The researchers, led by the University of Edinburgh, with colleagues from Cambridge, London, Slovenia and China, used a combination of theoretical and experimental methods to develop a strategy to detect the DNA of infectious diseases. The results are reported in the Proceedings of the National Academy of Sciences.

The current coronavirus pandemic highlights the need for fast and accurate detection of infectious diseases. Importantly, viral infections like coronavirus and bacterial infections like those associated with antimicrobial resistance (AMR) need to be distinguished. This is usually done by using a complementary sequence that binds selectively to the genome of interest. Normally, this is done by targeting a single, long DNA sequence that is unique to the pathogen.

However, the researchers believe that much higher selectivities can be achieved by simultaneously targeting many shorter sequences that occur with a higher frequency in the pathogen of interest than in the DNA of other organisms that may be present in the patient samples.

"This approach exploits a phenomenon called ‘multivalency’, and the extensive numerical calculations, based on real bacterial and viral DNA sequences show that this approach should significantly outperform current approaches," said co-author Professor Erika Eiser from Cambridge’s Cavendish Laboratory. "Even though the individual shorter sequences bind more weakly to the target DNA than a single, longer sequences, the strength of the multivalent binding increases much faster than linearly with the number of short sequences."

In other words, instead of designing molecular probes that bind strongly to one place on the target DNA, researchers should, counterintuitively, design probes that bind weakly all over the target DNA. Making such relatively short probe sequences is, at present, a standard procedure and the sequences can be ordered online.

The experimental part of the project started with experiments in Cambridge, showing that the method can work in principle on a mixture of viral DNA and colloids coated with short complementary strands. Then the simulations took over to predict what combination of probe sequences would give the highest selectivity.

This part of the project has so far only been tested in computer models. The next step is to carry out experiments on real mixtures of viral and bacterial DNA.

"Experiments are needed to test how well this works in practice – but it is exciting work, given the urgent need for fast, reliable disease detection methods, especially those that can be applied in countries with a weak health infrastructure," said Professor Rosalind Allen from the University of Edinburgh, who led the research.

This work was performed before the COVID-19 pandemic. However, the current emergency illustrates the need for robust and highly selective methods to quickly identify specific viruses – particularly in ‘low-tech’ environments.

The research was funded in part by the Royal Society and the European Research Council.

Reference:
Tine Curk et al. ‘Computational design of probes to detect bacterial genomes by multivalent binding.’ PNAS (2020). DOI: 10.1073/pnas.1918274117

Adapted from a University of Edinburgh press release.

After reports of two dogs testing positive for SARS-CoV-2 in Hong Kong, the most recent news to cause alarm among animal owners is that of a cat in Belgium with apparent symptoms of the virus that causes COVID-19.

The owner of the cat had recently tested positive for the virus. It is reported that the cat developed breathing difficulties and diarrhoea one week later. Vets at the University of Liège, Belgium then tested the cat for SARS-CoV-2 and subsequently detected the viral genome in vomit and a stool sample.

Should we now be concerned about the virus spreading to cats? To be succinct – not yet. Several key questions need to be answered before any conclusions can be drawn from this case.

Many people are asking if the coronavirus detected in the cat really is SARS-CoV-2 or whether it could be the completely different cat-only coronavirus, which has been infecting cats worldwide for decades. The feline coronavirus exists in two forms: one causes mild gastrointestinal disease and the other causes a highly fatal disease known as feline infectious peritonitis (FIP).

Feline coronaviruses look very different to SARS-CoV-2 at the genetic level. This means that as long as the correct test was run for the cat in question, it should be easy to differentiate between the two viruses.

The standard test for SARS-CoV-2 only detects the viral genome. It is very important to bear in mind that this test does not detect infectious or “live” virus particles, so it is impossible to tell whether the viral genome found in the cat was from a particle that could replicate. To demonstrate infectivity, many more tests are needed. It is possible that the cat ate contaminated food and the virus simply passed through its gut. This explanation is less likely if large quantities of genetic material were detected in the cat, but this data has not been released.

Whereas the two canine SARS-CoV-2 cases had no obvious clinical signs relating to COVID-9, the cat at the centre of the latest media attention did have respiratory symptoms. But as every vet knows, cats can have breathing difficulties for many reasons, from feline asthma to heart disease. Similarly, there is a long list of causes of diarrhoea in cats. Without knowing any clinical details of this case, we can’t tell whether COVID-19 was responsible for the disease or if this was just an upsetting coincidence.

Thankfully, there is still zero evidence of pets transmitting the virus to humans. It is also reassuring that a large veterinary diagnostic lab recently stated they have now tested thousands of cat and dog samples for SARS-CoV-2 with no positive cases. Also, given that as of March 30 there are over 720,000 human cases worldwide, it is safe to assume that if this virus readily caused disease in pets, we would know by now.

Sarah L Caddy, Clinical Research Fellow in Viral Immunology and Veterinary Surgeon, University of Cambridge

This article is republished from The Conversation under a Creative Commons license. Read the original article.

How you can support Cambridge's COVID-19 research effort

Donate to support COVID-19 research at Cambridge

One hundred and eighty-five senior scientists from across Europe were awarded grants in today’s announcement, representing a total of €450 million in research funding. The UK has 34 grantees in this year’s funding round, the second-most of any ERC participating country.

ERC grants are awarded through open competition to projects headed by starting and established researchers, irrespective of their origins, who are working or moving to work in Europe. The sole criterion for selection is scientific excellence.

ERC Advanced Grants are designed to support excellent scientists in any field with a recognised track record of research achievements in the last ten years.

Professors Mete Atatüre and Jeremy Baumberg, both based at Cambridge’s Cavendish Laboratory, work on diverse ways to create new and strange interactions of light with matter that is built from tiny nano-sized building blocks.

Baumberg’s PICOFORCE project traps light down to the size of individual atoms which will allow him to invent new ways of tugging them, levitating them, and putting them together. Such work uncovers the mysteries of how molecules and metals interact, crucial for creating energy sustainably, storing it, and developing electronics that can switch with thousands of times less power need than currently.

"This funding recognises the huge need for fundamental science to advance our knowledge of the world – only the most imaginative and game-changing science gets such funding," said Baumberg.

Atatüre’s project, PEDESTAL, investigates diamond as a material platform for quantum networks. What gives gems their colour also turns out to be interesting candidates for quantum computing and communication technologies. By developing large-scale diamond-semiconductor hybrid quantum devices, the project aims to demonstrate high-rate and high-fidelity remote entanglement generation, a building block for a quantum internet.

"The impact of ERC funding on my group’s research had been incredible in the last 12 years, through Starting and Consolidator grants. I am very happy that with this new grant we as UK scientists can continue to play an important part in the vibrant research culture of Europe," said Atatüre.

Professor Judith Driscoll from Cambridge’s Department of Materials Science & Metallurgy was also awarded ERC funding for her work on nanostructured electronic materials. She is also spearheading joint work of her team, as well as those of Baumberg and Atatüre, on low-energy IT devices.

"My approach uses a different way of designing and creating oxide nano-scale film structures with different materials to both create new electronic device functions as well as much more reliable and uniform existing functions," she said. "Cambridge is a fantastic place that enables all our approaches to come together, driven by cohorts of inspirational young researchers in our UK-funded Centre for Doctoral Training in Nanoscience and Nanotechnology – the NanoDTC."

