was made by an international team led by the , and The University of Manchester, who applied AI (artificial intelligence) on data from DNA to identify two different subtypes affecting the prostate.

The team hope their findings could save thousands of lives in future and revolutionise how prostate cancer is diagnosed and treated. Ultimately, it could provide tailored treatments to each individual patient according to a genetic test which will also be delivered using AI.

According to , prostate cancer is the most common cancer affecting men in the UK with around 52,000 cases a year. Dr Rupal Mistry, the charity青瓜视频檚 senior Science Engagement Manager, said:

青瓜视频淭he work published today by this global consortium of researchers has the potential to make a real difference to people affected by prostate cancer. The more we understand about cancer the better chance we have of developing treatments to beat it. We are proud to have helped fund this cutting-edge work, which has laid the foundations for personalised treatments for people with prostate cancer, allowing more people to beat their disease.青瓜视频�

The ground-breaking research, which involved additional funding from Prostate Cancer Research and involved scientists from the University of Oxford the University of Manchester, the University of East Anglia and the Institute of Cancer Research, London, highlights how a prostate cancer diagnosis can affect physical, emotional and mental wellbeing.

Lead researcher Dr Dan Woodcock, of the Nuffield Department of Surgical Sciences at the University of Oxford, said: 青瓜视频淥ur research demonstrates that prostate tumours evolve along multiple pathways, leading to two distinct disease types. 

青瓜视频淭his understanding is pivotal as it allows us to classify tumours based on how the cancer evolves rather than solely on individual gene mutations or expression patterns.青瓜视频� 

The researchers worked together as part of international consortium, called The Pan Prostate Cancer Group, set up by scientists at The Institute of Cancer Research (ICR) and The University of East Anglia to analyse genetic data from thousands of prostate cancer samples across nine countries. 

Crucially, the team's collaboration with Cancer Research UK (CRUK) aims to develop a genetic test that, when combined with conventional staging and grading, can provide a more precise prognosis for each patient, allowing tailored treatment decisions. 

The researchers used AI to study changes in the DNA of prostate cancer samples (using whole genome sequencing) from 159 patients. 

They identified two distinct cancer groups among these patients using an AI technique called neural networks. These two groups were confirmed by using two other mathematical approaches applied to different aspects of the data. This finding was validated in other independent datasets from Canada and Australia. 

They went on to integrate all the information to generate an evolutionary tree showing how the two subtypes of prostate cancer develop, ultimately converging into two distinct disease types termed 青瓜视频榚votypes青瓜视频�. 

of Manchester Cancer Research Centre, who led the study, explained: 青瓜视频淭his realisation is what enables us to distinguish the disease types. This hasn青瓜视频檛 been done before because it青瓜视频檚 more complicated than HER2+ in breast cancer, for instance. 

"This understanding is pivotal as it allows us to classify tumours based on their evolutionary trajectory rather than solely on individual gene mutations or expression patterns." 

Researcher Prof Colin Cooper, from UEA青瓜视频檚 Norwich Medical School, highlighted that while prostate cancer is responsible for a large proportion of all male cancer deaths, it is more commonly a disease men die with rather than from. This means that unnecessary treatment can often be avoided, sparing men from side-effects such as incontinence and impotence. 

He added: 青瓜视频淭his study is really important because until now, we thought that prostate cancer was just one type of disease. But it is only now, with advancements in artificial intelligence, that we have been able to show that there are actually two different subtypes at play. 

青瓜视频淲e hope that the findings will not only save lives through better diagnosis and tailored treatments in the future, but they may help researchers working in other cancer fields better understand other types of cancer too.青瓜视频� 

Dr Naomi Elster, Director of Research at Prostate Cancer Research, said: 青瓜视频淲e simply don青瓜视频檛 know enough about what a prostate cancer diagnosis means at present 青瓜视频� there are many men who have disease which is or may become aggressive and being able to treat aggressive disease more effectively is critical. But on the other side of the coin are the too many men who live with side effects of cancer treatment they may never have needed. 

