Earlier this year a team led by MediChain founder Dr Baker found hints of a radical new way of testing for and predicting Covid-19. Ultimately, an investigation by our Complex Systems Research Division led to a method called Characteristic Leucocyte Differential Count. The method was published earlier this year in the Journal Life Research. The cost per test is 10% of that of current tests and because it uses existing blood test infrastructure with no need for extra staff or reagents, it can increase test rates by a factor of 500% overnight.
It's been featured on BBC Television South Today (20 October 2020); Across the country on Heart Radio and in the newspapers The initial investigation won the global Coronahack in London (April 2020), beating hundreds of scientists from around the world including "The brightest minds in the fields of medicine and artificial intelligence . . . 500 Health experts; 500 Data scientists using Real time Covid-19 global health data"
MediChain were also finalists in the "COVID-19 edition" of the US National Pediatric Medical Device Competition in Washington, DC supported by the US Food and Drug Administration (FDA)
Using this technique and linked with live access to national data using MediChain's Athena system, it is possible to predict exactly where the new outbreaks will occur and lockdown to prevent them, reducing the period of lockdown and preventing second and future waves of COVID-19.
The UK government has awarded MediChain Ltd. funding from Innovate UK to develop a device that clips onto a smartphone to allow CLDC to be applied anywhere at any time by just about anyone.
On 7 Sept 2020 government representatives told the MediChain team that they receive tens of thousands of applications and that accepting MediChain UK’s offering made it One of the top companies at a global level.We believe that judges were excited about the impact that we could have on both the national and global economy and public health.
As well as being able to form CLDC, the new device developed by MediChain will be able to perform a large number of blood analyses at a low cost anywhere in the world simply by being clipped onto a smartphone. For CLDC it is robust against multiple variants allowing it to detect where other tests fail, as well as allowing it to pick up infection much earlier even than the gold standard RT-PCR test. This is essential in order to stop infections being spread between countries and in populations as pre-symptomatic, potentially infectious, individuals travel having got through RT-PCR testing due to it being too early to be detected. Using CLDC will form a method that prevents this.
The device developed for smartphones by MediChain has a wide range of other applications including; Leukemia detection, Malaria, Sickle Cell Anemia and potential for expansion into Diabetes and liver function.
MediChain and its sister company Hypatia are currently working to develop a pin-prick blood sample test for Covid-19 that anyone can use with a smartphone.
Based on over 10 years of conceptual development and two years of concerted effort and direct development, Athena combines artificial intelligence in a much broader sense than just neural networks with a number of esoteric principles and technologies.
Earlier this year a team led by MediChain founder Dr Baker found hints of a radical new way of testing for and predicting Covid-19. Ultimately investigation by the Complex Systems Research Division, Hypatia Solutions Ltd & MediChain Ltd led to a method calledcharacteristic leucocyte differential count. The method was published earlier this year in the Journal Life Research. The cost per test is 10% of that of current tests and because it uses existing blood test infrastructure with no need for extra staff or reagents, it can increase test rates by a factor of 500% overnight.
Sept 2020 is awarded the UK Government Innovate UK SMART award. The government representatives told the MediChain team that they receive tens of thousands of applications and that accepting Medichain UK’s offering made it One of the top companies at a global level. We believe that judges were excited about the impact that we could have on both the national and global economy and public health.
Medichain were also finalists in the "COVID-19 edition" of the US National Pediatric Medical Device Competition in Washington, DC supported by the US Food and Drug Administration (FDA)
“The main advantage of quantum computing is it can perform any task faster as compared to a classical computer. Because atoms move faster in a quantum computer than a classical computer. In quantum computing qubit is the conventional superposition state and so there is an advantage of exponential speedup which is resulted by handle number of calculations.
MediChain have been experimenting with Quantum Architectures and have working Athena Networks running under Google Tensorflow Quantum. These will execute on actual quantum processors that are supported by Cirq, including Google's Sycamore processor.
By designing a device that allows blood analysis on any smartphone for just a few dollars, and a cost per test of pennies, we can allow the number of Covid-19 tests to be increased to everyone in a country as often as they need. This could radically reduce lockdowns and prevent second and subsequent waves of Covid-19.