Professor John Robb from Cambridge’s Department of Archaeology was awarded an ERC grant for the ANCESTORS project on the politics of death in prehistoric Europe. The project takes the methods developed in the ‘After the Plague’ project and the taphonomy methods developed in the Scaloria Cave project and apply them to a major theoretical problem in European prehistory - the nature of community and the rise of inequality.

"This project is really exciting and I’ll be working with wonderful colleagues Dr Christiana ‘Freddi’ Scheib at the University of Tartu and Dr Mary Anne Tafuri at Sapienza University of Rome," said Robb. "The results will allow us to evaluate for the first time how inequality affected lives in prehistoric Europe and what role ancestors played in it."

The SAMBA II machines, developed by Diagnostics for the Real World, provide a simple and accurate system for the diagnosis of COVID-19 infection. They will be used by healthcare workers at point-of-care in order to rapidly diagnose patients, directing those who test positive for the infection to dedicated wards. They can also help identify which healthcare workers are infected, enabling those who test negative to return to the front line.

The machines will be made available to a number of hospitals across the country thanks to a US$3million (about £2.4 million) donation from the businessman and philanthropist Sir Chris Hohn, which will enable the purchase of 100 machines. The donation has enabled Addenbrooke’s Hospital, part of Cambridge University Hospitals NHS Foundation Trust, to obtain the first 10 SAMBA II machines this week for use in wards where suspected COVID-19 patients are brought in. The donation will be matched by the purchase of 10 additional machines by the Cambridge Trust.

SAMBA II looks for tiny traces of genetic material belonging to the virus, amplifies it billions of times chemically and is therefore extremely sensitive in the detection of active infections. Dr Helen Lee, CEO of Diagnostics for the Real World said: “Our goal has always been to make cutting-edge technology so simple and robust that the SAMBA machine can be placed literally anywhere and operated by anyone with minimum training.”

Patients will provide a nasal and throat swab. Once these have been loaded into the SAMBA machine, the remainder of the process is fully automated. At the moment, tests are sent for analysis in centralised laboratories and this, compounded by the sheer number of samples that are having to be analysed, means that diagnosis can take one to two days. SAMBA II is able to deliver results while the patient waits, helping healthcare workers ensure that those infected can be quickly directed to specialised wards. Whereas current tests can take over 24 hours or longer to deliver their results, SAMBA is able to deliver a diagnosis in less than 90 minutes.

The tests have been validated by Public Health England, Cambridge, in 102 patient samples and shown to have 98.7% sensitivity (ability to correctly identify positive cases) and 100% specificity (the ability to correctly identify negative cases) compared to the currently used NHS/Public Health England test. This has enabled the team to obtain a CE mark.

Dr Martin Curran who conducted the evaluation said: “I am extremely happy with the performance of the SAMBA test because it matched the routine centralised laboratory results.”

Professor Ravi Gupta from the Cambridge Institute for Therapeutic Immunology and Infectious Disease, who is leading the ‘COVIDx’ clinical study to evaluate the impact of the test, said: “Testing healthcare workers could help reduce the risk of infection in healthcare facilities themselves, which might in turn assist national control efforts. It will also reduce the number of staff self-isolating for symptoms as we could use the test to determine who is actually infected. At present the lack of testing is resulting in severe staff shortages nationally.”  

Research nurses to support COVIDx will be provided by the NIHR Cambridge Biomedical Research Centre.

Researchers at Cambridge will also be using SAMBA II to test healthcare workers in high-risk areas such as intensive care units or COVID-19 wards. Their aim is to see whether the tests can identify asymptomatic individuals – those who are infected but do not realise it – so that they can self-isolate and prevent inadvertent transmission.

The technology behind SAMBA II was developed while Dr Lee was at Cambridge’s Department of Haematology. The development of the technology has been supported by Wellcome, the Children’s Investment Fund Foundation, the US National Institutes of Health and Cambridge Enterprise, among others.

“We urgently need rapid diagnostic tests to help the NHS and Public Health England manage the coronavirus outbreak and identify those patients at risk to themselves and to others,” says Sir Chris Hohn. “I’m delighted to have supported Dr Lee’s important research and now help begin the rollout of this cutting-edge technology across the NHS. This is a game changer.”

How you can support Cambridge's COVID-19 research effort

Donate to support COVID-19 research at Cambridge

But the Garden staff remain committed to sharing the beauty of springtime, and encouraging gardening activities in whatever space people may have available at home.

“In this challenging time we’re more aware than ever of the importance of nature. The emergence of new life in Spring can be really uplifting, giving people an important psychological boost, and we want to share this with everyone, whatever their situation,” said Professor Beverley Glover, the Garden’s Director.

The skeleton team of horticultural staff now looking after the collections will bring the Garden into our homes through weekly ‘Wellness Wanders’. With short videos they will be sharing the sights, colours and sounds of the unfolding Spring with those who are self-isolating or don’t have immediate access to nature. They will also be posting ‘BitesizeBotanics’, short videos and pictures of the springtime beauty, on the Garden’s website and social media channels.

At this time of year, Horticultural Learning Coordinator Sandie Cain would normally be in the Botanic Garden’s dedicated Schools’ Garden, teaching primary school children how to grow vegetables from seed, and hosting ‘Meet the Vegetable Gardener’ sessions. Instead, she has filmed herself in her own garden sharing valuable tips and advice for the novice gardener, young and old.

Over the next 10-12 weeks Sandie will be posting regular videos with information on what we can all do at home to grow our own veg, whether in a garden or on a sunny windowsill. And although garden centres may be closed, Sandie has some great tips for improvised equipment and materials. 

Other members of the Learning Team will also contribute to the series, sharing advice on how to encourage garden wildlife, and how to create art with kids in the garden. And for those wishing to test their plant knowledge, the team is posting a daily quiz on the Garden’s Twitter and Facebook channels based on popular plants in the collection. Do you know how many bananas the average person in the UK eats in a year, or what people used to believe would happen if they pulled up a mandrake? Answers are revealed the following day.

The Garden will remain closed over Easter weekend, but fortunately filming has already taken place for Heavenly Gardens, a two-part programme with former chorister Alexander Armstrong and garden designer Arit Anderson. 

Image credit: Tern

The first part, to be broadcast on Good Friday on BBC One, will showcase Cambridge University Botanic Garden as an ‘outdoor laboratory’ used for scientific research as well as a spiritual place representing hope, expectation and joy.

A growing body of evidence shows that we need nature for our physical health and mental wellbeing. In this time of great uncertainty, finding a way to connect with the living world seems more important than ever. 

For more information and to watch the films created by Garden staff, visit the Botanic Garden website: 

• Gardening club
• Wellness wander

To test yourself with the daily quiz, follow #CUBGplantquiz or to send your gardening questions to the team go to:

• CUBG on Twitter
• CUBG on Facebook

How you can support Cambridge's COVID-19 research effort

Donate to support COVID-19 research at Cambridge

Workers under the age of thirty, as well as those on lower incomes, on both sides of the Atlantic are already bearing the brunt of the economic shutdown caused by the COVID-19 pandemic, latest research finds.

Data collected by economists towards the end of March shows younger workers in the UK and US were more likely to have either recently lost their job or seen a drop in hours and earnings compared to workers in middle age.

Researchers from the universities of Cambridge, Oxford and Zurich also found that those under 30 and still in employment believed they were much more likely to lose their job by August, compared to those aged 40-55.