青瓜视频淭hese results could be the beginning of us being able to take the same 青瓜视频榙ivide and conquer青瓜视频� approach to prostate cancer that has worked in other diseases, such as breast cancer.青瓜视频� 

Professor Ros Eeles, Professor of Oncogenetics at The Institute of Cancer Research, London, and Honorary Consultant in Clinical Oncology and Cancer Genetics at The Royal Marsden NHS Foundation Trust, said: 青瓜视频淭his study has utilised the enormous genomic dataset from The Pan Prostate Cancer Group 青瓜视频� a powerhouse of information about prostate cancer from around the world. These results will hopefully lead to better treatments for patients, demonstrating the importance of data sharing and team science.青瓜视频�

The study - 青瓜视频樓喙鲜悠禉 is published online in the journal Cell Genomics.

The EPSRC AI Hub for Causality in Healthcare AI with Real Data (CHAI) will develop new ways of unearthing important links in complex health data.

The hub will develop ways to use AI to enable the early prediction of debilitating diseases thanks to the 青瓜视频12m funding from the Engineering and Physical Sciences Research Council (EPSRC).

It is part of the nine centres announced as part of EPSRC青瓜视频檚 青瓜视频80m UK-wide investment in applying AI to real world data and research.

CHAI aims to develop AI that can empower decision making tools to improve challenging tasks such as the early prediction, diagnosis and prevention of disease, and 青瓜视频� crucially 青瓜视频� to improve the safety of such technology in healthcare.

Researchers hope to apply this new technology to tackle key societal health challenges such as understanding infection, Alzheimer青瓜视频檚, improving cancer treatments, social care, diabetes, and rehabilitation.

CHAI will be led by The University of Edinburgh青瓜视频檚 Professor Sotirios Tsaftaris, Canon Medical/RAEng Chair in Healthcare AI.

Professor Tsaftaris said: 青瓜视频淚'm delighted that the University of Edinburgh will be leading this world-leading consortium to develop next generation Causal AI. Causal AI holds tremendous promise for creating a new generation of AI solutions that are more robust, fair, safe, and transparent. Causal AI offers a step change in what AI can do for health with the proper safeguards. To fulfil this vision CHAI brings together an incredible team from across the UK (Imperial, Manchester, UCL, Exeter, KCL), several affiliated researchers and domain experts, as well as more than 50 world-leading partner organisations to work together to co-create solutions thoroughly integrating ethical and societal aspects.

青瓜视频淚 am extremely excited to lead this hub, particularly because of the strong people focus ensuring that we prepare the next generation of researchers in such cutting-edge AI methods.青瓜视频�

, who directs the University of Manchester part of the hub said: 青瓜视频淚 am so excited to be a part of the new CHAI hub. The focus on causality aligns with key strengths at the University, and ensures that we can build AI for healthcare that is robust, fair, and directly applicable to decision support. This is a genuine opportunity for us to transform the role of AI in health.青瓜视频�

Though methotrexate is the first-line drug to be given for Juvenile idiopathic arthritis (JIA), it is only effective or tolerated in half of the children and young people who receive it.

Those patients not helped by the drug have to wait longer to receive second line therapies, potentially prolonging the severe joint pain and other symptoms which often have a devastating impact on children and their families.

The study, published in the journal eBioMedicine, could facilitate more precise research into the identification of response predictors to methotrexate, such as biomarkers, and lead to better forecasting of likely outcomes following drug initiation.

It confirms that one in eight children and young people starting methotrexate will demonstrate improvements in inflammatory features of disease yet have some symptoms.

They also showed that in 16 per cent of children taking methotrexate, improvements in disease activity could be slower than in others over time.

Lead author said: 青瓜视频淕iving methotrexate to children who it will not help wastes time, money and effort for healthcare services-  as well as unnecessarily exposing them to potential side effects.

青瓜视频淏ut now machine learning has opened the door made it  possible to predicting which aspects of a child青瓜视频檚 disease would be helped by the drug and so which children should start other therapies either alongside or instead of methotrexate straight away.

青瓜视频淚n addition, this work shows how clinical trials are missing the mark in only looking at drug 青瓜视频榬esponse青瓜视频� or 青瓜视频榥on-response青瓜视频� for childhood-onset arthritis.

青瓜视频淭his oversimplification could lead to a drug being labelled as 青瓜视频榚ffective青瓜视频� when key symptoms such as pain remain, or 青瓜视频榠neffective青瓜视频� where a significant improvement is seen in one aspect of this complex disease.青瓜视频�

The research is funded by the Medical Research Council, Versus Arthritis, Great Ormond Street Hospital Children青瓜视频檚 Charity, Olivia青瓜视频檚 Vision, and the National Institute for Health Research as part of the CLUSTER consortium.