Using training data from the COVID-19 Risk and Treatments (CORIST) collaboration and the Moli-Sani Project, both from southern Italy, comprising WBC differential data for 72 positive (RT-PCR confirmed) and 4742 negative COVID patients, respectively, two variations of the test were applied. The first (algorithmic ensemble CLDC 21-04- SE) was optimised for sensitivity and has a sensitivity of 97·29% and specificity of 67·95% for a single test. A version of the algorithmic ensemble optimised to reduce false-positive rate (algorithmic ensemble CLDC 21-04-SP) has a sensitivity of 81·82% with a specificity of 96·50%.
Our CLDC algorithms were also applied to data from the Regional Service for the Epidemiology and Surveillance of Infectious Diseases database, a longitudinal cohort study of 379 patients with laboratory-confirmed COVID-19 admitted at the Italian National Institute for Infectious Diseases “Lazzaro Spallanzani” (INMI), in Rome (Italy). The 21-04-SE algorithm showed a sensitivity of 98·64% during the first seven days during symptom onset and 96·73% sensitivity for data taken over 21 days after initial symptoms. The 21-04-SP algorithm showed a sensitivity of 79.0%. As this dataset only contains positive patients, specificity cannot be calculated in either.
Doctorate (D.Phil) in Cancer Research, University of Oxford, Post Doctoral Fellowship with UK National Physical Laboratory (UK Standards Authority) and the University of Cambridge. Head of Laboratory in the Medical Research Council's Brain and Behaviour Centre, University of Oxford University and NHS's Radcliffe Infirmary, Oxford. Chief Technology Officer Peerius (Episerver) - Europe's Largest AI & Predictive Analytics provider, Project lead & Scientist Johnson & Johnson. Imperial College & King's College Alum. Designs research systems running in universities such as Harvard. Associate of the Royal College of Science. Fellow of the Royal Astronomical Society.
Dr Saqib Mukhtar holds both MBBS and BSc (Hons) from the University of London. He has worked as a medical doctor since 2004, and as a Primary Care Physician since 2009. He has a particular interest in health technology and has been involved within this speciality since 2015. Following his initial health tech start up, he has participated in working for a number of telemedicine applications as both a Clinical Safety Officer and Medical Advisor. His goal is to update the model of disease and responsibly utilise the best of advancing technology to drive healthcare to an outcome-based model within the individuals’ control.
Filippo Dal Ben holds an MPhil in Biotechnology from the University of Cambridge and a B.Eng. in Electronics and Electrical Engineering with work experience in multi-disciplinary engineering environments. He specializes in biotechnology, medical devices, biosensors and has a strong programming background, especially building pipelines for image analysis and machine learning. Within the company, he also bridges between MediChain Ltd. and our Italian partners.
Fleur Conway holds an MEng from the University of Cambridge. She specialises in mechanical engineering, with a focus on design and materials, and bioengineering, in biosensors and biomedical applications. She has both industry and academic experience in mechatronics - building embedded electronics systems and combining a variety of programming languages. She has also worked as a volunteer teacher in maths, science and English in Nepal. Within the company, Fleur focuses on the design and prototyping of the device, which also requires the development of optics and illumination. She also works on the data analysis and manipulation aspects of the project and works as sub-team leader, liaising with the team across their disciplines.
Lily Fitzgerald has a background in neuroscience, psychology and biomedical research. She holds a BA (Hons) in Natural Sciences from the University of Cambridge and an MSc in Research Methods of Psychological Science from the University of Glasgow. She has worked on clinical research at Imperial College London and has spent time living and working in Japan. As a Scientific Officer at MediChain Ltd., she leads data liaison and collaboration between MediChain Ltd. and MediChain Asia.
Yemurai Rabvukwa holds a First Class degree in Chemistry from the University of Liverpool. She has completed a 12 week Cloud Computing course and holds an Amazon Web Services Certified Cloud Practitioner certification. Yemurai is now working as a Scientific Officer for MediChain Ltd. . Her work within the company involves sourcing reagents, conducting laboratory experiments and consulting with technical experts.
Ben Phan holds a Master of Engineering Science from the University of Oxford. He read general engineering and specialises in information engineering overlapping with biomedical science. Ben works at the system level of image acquisition and image processing to ensure good enough images are recorded and analysed which provides the input to the master algorithm.