The research suggests that in the UK, 8% of all workers employed in February had already lost their jobs. A third of all those still in work expected to lose their jobs within the next four months.

In the US, 11% of all workers had already lost their jobs due to COVID-19, and 40% of all those still working expected job loss by August. 

Workers on lower incomes – those earning below 20,000 pounds or dollars a year – across all age groups in both countries were more likely to have lost their job in the preceding four weeks than workers earning over £40k in the UK or $50k in the States. 

Those still employed on lower incomes in the UK and US could conduct a much smaller percentage of their normal working tasks from the safety of home.

Data was collected from “a large geographically representative sample” in each country say researchers. A total of 3,974 people in the UK were surveyed on March 25, two days into the government-imposed lockdown. The US data came from 4,003 people on March 24.

“Our findings suggest that the immediate impact of the coronavirus downturn on workers has been large and unequal, with younger workers and those at the bottom of the income distribution hit hardest,” said Dr Christopher Rauh from the University of Cambridge’s Faculty of Economics, who led the research.

“In the short term, there is a need to provide quick assistance to help those hit hardest to cover their bills in the coming weeks. Around half of all workers on both sides of the Atlantic expect to have difficulty paying their usual bills,” Rauh said.

“In the long term, the economic shock caused by the pandemic is highly likely to increase inequality between young and old, between higher and lower earners, and between those on secure and insecure contracts.”

The survey found that workers on UK statutory sick pay, and those without paid sick leave in the US, were more likely to say they would to go into work with a cold or light fever. Researchers say that “paid sick leave policies should be rethought not only in light of workers’ welfare but public health as a whole”.

In both countries, far more self-employed people earned less than usual the week prior to the survey compared with those on permanent contracts.

The research was done before the UK Chancellor announced new measures for the self-employed, beginning in June. However, the researchers caution that it “might be too late to prevent severe economic hardship”.

Added Rauh: “Preventing this shock from scarring the employment progression of the younger generation and the less-economically advantaged is vital if we are to avoid permanent damage to economies and individual welfare.”

The findings have just been published as two working papers through the University of Cambridge Institute for New Economic Thinking: Working paper, UK; Working paper, US. 

The Cambridge-INET Institute has now launched a dedicated website for all their coronavirus-related research: http://covid.econ.cam.ac.uk. 

Key UK findings:

• On average across all UK workers, people expect to earn 35% less in the next four months compared to usual.
• 69% of workers under 30 reported working fewer hours the previous week compared to usual and 58% reported earning less, compared to 49% and 36% of workers aged 40-55 respectively.
• 10% of workers under 30 are now unemployed because of COVID-19, compared to 6% of workers aged 40-55.
• On average, those under 30 and still employed believe they have a 39% chance of job loss by August, compared to 27% for 40-55 year olds.
• Workers earning under £20,000 can do 30% of the tasks in their main job from home compared to 55% for those earning more than £40,000.
• 12% of low-income workers earnings are now unemployed because of COVID-19 compared to 5% of higher earners.
• Workers earning less than £20,000 expect to earn just 58% of their usual income between now and August. Those earning more than £40,000 expect to make 69% of their usual income on average.
• 43% of workers with just statutory sick pay said they usually go to work with a cold or light fever, compared to 31% of workers with additional paid sick leave.

Key US findings:

• On average across all US workers, people expect to earn 39% less in the next four months compared to usual.
• 72% of workers under 30 reported working fewer hours the previous week compared to usual and 61% reported earning less, compared to 62% and 55% of workers aged 40-55 respectively.
• On average, those under 30 and still employed believe they have a 43% chance of job loss by August, compared to 40% for 40-55 year olds.
• Workers earning under $20,000 can do 42% of the tasks in their main job from home compared to 57% for those earning more than $50,000.
• 16% of low-income workers earnings are now unemployed because of COVID-19 compared to 7% of higher earners.
• Workers earning less than $20,000 expect to earn just 48% of their usual income between now and August. Those earning more than $50,000 expect to make 69% of their usual income on average.
• 26% of workers without paid sick leave report they would go to work with a cold or light fever, compared to 24% of those with paid sick leave.

How you can support Cambridge's COVID-19 research effort

Donate to support COVID-19 research at Cambridge

One short week ago, I called on governments to use existing data and proven machine learning and AI techniques to help healthcare systems combat the COVID-19 pandemic.

The response was amazing. My team and I received encouragement, ideas, and proposals for collaboration.

We also received, courtesy of Public Health England, a set of (depersonalised) data on existing COVID-19 cases. Along with my team at the Cambridge Centre for AI in Medicine, I’ve spent the last few days training our models on this data. The results so far are extremely encouraging.

Among other things, we wanted to demonstrate that machine learning techniques can accurately predict how COVID-19 will impact resource needs (ventilators, ICU beds, etc.) at the individual patient level and the hospital level, thereby giving a reliable picture of future resource usage and enabling healthcare professionals to make well-informed decisions about how these scarce resources can be used to achieve the maximum benefit.

Based on the data we received from Public Health England, we now have a proof-of-concept demonstrator showing that this can be done, in the form of a new system we call Adjutorium.

Isn’t flattening the curve enough?

Social policies can certainly help take the strain off healthcare systems around the world. But there’s no guarantee that certain individual hospitals won’t still be stretched well beyond capacity. Additionally, these measures may not be properly observed by everyone, or may be relaxed slowly over time. It’s important to ensure that hospitals remain armed with information that will help them manage peaks in demand for resources like ICU beds or ventilators.

As I touched upon last week, life-or-death choices will be made regarding the use of scarce resources like ventilators and ICU beds. If you are managing or working in a hospital, it would be incredibly helpful (but it’s currently not possible) to have a highly reliable picture of the likely usage status of these resources over time.

This is what too many healthcare professionals around the world are currently worrying about:

We can help answer these questions by being smart about how we use existing data on hospital admissions, ICU admissions, use of ventilators, patient outcomes (e.g. discharge, mortality), and more. If we have access to high-quality datasets containing such information, we can use machine learning to answer questions such as:

- Which patients are most likely to need ventilators within a week?

- How many free ICU beds is this hospital likely to have in a week?

- Which of these two patients will get the most benefit from going on a ventilator today?

While these questions can reliably be answered using the machine learning techniques we’ve developed, I cannot emphasise enough that the decisions themselves will, of course, still be made by healthcare professionals on the basis of their organisation’s priorities and policies.

Here’s how a machine learning model can help answer questions in a way that’s useful to healthcare professions:

As you can see, patients are given risk scores based on their likelihood of ICU admission or ventilator usage. These are then aggregated across the hospital to give a picture of future demand on resources.

Using Public Health England data

Last week, I shared a firm belief that existing and proven machine learning techniques can already tackle these kinds of challenges and can deliver essential insights, even using existing (possibly quite noisy) data sources. Thanks to the data we received from Public Health England, I feel more confident than ever.

We received data for nearly 1,700 patients, and that number continues to increase because the dataset is updated daily. While the data was depersonalised, it includes basic information, lab results, hospitalisation details, risk factors and outcomes.

We fed this data to AutoPrognosis, a state-of-the-art automated machine learning framework that our team developed in 2018 (initially for cardiovascular issues, but subsequently also for cystic fibrosis and breast cancer, among others).

To predict mortality, we used data from 850 patients to train our model, and then verified the accuracy of the model using results from 197 other patients from the same dataset. For ICU admission prediction, we trained with data from 950 patients and verified with data from 285 patients. To predict need for ventilation, we trained with 810 patients and verified with 276 patients.

