̽��ֱ�� of Cambridge - Engineering

Handheld device could transform heart disease screening

sc604 — Tue, 08 Apr 2025 08:10:47 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, developed a device that makes it easy for people with or without medical training to record heart sounds accurately. Unlike a stethoscope, the device works well even if it’s not placed precisely on the chest: its larger, flexible sensing area helps capture clearer heart sounds than traditional stethoscopes.

̽��ֱ��device can also be used over clothing, making it more comfortable for patients – especially women – during routine check-ups or community heart health screening programmes.

̽��ֱ��heart sound recordings can be saved on the device, which can then be used to detect signs of heart valve disease. ̽��ֱ��researchers are also developing a machine learning algorithm which can detect signs of valve disease automatically. ̽��ֱ��results are reported in the IEEE Journal of Biomedical and Health Informatics.

Heart valve disease (valvular heart disease or VHD) has been called the ‘next cardiac epidemic,’ with a prognosis worse than many forms of cancer. Up to 50% of patients with significant VHD remain undiagnosed, and many patients only see their doctor when the disease has advanced and they are experiencing significant complications.

In the UK, the NHS and NICE have identified early detection of heart valve disease as a key goal, both to improve quality of life for patients, and to decrease costs.

An examination with a stethoscope, or auscultation, is the way that most diagnoses of heart valve disease are made. However, just 38% of patients who present to their GP with symptoms of valve disease receive an examination with a stethoscope.

“ ̽��ֱ��symptoms of VHD can be easily confused with certain respiratory conditions, which is why so many patients don’t receive a stethoscope examination,” said Professor Anurag Agarwal from Cambridge’s Department of Engineering, who led the research. “However, the accuracy of stethoscope examination for diagnosing heart valve disease is fairly poor, and it requires a GP to conduct the examination.”

In addition, a stethoscope examination requires patients to partially undress, which is both time consuming in short GP appointments, and can be uncomfortable for patients, particularly for female patients in routine screening programmes.

̽��ֱ��‘gold standard’ for diagnosing heart valve disease is an echocardiogram, but this can only be done in a hospital and NHS waiting lists are extremely long – between six to nine months at many hospitals.

“To help get waiting lists down, and to make sure we’re diagnosing heart valve disease early enough that simple interventions can improve quality of life, we wanted to develop an alternative to a stethoscope that is easy to use as a screening tool,” said Agarwal.

Agarwal and his colleagues have developed a handheld device, about the diameter of a drinks coaster, that could be a solution. Their device can be used by any health professional to accurately record heart sounds, and can be used over clothes.

While a regular or electronic stethoscope has a single sensor, the Cambridge-developed device has six, meaning it is easier for the doctor or nurse – or even someone without any medical training – to get an accurate reading, simply because the surface area is so much bigger.

̽��ֱ��device contains materials that can transmit vibration so that it can be used over clothes, which is particularly important when conducting community screening programmes to protect patient privacy. Between each of the six sensors is a gel that absorbs vibration, so the sensors don’t interfere with each other.

̽��ֱ��researchers tested the device on healthy participants with different body shapes and sizes and recorded their heart sounds. Their next steps will be to test the device in a clinical setting on a variety of patients, against results from an echocardiogram.

In parallel with the development of the device, the researchers have developed a machine learning algorithm that can use the recorded heart sounds to detect signs of valve disease automatically. Early tests of the algorithm suggest that it outperforms GPs in detecting heart valve disease.

“If successful, this device could become an affordable and scalable solution for heart health screening, especially in areas with limited medical resources,” said Agarwal.

̽��ֱ��researchers say that the device could be a useful tool to triage patients who are waiting for an echocardiogram, so that those with signs of valve disease can be seen in a hospital sooner.

A patent has been filed on the device by Cambridge Enterprise, the ̽��ֱ��’s commercialisation arm. Anurag Agarwal is a Fellow of Emmanuel College, Cambridge.

Reference:
Andrew McDonald et al. ‘A flexible multi-sensor device enabling handheld sensing of heart sounds by untrained users.’ IEEE Journal of Biomedical and Health Informatics (2025). DOI: 10.1109/JBHI.2025.3551882

Researchers have developed a handheld device that could potentially replace stethoscopes as a tool for detecting certain types of heart disease.

This device could become an affordable and scalable solution for heart health screening, especially in areas with limited medical resources

Anurag Agarwal

Acoustics Lab

Person demonstrating use of a handheld device for heart disease screening

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes

Researchers demonstrate the UK’s first long-distance ultra-secure communication over a quantum network

sc604 — Mon, 07 Apr 2025 23:38:58 +0000

̽��ֱ��team, from the Universities of Bristol and Cambridge, created the network, which uses standard fibreoptic infrastructure, but relies on a variety of quantum phenomena to enable ultra-secure data transfer.

̽��ֱ��network uses two types of quantum key distribution (QKD) schemes: ‘unhackable’ encryption keys hidden inside particles of light; and distributed entanglement: a phenomenon that causes quantum particles to be intrinsically linked.

̽��ֱ��researchers demonstrated the capabilities of the network via a live, quantum-secure video conference link, the transfer of encrypted medical data, and secure remote access to a distributed data centre. ̽��ֱ��data was successfully transmitted between Bristol and Cambridge – a fibre distance of over 410 kilometres.

This is the first time that a long-distance network, encompassing different quantum-secure technologies such as entanglement distribution, has been successfully demonstrated. ̽��ֱ��researchers presented their results at the 2025 Optical Fiber Communications Conference (OFC) in San Francisco.