The research team accessed data from four nationwide cohorts of children and young people who began their treatment before January 2018.

Juvenile arthritis disease activity score components (including how many swollen joints, a doctor青瓜视频檚 perception of disease, a patient/parent report of wellbeing, results of a blood test for inflammation) were recorded at the start of treatment  and over the following year.

They used machine learning identify clusters of patients with distinct disease patterns following methotrexate treatment, predict clusters; and compare clusters to existing treatment response measures.

From 657 children and young people verified in 1241 patients they identified Fast improvers (11%), Slow Improvers (16%), Improve-Relapse (7%), Persistent Disease (44%).

Two other clusters they called Persistent physician global assessment (8%) and Persistent parental global assessment  (13%), were characterised by improvement in all activity score features except one.

Dr Shoop-Worrall added: 青瓜视频淭he longer-term impact of this slower disease control needs further investigation. Our study also demonstrates the utility of machine learning methods to uncover clusters of children as a basis for stratified treatment decisions.

青瓜视频淭his work builds on existing studies of methotrexate treatment response, confirming that response is not bivariate but can be highly variable across different features of disease within individuals.

青瓜视频淎t the moment trials of methotrexate in JIA categorise patients into responders and non-responders.

青瓜视频淭hat misclassification can compromise studies looking to identify predictors of response, such as biomarkers.青瓜视频�

• The paper Towards Stratified Treatment of JIA: Machine Learning Identifies Subtypes in Response to Methotrexate from Four UK Cohorts is available
• Image shows open bottle of methotrexate drug青瓜视频攐ne of the first chemotherapeutic drugs used in the early 1950s (released by the National Cancer Institute in the US,  part of the National Institutes of Health)

The purpose of the consortium will be to provide Sellafield Ltd with technical support as it delivers its long-term objectives of safely inspecting and decommissioning their facilities using remote technologies.

Sellafield Ltd have made considerable progress with the deployment of robots to address challenges on its site. However, there are many challenges that remain, many of which cannot be solved using currently available commercial technologies.

The academic consortium will be led by Professor Barry Lennox and Dr Simon Watson at The University of Manchester and supported by groups at The University of Bristol, led by Professor Tom Scott, and The University of Oxford, led by Professor Nick Hawes. Sellafield Ltd青瓜视频檚 engagement with the academic consortium will be led by its Robotics and Manufacturing Lead, Dr Melissa Willis.

Melissa Willis, Robotics and Manufacturing Research Lead at Sellafield Ltd, added: 青瓜视频�We are excited by the opportunities that this consortium provides us with and are confident that their technical expertise will help us to deliver the benefits that robotics technology offers us on the Sellafield site.青瓜视频�

The consortium has considerable experience of working with Sellafield Ltd, having all been involved in the RAIN (Robotics and Artificial Intelligence for Nuclear) hub, and more recently The University of Manchester has provided the academic leadership for the Robotics and AI Collaboration (RAICo) in Cumbria.

Experience of the consortium includes the design, development and deployment of mobile robots in a range of air, land and aquatic environments in the UK and overseas.

Working collaboratively with Sellafield Ltd, researchers at The University of Manchester developed AVEXIS, which can be deployed into aquatic facilities with access ports as small as 150 mm and collect visual and radiometric data. The commercial version of AVEXIS was the first robot to be deployed into Sellafield青瓜视频檚 Magnox Swarf Storage Silos and its use at Fukushima Daiichi has been explored.

The University of Oxford青瓜视频檚 Robotics Institute (ORI) have developed a range of mapping and mission planning technologies that can be used by robots, such as Boston Dynamics青瓜视频� Spot to autonomously monitor facilities and identify unexpected changes.

Using quadrotor and fixed wing vehicles, the University of Bristol have developed technology able to map radioactivity levels over large areas of land. The technology has been deployed successfully in the UK and overseas, with the image showing a radiation dose map generated over the Red Forest area of the Chornobyl Exclusion Zone, Ukraine, with the orange/red areas showing regions of elevated gamma dose rates.

Viscount Camrose started his tour at Engineering Building A, home to the new international research centre CRADLE (Centre for Robotic Autonomy in Demanding and Long-lasting Environments), where he announced the countdown to the centre青瓜视频檚 official opening in November.