We called the new system we created "Adjutorium," meaning help, assistance or support.

What we learned

So, how did Adjutorium perform?

Simply put: it did really, really well.

Once trained with patient data, Adjutorium was able to make highly accurate predictions about the patients whose data we used for verification. Crucially, we managed to do so much more accurately than existing and widely-used survival analysis techniques such as Cox regression or well-known indexes such as the Charlson comorbidity index.

Accuracy is measured using AUC-ROC. Higher is better.

It’s also worth bearing in mind that Adjutorium achieved these results with a relatively small proportion of the data that could be gathered from COVID-19 cases globally. The more data we have access to, the better we can train our models and improve their accuracy, and the more useful Adjutorium becomes.

Next steps

The progress we’ve made so far is extremely encouraging: we now have a functioning proof of concept that demonstrates the potential use of machine learning in helping to manage scarce resources like ICU beds and ventilators. There’s still work to be done, though, and much of this will rely on continuing to receive new and high-quality data.

Our immediate priority is to continue to validate the models we’ve developed. Doing so will bring us closer to finalising the system for usage by healthcare professionals.

We also need to get our hands on new types of data that will make our existing models even more accurate. Specifically, we require longitudinal data that enables us to gain a deeper understanding of the progression of patients while they’re hospitalised (rather than irregularly-recorded "snapshots" that show the state of affairs at specific times). Given how little is known about COVID-19, such data would provide valuable insights. Additionally, we’re hoping for clearer data regarding the timing and effects of ventilators when used to treat patients. This would let us tell, for example, how long individual patients could or should have waited before ventilation in order to achieve the best possible outcomes.

We will also be working with the NHS and Public Health England to transform our tools into a system that can easily be used and understood by healthcare professionals. In this sense, interpretability is key: we want to ensure that decision-makers can debug and analyze the information generated by our system.

If you’d like to know more…

On Wednesday, I gave a presentation summarising our progress at a COVID-19 workshop hosted by ELLIS (European Laboratory for Learning and Intelligent Systems). You can find the slide deck here and a video of my presentation embedded below.

How you can support Cambridge's COVID-19 research effort

Donate to support COVID-19 research at Cambridge

The researchers, from Cambridge and Newcastle Universities, have designed a new way to monitor batteries by sending electrical pulses into them and measuring the response. The measurements are then processed by a machine learning algorithm to predict the battery’s health and useful lifespan. Their method is non-invasive and is a simple add-on to any existing battery system. The results are reported in the journal Nature Communications.

Predicting the state of health and the remaining useful lifespan of lithium-ion batteries is one of the big problems limiting widespread adoption of electric vehicles: it’s also a familiar annoyance to mobile phone users. Over time, battery performance degrades via a complex network of subtle chemical processes. Individually, each of these processes doesn’t have much of an effect on battery performance, but collectively they can severely shorten a battery’s performance and lifespan.

Current methods for predicting battery health are based on tracking the current and voltage during battery charging and discharging. This misses important features that indicate battery health. Tracking the many processes that are happening within the battery requires new ways of probing batteries in action, as well as new algorithms that can detect subtle signals as they are charged and discharged.

"Safety and reliability are the most important design criteria as we develop batteries that can pack a lot of energy in a small space," said Dr Alpha Lee from Cambridge’s Cavendish Laboratory, who co-led the research. "By improving the software that monitors charging and discharging, and using data-driven software to control the charging process, I believe we can power a big improvement in battery performance."

The researchers designed a way to monitor batteries by sending electrical pulses into it and measuring its response. A machine learning model is then used to discover specific features in the electrical response that are the tell-tale sign of battery aging. The researchers performed over 20,000 experimental measurements to train the model, the largest dataset of its kind. Importantly, the model learns how to distinguish important signals from irrelevant noise. Their method is non-invasive and is a simple add-on to any existing battery systems.

The researchers also showed that the machine learning model can be interpreted to give hints about the physical mechanism of degradation. The model can inform which electrical signals are most correlated with aging, which in turn allows them to design specific experiments to probe why and how batteries degrade.

"Machine learning complements and augments physical understanding," said co-first author Dr Yunwei Zhang, also from the Cavendish Laboratory. "The interpretable signals identified by our machine learning model are a starting point for future theoretical and experimental studies."

The researchers are now using their machine learning platform to understand degradation in different battery chemistries. They are also developing optimal battery charging protocols, powering by machine learning, to enable fast charging and minimise degradation.

This work was carried out with funding from the Faraday Institution. Dr Lee is also a Research Fellow at St Catharine’s College.

Reference:
Yunwei Zhang et al. ‘Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning.’ Nature Communications (2020). DOI: 10.1038/s41467-020-15235-7

The COVID-19 Sounds App is now available as a web app for Chrome and Firefox browsers. Versions for Android and iOS will be available soon.

As COVID-19 is a respiratory condition, the sounds made by people with the condition – including voice, breathing and cough sounds – are very specific. A large, crowdsourced data set will be useful in developing machine learning algorithms that could be used for automatic detection of the condition.

"There’s still so much we don’t know about this virus and the illness it causes, and in a pandemic situation like the one we’re currently in, the more reliable information you can get, the better," said Professor Cecilia Mascolo from Cambridge’s Department of Computer Science and Technology, who led the development of the app.

"I am amazed at the speed that we managed to connect across the University to conceive this project, and how Cecilia's team of developers came together to respond to the urgency of the situation," said Professor Pietro Cicuta from Cambridge’s Cavendish Laboratory, a member of the team behind the app’s development. Professor Andres Floto, Professor of Respiratory Biology at the University, and Research Director of the Cambridge Centre for Lung Infection at Papworth Hospital, Cambridge, has also advised on the clinical aspects of the app.

The COVID-19 Sounds App collects basic demographic and medical information from users, as well as spoken voice samples, breathing and coughing samples through the phone’s microphone. The app will also ask users if they have tested positive for the coronavirus.

In addition, the app will collect one coarse grain location sample. The app will not track users, and will only collect location data once when users are actively using it. The data will be stored on University servers and be used solely for research purposes. The app will not provide any medical advice.

Once they have completed their initial analysis of the data collected by the app, the team will release the dataset to other researchers. The dataset could help shed light on disease progression, further relationship of the respiratory complication with medical history, for example.

"Having spoken to doctors, one of the most common things they have noticed about patients with the virus is the way they catch their breath when they’re speaking, as well as a dry cough, and the intervals of their breathing patterns," said Mascolo. "There are very few large datasets of respiratory sounds, so to make better algorithms that could be used for early detection, we need as many samples from as many participants as we can get. Even if we don’t get many positive cases of coronavirus, we could find links with other health conditions."

The study has been approved by the Ethics Committee of the Department of Computer Science and Technology, and is partly funded by the European Research Council through Project EAR.

How you can support Cambridge's COVID-19 research effort

Donate to support COVID-19 research at Cambridge

The work enhances the hospital’s own modelling, and provides insight into how day-to-day activities might be affected by a rise in patient numbers in the coming weeks or months.

‘Discrete event simulations’ have been worked up by a team from the University’s Department of Engineering to manage the flow of patients through hospital wards in the event of a surge in cases, and anticipate waiting times, bed availability, and equipment and staff shortages.

Lead on the simulation development, Dr Ajith Parlikad, of Cambridge’s Institute for Manufacturing, said: “It’s looking at the physical flow of patients and projecting admissions rates into the future - identifying where ‘bottlenecks’ might occur, and where the hospital might need to scale up beds, ventilators, oxygen and staff as part of their COVID-19-orientated activities.

“We started with a flow diagram of how we thought the hospital worked, then talked it through with the team at Addenbrooke’s. It was quite close to their own model, but we were able to factor in more details, such as ICU beds, ‘COVID-positive’ beds (patients with the virus who don’t require intensive care), and the initial checking and testing process when patients arrive – everything has a statistical distribution associated with it.”

As well as patient flow modelling, the Department is supporting Addenbrooke’s in a number of other ways. Industrial engineering students are volunteering their time to focus on the hospital’s oxygen supply, among other things how it might be replenished and filtered, and are also looking at how to model and optimise COVID-19 testing processes. Colleagues Tom Ridgman and Florian Urmetzer are co-ordinating the volunteer student group.

In addition, further work is beginning on modelling that will help the hospital better understand staffing level availability during disruptions such as the COVID-19 outbreak.

Duncan McFarlane, Professor in Industrial Information Engineering, said: “Two weeks ago we knew very little about hospital operations, but with close input from the hospital we’ve been able to pick from a series of industrial techniques and apply the most useful ones to this new setting. Instead of production lines we’re now looking at hospital wards, and rather than products or raw materials we are examining the flow of patients and supplies.

“The support from the hospital has been extraordinary - especially given the level of pressure it has been operating under."

Dr Ewen Cameron, Director of Improvement and Transformation at Cambridge University Hospitals, added: “At this time of unprecedented change for the NHS, our teams are working around the clock to set up innovative ways of working to best care for patients and protect our staff.

“The hospital looks very different now to a few weeks ago, and we remain open to additional ideas on how to manage this crisis as best we can. New challenges require new ways of thinking, and we are hugely grateful to the Institute for Manufacturing for offering their expertise to help us beat the virus.”

How you can support Cambridge's COVID-19 research effort

Donate to support COVID-19 research at Cambridge

A new testing laboratory will be set up by AstraZeneca, GSK and Cambridge at the University’s Anne McLaren laboratory. This facility will be used for high throughput screening for COVID-19 testing and to explore the use of alternative chemical reagents for test kits in order to help overcome current supply shortages. 

Alongside this new testing facility, AstraZeneca and GSK are working together to provide process optimisation support to the UK national testing centres in Milton Keynes, Alderley Park and Glasgow for COVID-19, providing expertise in automation and robotics to help the national testing system to continue to expand capacity over the coming weeks.

While diagnostic testing is not part of either company’s core business, we are moving as fast as we can to help where possible - with a focus on providing our world class scientific and technical expertise - working both with the Government’s screening programme and alongside the wider life sciences sector and specialist diagnostic companies.

Further updates on progress will be issued on this work in due course.

We continue to pay tribute to those working on the frontlines of this pandemic, in the UK and globally. Defeating COVID-19 requires a collective effort from everyone working in healthcare and we are committed to playing our part.

In the midst of the pandemic, she has been providing advice to the Government through SPI-M, the specialist pandemic modelling group that feeds into SAGE, the Scientific Advisory Group for Emergencies, as well as through Cambridge’s Centre for Science and Policy (CSaP).

In 2018, she and her team were behind the UK’s largest citizen science experiment in collaboration with the BBC, using location data from mobile phones to map how pandemic influenza might spread across the UK. The massive dataset that resulted from the experiment, the largest and most detailed of its kind, has been useful to teams working on the current pandemic.

"Public health experts have been saying for decades that when it comes to pandemic flu, it wasn’t a matter of if, it was a matter of when," Gog said. "And now that this coronavirus pandemic is here and things are changing every day, we’ve got to get information out there quickly, but making sure that it’s useful information that can help inform good policy."

With the earliest cases of COVID-19 in the UK, it was possible to perform contact tracing and shut down early chains of transmission. The data suggests that there wasn’t a single case that began the virus’ spread across the UK, but multiple cases, each with their own transmission chains.

It’s likely that cases arrived relatively early London due to its centrality in the nation and as much of the country’s transport infrastructure is built around getting people in and out of London. With many international imports of new cases, eventually the approach of contact tracing is overwhelmed and transmission takes hold within the UK, requiring the introduction of more severe overall social distancing measures to control transmission.

Once cases are rising exponentially, in order to contain the pandemic we must reduce the number of people that each contagious person infects. In disease dynamics, this reproduction ratio is called R. For any epidemic or pandemic to die out, in the absence of a vaccine, the effective R needs to be less than one: that is, if each contagious person infects less than one other person, then the number of new cases will slow and, eventually, stop. Current data suggests that the original reproduction ratio, R0, for coronavirus was between 1.5 and 3.5.

For modellers like Gog, knowing how and when people come into contact with other people helps determine R, and in turn helps develop a model of how a pandemic spreads.

The data from the BBC Pandemic project provides a highly useful source of data on how we most often come into contact with others. For those of working age, the workplace is the source of much person-to-person contact, so switching to remote working for those who can do so will reduce transmission between workplace colleagues. For those over 65, who are most at risk from severe illness due to COVID-19, most contact occurs outside the home, in places such as shops, restaurants and leisure activities, so shutting down these non-essential activities is also key to reducing R.

Earlier in the pandemic, there was criticism of the Government’s initial reluctance to close schools. However, Gog says that all the evidence suggests that school closures will only reduce transmission rates between 10 and 20 percent. Earlier models of the spread of seasonal ‘flu have looked at schoolchildren as key spreaders, but the behaviour of children, in particular teenagers, has changed a great deal in the past decade: teenagers now do much of their socialising online, and don’t so often gather in large groups as much as older generations did, a point that was confirmed by the BBC Pandemic data. In addition, it is unclear at the moment how much role children play in coronavirus transmission, whether they are as susceptible and infectious as adults.

"We have to adapt our models to account for the way that people are behaving now," said Gog. "Four weeks ago, a transmission reduction of ten or twenty percent might not have seemed like a lot. Additionally, children who were out of school while their parents were continuing to work might have gone to spend the day with their grandparents, putting them at risk. But right now, we’ll take any reduction you can get. The key thing now is to keep the number of critical cases as low as you can to reduce burden to a point that health systems can manage."

Every model has a degree of uncertainty, and for Gog, the biggest challenge in mapping how COVID-19 might continue to spread is that there has not been widescale testing in the UK. There is no ‘one true model’, and so epidemiological modellers have been modelling a range of scenarios and adapting as more data becomes available. In addition, there is a lag between the number of reported deaths and when those people became infected, further complicating the work of Gog and her colleagues.

"There are different ways this all plays out based on the information we have right now, and we have to model for different eventualities," said Gog. "It’s likely we won’t see very clearly the full effect of the lockdown measures until they have been in place for a few weeks."

Looking beyond the next few weeks, Gog says the information she and her colleagues around the country are desperate to have is information about what proportion of the population has been infected, via widescale antibody testing.

"Once we have that information, it will help us make better decisions about what to do next," said Gog. "If only a small proportion of the population has contracted the virus, then we could remain in lockdown for quite some time, whereas if a significant part of the population has already had it, then we can start thinking about how we get back to normal."