The Minister was guided by Professor Barry Lennox, The University of Manchester青瓜视频檚 Centre for Robotics and AI Co-Director, where he learnt all about the interdisciplinary research going on in the centre, including a demonstration of a robot named Lyra, built to help transform nuclear infrastructure inspection.

Lyra was used to survey one of the radiologically contaminated ducts in Dounreay. It performed the equivalent of more than 400 air-fed suited entries into the site, equal to 2,250 man-hours. This capability reduced costs by an estimated 青瓜视频5m and it is predicted that similar surveys could save decommissioning costs by a further 青瓜视频500m in the future.

The Minister then took a tour of the Graphene Engineering Innovation Centre (GEIC), taking in its energy storage labs, printing lab facilities and construction materials testing facility, before making his way to ID Manchester and the location for the (TIC); a project which aims to link businesses to cutting-edge AI research and technologies to help enhance productivity.

John Holden, Associate Vice-President for Major Special Projects at The University of Manchester, said: 青瓜视频淚 was delighted to welcome the minister to The University of Manchester and to show him the leading-edge research and development activity we are undertaking in areas critical to the UK青瓜视频檚 future economic growth and prosperity, including our pioneering work in AI and robotics.

青瓜视频淔unding research and development in universities is critical to regional and national efforts to improve productivity across all industries, and the visit was an opportunity to highlight to the minister how we are accelerating the translation of our research base into industrial application through initiatives such as GEIC and the Turing Innovation Catalyst.

青瓜视频淭he visit was also an opportunity to highlight the major opportunity that ID Manchester represents for the region and UK 青瓜视频� our plan to transform eight hectares of the North Campus into a commercially-led innovation district will create a world-leading innovation ecosystem around the University and has the potential to create 10,000 high quality jobs in research and development intensive sectors linked to the University青瓜视频檚 capabilities over the next 10-15 years.青瓜视频�

The Minister for AI and Intellectual Property, Viscount Camrose, added: 青瓜视频淕reater Manchester has long been at the forefront of science and innovation in this country, from the first splitting of the atom to the invention of the first computer.

青瓜视频淏y engaging closely with partners including The University of Manchester, businesses and local government, we can continue to grow our innovation economy across the country and level-up the UK.

青瓜视频淚t was great to see first-hand some of the fantastic Government-backed research in Manchester, such as the development of graphene applications at the GEIC, CRADLE青瓜视频檚 cutting-edge innovations in robotics, as well as some of the projects underway through our 青瓜视频100m Innovation Accelerators programme such as the Turing Innovation Catalyst, the Centre for Digital Innovation and the Immersive Technologies Innovation Hub.青瓜视频�

The visit ended with a round-table discussion about the . Led by Innovate UK on behalf of the Department for Science, Innovation Technology (DSIT), the pilot programme is investing 青瓜视频100m in 26 transformative R&D projects to accelerate the growth of three high-potential innovation clusters 青瓜视频� Greater Manchester, Glasgow City Region and the West Midlands.

Leaders from three AI-related projects backed by the Innovation Accelerator 青瓜视频� the Turing Innovation Catalyst, led by The University of Manchester, the Centre for Digital Innovation, led by Manchester Metropolitan University, and the MediaCity Immersive Technologies Innovation Hub, led by The Landing at MediaCityUK 青瓜视频� attended the round-table. They were joined by Cllr Bev Craig, Leader of Manchester City Council and Greater Manchester lead for Economy, Business and International, and representatives from Greater Manchester Combined Authority (GMCA).

Participants discussed how to strengthen connections between these projects and maximise their value, and other national initiatives to support AI and related technologies.

Cllr Bev Craig, Leader of Manchester City Council and GMCA Lead for Economy and Business, said: 青瓜视频淭oday青瓜视频檚 visit provided a fantastic opportunity for the minister to learn more about the groundbreaking research and innovation happening right here in Greater Manchester, and particularly at The University of Manchester.

青瓜视频淚n recent years we have grown a reputation as a leading digital city-region, with AI as an important emerging sub-sector. As the impact of AI on our economy and society continues to grow, Greater Manchester is well-placed, with the potential to go even further.

青瓜视频淲e also held a productive discussion about Greater Manchester青瓜视频檚 Innovation Accelerator programme and its AI-related projects. Through the Innovation Accelerator we are piloting a new model of R&D decision making that empowers local leaders to harness innovation in support of regional economic growth.青瓜视频