The ‘lowhighcovid’ tool is intended to highlight the potential impact of different control strategies on the rate of spread of COVID-19. It is designed as an educational tool, and is not intended to be used as a COVID-19 disease management or forecasting tool. 

“Our website is intended to demystify infectious disease modelling, and highlight the broad type of model behind government policies for the control of COVID-19,” said Nick Taylor, a PhD researcher in Theoretical and Computational Epidemiology in Cambridge’s Department of Plant Sciences who was involved in developing the tool.

Control measures, including social distancing and lockdown, affect the rate at which COVID-19 spreads through a population. The interactive model allows users to see the likely effects of different measures, depending on when they are started and the length of time they are in place.

There are a wide variety of approaches to modelling the spread of disease. Models used so far for COVID-19 range from detailed individual-based models, which are run many times for each set of parameters to give a range of predictions, to well-established deterministic models which divide the population into Susceptible, Infected and Resistant classes (referred to as an SIR model) resulting in a single prediction for one set of parameters. The new tool allows users to explore how a modified SIR model can be used to understand and manage infectious disease transmission.

Users select a country, a control measure, and how long the control is in place. The model then predicts how rapidly coronavirus will spread through the population. It illustrates how various control strategies applied today might impact the number of infections, hospitalisations, ICU bed requirements and deaths.

“COVID-19 spreads so rapidly that it is capable of quickly generating enough seriously ill patients to overwhelm the intensive care unit capacity of most healthcare systems in the world. This is why most countries have opted for strategies that slow the infection rate,” said Taylor.

A real-time data feed within the new tool allows users to follow the progress of the current pandemic, and to compare this across different countries. The data feed was designed by Daniel Muthukrishna, a PhD student at the University’s Institute of Astronomy. 

Explanatory videos, included alongside the interactive model, give users a greater insight into some of the science underlying disease control strategies. 

“Biological systems are very complicated, and there are still many uncertainties surrounding COVID-19,” said Dr Cerian Webb, a post-doctoral researcher in the Epidemiology and Modelling Group of the University’s Department of Plant Sciences who provided the videos. “Controlling this disease is a difficult task, and there is no perfect strategy – each has advantages and disadvantages.”

How you can support Cambridge's COVID-19 research effort

Based on the Cambridge Biomedical Campus, the Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID) is integrated with the NHS both locally, through Cambridge University Hospitals NHS Foundation Trust and the Royal Papworth Hospital NHS Foundation Trust, and nationally, in particular through the National Institute for Health Research (NIHR) BioResource.

CITIID, based in the Jeffrey Cheah Biomedical Centre, opened its doors in September 2019. Following the outbreak of SARS-CoV-2, the virus that causes COVID-19, the institute has redirected all of its research efforts to tackling the pandemic.

“The world is facing an unprecedented challenge, with potentially millions of lives at risk, which is why over 150 of my colleagues at our new institute are focusing their expertise on the fightback against COVID-19,” says Professor Ken Smith, Director of CITIID.

“Together with our partners in the NHS and NIHR, we aim to identify those patients at greatest risk and understand why the coronavirus makes some people so sick while leaving others with only mild symptoms. Ultimately, we hope this will lead to the development of new treatments against this dreadful disease.”

The Institute last week opened what is believed to be the largest Containment Level 3 Facility in any UK academic institution. These facilities are required for work on dangerous pathogens such as the coronavirus.

“The state-of-the-art facilities and equipment at CITIID will allow us to do essential work on the novel coronavirus in a safe environment,” says Professor Gordon Dougan. “Our institute, positioned as it is on the thriving Cambridge Biomedical Campus, is perfectly suited to lead Cambridge’s response, working with research and health partners locally, nationally and internationally on this urgent problem.”

The team at the institute has also been instrumental in evaluating and helping set up point-of-care, rapid diagnostic testing for patients at Addenbrooke’s Hospital, part of Cambridge University Hospitals (CUH) NHS Foundation Trust, as well as developing tests for frontline healthcare workers treating COVID-19 patients.

“Organising the logistics for testing has been a huge challenge,” says Professor Paul Lehner. “But thanks to a tremendous collaborative effort between CUH and the University, we are now testing frontline healthcare workers as well as people who are off work and in isolation due to potential COVID-related contacts.”

Recruitment is already underway at the institute of COVID-19 patients at Addenbrooke’s. Researchers aim to recruit all consenting patients infected with SARS-CoV-2 from the hospital.

Once consent has been obtained from a patient, the team will take blood and other samples, which will be processed in the Department of Medicine’s laboratories before being transferred for storage and further study at CITIID. Samples will be taken when the patients first arrive at the hospital and during the course of disease, with the research team also working alongside NHS staff to support patient care.

This study forms part of the COVID-19 BioResource, a collaboration with the NIHR National BioResource, and will involve state-of-the-art analysis of the samples, helping the team understand how coronavirus infects us and causes disease and how our immune system fights back. It aims to allow researchers to predict which patients will do well or badly, and to help inform the development of new medicines to tackle the disease.

“A key challenge for the institute is trying to understand how much of the lung disease seen in COVID-19 patients is caused by the virus itself and how much is due to an inappropriate immune response,” explains Professor Lehner. “An answer to this question will help guide how best we treat this devastating condition.”

The University recently announced that CITIID would take be taking a leading role in the £20 million COVID-19 Genomics UK Consortium, a major national effort to help understand and control the infection. Its researchers are also leading the evaluation of a new rapid diagnostic test for COVID-19, developed by a University spinout company, which is capable of diagnosing the infection in under 90 minutes.

Visit the Cambridge Fighting COVID website

How you can support Cambridge's COVID-19 research effort

Donate to support COVID-19 research at Cambridge

This forms part of a national effort by medical schools, the GMC and the NHS following the government’s call for additional medical staff in these times of crisis due to the COVID-19 pandemic.

Patrick Maxwell, Regius Professor of Physic and Head of the School of Clinical Medicine at the University of Cambridge, said: “I would like to offer my warm congratulations to all of our successful final year medical students. These individuals are all entering the medical profession because they want to make a difference to people’s lives. Their years of clinical training will have helped prepare them for this day, but even so, this is an extraordinary and challenging time. We owe them, and all our NHS colleagues, a huge thank you and wish them well in the future.”

Diana Wood, Clinical Dean at the School of Clinical Medicine, added: “I am immensely proud of our 2020 finalists.  Clearly this was not the culmination of their medical student careers that they expected, and they all feel keenly the lack of shared celebration with the staff and their student colleagues at this time.

“Our students responded to this national crisis in an exemplary fashion, and I have been both impressed and moved by their reactions and their willingness to put their years of medical education into practice at this time.  I wish them all the very best for the next few months and great success in their future careers.” 

In the light of the COVID-19 outbreak and the pressure it is putting on the NHS, the School of Clinical Medicine last month took the decision, in consultation with the GMC, to cancel its final clinical examinations. The students had already completed their final written examinations and been assessed on clinical competence in previous examinations and on placements in a range of clinical environments.

Researchers from Cambridge University and University of California San Diego have 3D printed coral-inspired structures that are capable of growing dense populations of microscopic algae. Their results, reported in the journal Nature Communications, open the door to new bio-inspired materials and their applications for coral conservation.

In the ocean, corals and algae have an intricate symbiotic relationship. The coral provides a host for the algae, while the algae produce sugars to the coral through photosynthesis. This relationship is responsible for one of the most diverse and productive ecosystems on Earth, the coral reef.

"Corals are highly efficient at collecting and using light," said first author Dr Daniel Wangpraseurt, a Marie Curie Fellow from Cambridge’s Department of Chemistry. "In our lab, we’re looking for methods to copy and mimic these strategies from nature for commercial applications."

Wangpraseurt and his colleagues 3D printed coral structures and used them as incubators for algae growth. They tested various types of microalgae and found growth rates were 100x higher than in standard liquid growth mediums.

To create the intricate structures of natural corals, the researchers used a rapid 3D bioprinting technique capable of reproducing detailed structures that mimic the complex designs and functions of living tissues. This method can print structures with micrometer-scale resolution in just minutes.

This is critical for replicating structures with live cells, said co-senior author Professor Shaochen Chen, from UC San Diego. "Most of these cells will die if we were to use traditional extrusion-based or inkjet processes because these methods take hours. It would be like keeping a fish out of the water; the cells that we work with won’t survive if kept too long out of their culture media. Our process is high throughput and offers really fast printing speeds, so it’s compatible with human cells, animal cells, and even algae cells in this case," he said.

The coral-inspired structures were highly efficient at redistributing light, just like natural corals. Only biocompatible materials were used to fabricate the 3D printed bionic corals.

"We developed an artificial coral tissue and skeleton with a combination of polymer gels and hydrogels doped with cellulose nanomaterials to mimic the optical properties of living corals," said co-senior author Dr Silvia Vignolini, also from Cambridge's Department of Chemistry. "Cellulose is an abundant biopolymer; it is excellent at scattering light and we used it to optimise delivery of light into photosynthetic algae."

The team used an optical analogue to ultrasound, called optical coherence tomography, to scan living corals and utilise the models for their 3D printed designs. The custom-made 3D bioprinter uses light to print coral micro-scale structures in seconds. The printed coral copies natural coral structures and light-harvesting properties, creating an artificial host-microenvironment for the living microalgae.

"By copying the host microhabitat, we can also use our 3D bioprinted corals as a model system for the coral-algal symbiosis, which is urgently needed to understand the breakdown of the symbiosis during coral reef decline," said Wangpraseurt. "There are many different applications for our new technology. We have recently created a company, called mantaz, that uses coral-inspired light-harvesting approaches to cultivate algae for bioproducts in developing countries. We hope that our technique will be scalable so it can have a real impact on the algal biosector and ultimately reduce greenhouse gas emissions that are responsible for coral reef death."

This study was funded by the European Union’s Horizon 2020 research and innovation programme, the European Research Council, the David Phillips Fellowship, the National Institutes of Health, the National Science Foundation, the Carlsberg Foundation and the Villum Foundation.

Reference:
Daniel Wangpraseurt et al. ‘Bionic 3D printed corals.’ Nature Communications (2020). DOI: 10.1038/s41467-020-15486-4

Researchers from Cambridge, UK, and Germany have reconstructed the early “evolutionary paths” of COVID-19 in humans – as infection spread from Wuhan out to Europe and North America – using genetic network techniques.

By analysing the first 160 complete virus genomes to be sequenced from human patients, the scientists have mapped some of the original spread of the new coronavirus through its mutations, which creates different viral lineages.

“There are too many rapid mutations to neatly trace a COVID-19 family tree. We used a mathematical network algorithm to visualise all the plausible trees simultaneously,” said geneticist Dr Peter Forster, lead author from the University of Cambridge.  

“These techniques are mostly known for mapping the movements of prehistoric human populations through DNA. We think this is one of the first times they have been used to trace the infection routes of a coronavirus like COVID-19.” 

The team used data from virus genomes sampled from across the world between 24 December 2019 and 4 March 2020. The research revealed three distinct “variants” of COVID-19, consisting of clusters of closely related lineages, which they label ‘A’, ‘B’ and ‘C’.

Forster and colleagues found that the closest type of COVID-19 to the one discovered in bats – type ‘A’, the “original human virus genome” – was present in Wuhan, but surprisingly was not the city’s predominant virus type.

Mutated versions of ‘A’ were seen in Americans reported to have lived in Wuhan, and a large number of A-type viruses were found in patients from the US and Australia.

Wuhan’s major virus type, ‘B’, was prevalent in patients from across East Asia. However, the variant didn’t travel much beyond the region without further mutations – implying a "founder event" in Wuhan, or “resistance” against this type of COVID-19 outside East Asia, say researchers.

The ‘C’ variant is the major European type, found in early patients from France, Italy, Sweden and England. It is absent from the study’s Chinese mainland sample, but seen in Singapore, Hong Kong and South Korea.

The new analysis also suggests that one of the earliest introductions of the virus into Italy came via the first documented German infection on January 27, and that another early Italian infection route was related to a “Singapore cluster”.

Importantly, the researchers say that their genetic networking techniques accurately traced established infection routes: the mutations and viral lineages joined the dots between known cases.

As such, the scientists argue that these “phylogenetic” methods could be applied to the very latest coronavirus genome sequencing to help predict future global hot spots of disease transmission and surge.

“Phylogenetic network analysis has the potential to help identify undocumented COVID-19 infection sources, which can then be quarantined to contain further spread of the disease worldwide,” said Forster, a fellow of the McDonald Institute of Archaeological Research at Cambridge, as well as the University’s Institute of Continuing Education.

The findings are published today in the journal Proceedings of the National Academy of Sciences (PNAS). The software used in the study, as well as classifications for over 1,000 coronavirus genomes and counting, is available free at www.fluxus-technology.com.   

Variant ‘A’, most closely related to the virus found in both bats and pangolins, is described as “the root of the outbreak” by researchers. Type ‘B’ is derived from ‘A’, separated by two mutations, then ‘C’ is in turn a “daughter” of ‘B’.

Researchers say the localisation of the ‘B’ variant to East Asia could result from a “founder effect”: a genetic bottleneck that occurs when, in the case of a virus, a new type is established from a small, isolated group of infections.

Forster argues that there is another explanation worth considering. “The Wuhan B-type virus could be immunologically or environmentally adapted to a large section of the East Asian population. It may need to mutate to overcome resistance outside East Asia. We seem to see a slower mutation rate in East Asia than elsewhere, in this initial phase.”

He added: “The viral network we have detailed is a snapshot of the early stages of an epidemic, before the evolutionary paths of COVID-19 become obscured by vast numbers of mutations. It’s like catching an incipient supernova in the act.”

Since today’s PNAS study was conducted, the research team has extended its analysis to 1,001 viral genomes. While yet to be peer-reviewed, Forster says the latest work suggests that the first infection and spread among humans of COVID-19 occurred between mid-September and early December. 

The phylogenetic network methods used by researchers – allowing the visualisation of hundreds of evolutionary trees simultaneously in one simple graph – were pioneered in New Zealand in 1979, then developed by German mathematicians in the 1990s.

These techniques came to the attention of archaeologist Professor Colin Renfrew, a co-author of the new PNAS study, in 1998. Renfrew went on to establish one of the first archaeogenetics research groups in the world at the University of Cambridge.  

Using a modified PCR test, the CITIID team are able to diagnose infection in four hours, much faster than the current tests, which take over 24 hours to return a result. They are using the test on NHS healthcare workers at Addenbrooke’s Hospital, part of Cambridge University Hospitals (CUH) NHS Trust, as well as on staff who have been asked to isolate due to potential contact with infected individuals but who may or may not be themselves infected.

The polymerase chain reaction (PCR) test allows scientists to extract a miniscule amount of RNA from the virus and copy it millions of times, creating an amount large enough to confirm presence of the virus. However, because of the infectious nature of the virus, which causes COVID-19 disease, until now samples have had to be processed in containment level 3 facilities, which slows down the testing process because of the safety requirements.

Now, a team led by Professor Stephen Baker at CITIID has found a way of inactivating the virus at the point of sampling, enabling them to carry out their work rapidly in level 2 facilities, which are more widely available and have less restrictions on their use. Their method is based on previous work led by Professor Ian Goodfellow and colleagues in the Department of Pathology.

“PCR tests for coronavirus infection are slow because of the safety requirements necessary for handing this potentially lethal virus,” said Professor Baker. “Now that we are able to inactivate it, we can dramatically improve the turnaround time from swab to result. This will be extremely useful in helping test NHS frontline staff and helping clarify whether self-isolating healthcare staff are infected or negative, potentially allowing them to return to work.”

Samples are taken using nasal swabs. Once the virus has been inactivated, the samples are sent to the lab and tested. The whole process takes just four hours.

Professor Baker says that the team has enough reagents – the chemical substances used to detect the virus – to allow them to test 200 samples a day, five days a week, for the next 10-12 weeks. He hopes to be able to expand this capacity in the future.      

The PCR test has been adapted from the in-house, real-time PCR developed in the routine diagnostics laboratory at Addenbrooke’s by Dr Martin Curran. It has been validated against the approved Public Health England tests and is now being offered as a screening tool for NHS staff. Establishing and validating the process for staff testing has been a successful collaboration between clinical and occupational health staff at CUH with the University team.

The University, together with CUH, is already at the forefront of responding to the UK Government’s five pillar plan to boost testing for COVID-19. Yesterday, it announced a partnership with AstraZeneca and GSK to set up a new testing laboratory at the University’s Anne McLaren Building. This facility will be used for high throughput screening for COVID-19 testing and to explore the use of alternative chemical reagents for test kits in order to help overcome current supply shortages. 

Last week, it announced that a spinout company had developed a point-of-care, rapid diagnostic test for patients that was capable of diagnosing COVID-19 in 90 minutes. This test is also being evaluated at CITIID.

More and more evidence suggests that SARS-CoV2, the virus that causes COVID-19, may be commonly transmitted before individuals show symptoms.

Professor Babak Javid, a consultant in infectious diseases at Cambridge University Hospitals NHS Trust, as well as a professor at Tsinghua University in Beijing, said: “We know that a lot of transmission of the coronavirus occurs before people show any symptoms. Wearing masks is primarily to protect others, as well as offering some degree of protection to the wearer.”

Writing in an Editorial for The BMJ, the team argue that the potential benefits vastly outweigh the possible downsides associated with mask use. Studies performed prior to the current emergency were of variable quality, and didn’t take into account how likely individuals were to comply with wearing a mask. The authors argue that in the midst of a pandemic, people are much more likely to follow guidelines.

The evidence for masks is no worse than other widely adopted and promoted behaviours, such as hand-washing, they say. Even if masks are only 20% effective at reducing transmission, previous models for an influenza pandemic suggested that substantial numbers of cases may still be prevented. Widespread education campaigns, such as those promoting handwashing at present, could help ensure the masks are used properly and mitigate some of the concerns over their proper use.

Due to shortages of medical masks for our healthcare workers, the researchers recommend cloth masks for the public. Dr Michael Weekes from the Cambridge Institute of Therapeutic Immunology and Infectious Disease (CITIID) said: “The evidence to support the use of masks in non-clinical settings may be limited, but the mass manufacture and use of cloth masks is cheap and easy, compared to the societal economic costs associated with isolation and social distancing measures.”

Dr Nicholas Matheson, also from CITIID, and a consultant at Cambridge University Hospitals NHS Trust and NHS Blood and Transplant added: “As we prepare to enter a ‘new normal’, wearing a mask in public may become the face of our unified action in the fight against this common threat, and reinforce the importance of social distancing measures.

Reference
Covid-19: should the public wear face masks? BMJ; 9 Apr 2020; DOI: 10.1136/bmj.m1442 

Since adult hearts cannot usually repair themselves once damaged, harnessing the power of this gene represents major progress towards the first curative treatment for heart disease. 

“This is really exciting because scientists have been trying to make heart cells proliferate for a long time. None of the current heart disease treatments are able to reverse degeneration of the heart tissue – they only slow progression of the disease. Now we’ve found a way to do it in a mouse model,” said Dr Catherine Wilson, a researcher in the University of Cambridge’s Department of Pharmacology, who led the study.

The cell cycle - through which cells make copies of themselves - is tightly controlled in mammalian cells. Cancer develops when cells start to replicate themselves uncontrollably, and the Myc gene plays a key role in the process. Myc is known to be overactive in the vast majority of cancers, so targeting this gene is one of the highest priorities in cancer research. Much recent research has focused on trying to take control of Myc as a means of cancer therapy.

When the researchers made Myc overactive in a mouse model, they saw its cancerous effects in organs including the liver and lungs: huge amount of cells started replicating over the course of a few days. But in the heart, nothing much happened. 

They found that Myc-driven activity in heart muscle cells is critically dependent on the level of another protein called Cyclin T1, made by a gene called Ccnt1, within the cells. When the Ccnt1 and Myc genes are expressed together, the heart switches into a regenerative state and its cells start to replicate. The results are published today in the journal Nature Communications.

“When these two genes were overexpressed together in the heart muscle cells of adult mice we saw extensive cell replication, leading to a large increase in the number of heart muscle cells,” said Wilson. 

Heart failure affects around 23 million people worldwide each year, and there is currently no cure. After a heart attack, an adult human heart can lose up to one billion heart muscle cells - called cardiomyocytes. Unlike many other organs in the body, the adult heart can’t regenerate itself, so these cells are never replaced. Their loss reduces the strength of the heart and causes scar formation, heart failure and ultimately death. 

Using a next generation sequencing technology called ChIP, the researchers were able to watch the action of Myc in the heart cells. Myc produces a protein - called a transcription factor - that binds to the DNA in specific cells and activates gene expression. But despite the protein binding successfully, the heart cells didn’t start to replicate themselves because the protein could not activate gene expression. Another protein vital to gene expression, Cyclin T1, was deficient in the heart. Adding it to the cells with the overactive Myc caused the cells to start proliferating.

“None of the current treatment options can reverse the degeneration of heart tissue. The inability of the heart to regenerate itself is a significant unmet clinical need,” said Wilson. “We found that even when Myc is switched on in a heart, the other tools aren’t there to make it work, which may be one of the reasons heart cancer is so extremely rare. Now we know what’s missing, we can add it and make the cells replicate.”

As the world’s population grows and the prevalence of heart failure increases, the cost of patient care is anticipated to increase dramatically. The researchers hope to develop their finding into a genetic therapy for the treatment of heart disease. “We want to use short-term, switchable technologies to turn on Myc and Cyclin T1 in the heart. That way we won’t leave any genetic footprint that might inadvertently lead to cancer formation,” said Wilson.

This research was funded by Cancer Research UK.

Reference
Bywater, M. J. et al: ‘Reactivation of Myc transcription in the mouse heart unlocks its proliferative capacity.’ Nature Communications, April 2020. DOI: 10.1038/s41467-020-15552-x