Quantum communications offer unparalleled security advantages compared to classical telecommunications solutions. These technologies are immune against future cyber-attacks, even with quantum computers, which – once fully developed – will have the potential to break through even the strongest cryptographic methods currently in use.

In the past few years, researchers have been working to build and use quantum communication networks. China recently set up a massive network that covers 4,600 kilometres by connecting five cities using both fibreoptics and satellites. In Madrid, researchers created a smaller network with nine connection points that use different types of QKD to securely share information.

In 2019, researchers at Cambridge and Toshiba demonstrated a metro-scale quantum network operating at record key rates of millions of key bits per second. And in 2020, researchers in Bristol built a network that could share entanglement between multiple users. Similar quantum network trials have been demonstrated in Singapore, Italy and the USA.

Despite this progress, no one has built a large, long-distance network that can handle both types of QKD, entanglement distribution, and regular data transmission all at once, until now.

̽��ֱ��experiment demonstrates the potential of quantum networks to accommodate different quantum-secure approaches simultaneously with classical communications infrastructure. It was carried out using the UK’s Quantum Network (UKQN), established over the last decade by the same team, supported by funding from the Engineering and Physical Sciences Research Council (EPSRC), and as part of the Quantum Communications Hub project.

“This is a crucial step toward building a quantum-secured future for our communities and society,” said co-author Dr Rui Wang, Lecturer for Future Optical Networks in the Smart Internet Lab's High Performance Network Research Group at the ̽��ֱ�� of Bristol. “More importantly, it lays the foundation for a large-scale quantum internet—connecting quantum nodes and devices through entanglement and teleportation on a global scale.”

“This marks the culmination of more than ten years of work to design and build the UK Quantum Network,” said co-author Adrian Wonfor from Cambridge’s Department of Engineering. “Not only does it demonstrate the use of multiple quantum communications technologies, but also the secure key management systems required to allow seamless end-to-end encryption between us.”

“This is a significant step in delivering quantum security for the communications we all rely upon in our daily lives at a national scale,” said co-author Professor Richard Penty, also from Cambridge and who headed the Quantum Networks work package in the Quantum Communications Hub. “It would not have been possible without the close collaboration of the two teams at Cambridge and Bristol, the support of our industrial partners Toshiba, BT, Adtran and Cisco, and our funders at UKRI.”

“This is an extraordinary achievement which highlights the UK’s world-class strengths in quantum networking technology,” said Gerald Buller, Director of the IQN Hub, based at Heriot-Watt ̽��ֱ��. “This exciting demonstration is precisely the kind of work the Integrated Quantum Networks Hub will support over the coming years, developing the technologies, protocols and standards which will establish a resilient, future-proof, national quantum communications infrastructure.”

̽��ֱ��current UKQN covers two metropolitan quantum networks around Bristol and Cambridge, which are connected via a ‘backbone’ of four long-distance optical fibre links spanning 410 kilometres with three intermediate nodes.

̽��ֱ��network uses single-mode fibre over the EPSRC National Dark Fibre Facility (which provides dedicated fibre for research purposes), and low-loss optical switches allowing network reconfiguration of both classical and quantum signal traffic.

̽��ֱ��team will pursue this work further through a newly funded EPSRC project, the Integrated Quantum Networks Hub, whose vision is to establish quantum networks at all distance scales, from local networking of quantum processors to national-scale entanglement networks for quantum-safe communication, distributed computing and sensing, all the way to intercontinental networking via low-earth orbit satellites.

Reference:
R. Yang et al. ‘A UK Nationwide Heterogeneous Quantum Network.’ Paper presented at the 2025 Optical Fiber Communications Conference and Exhibition (OFC): https://www.ofcconference.org/en-us/home/schedule/

Researchers have successfully demonstrated the UK’s first long-distance ultra-secure transfer of data over a quantum communications network, including the UK’s first long-distance quantum-secured video call.

MR.Cole_Photographer via Getty Images

Abstract background

Yes

Cambridge researchers named 2025 Schmidt Science Fellows

Anonymous — Mon, 07 Apr 2025 11:55:17 +0000

Now in its eighth year, the Fellowship provides financial support for a postdoctoral placement of one to two years at a world-class research institution.

̽��ֱ��funding equips scientists to apply their knowledge to a new field of study with the goal of accelerating discoveries, and to develop their leadership potential.

Dr Poppy Oldroyd, a 2025 Schmidt Science Fellow from the Department of Engineering, plans to pioneer a new frontier in understanding brain communication through optical measurements, ultimately advancing treatments for memory-related diseases.

̽��ֱ��human brain communicates through intricate networks of neurons, crucial for learning and memory. However, how these neural conversations translate into memory formation remains a mystery in neuroscience. Oldroyd’s research aims to use light-based tools, like advanced optogenetics, to explore these pathways in detail. By uncovering how specific brain circuits contribute to learning and memory, this research could revolutionise our understanding of these essential brain functions.

Ultimately, this knowledge may enhance our comprehension of memory-related disorders like Alzheimer’s disease and epilepsy.

Dr Matthew McLouglin, a 2025 Schmidt Science Fellow from the Cambridge Stem Cell Institute, plans to develop tools to study how our cells age in real time. This will help us understand why we age and how we might promote healthy aging to improve quality of life in the elderly.

Our DNA is organised into structures called chromosomes. Each chromosome has a protective cap, the ‘telomere’, which is partially lost with each cell division. In old age, cells cannot function properly due to the loss of telomeres, increasing the risk of age-related diseases such as cancer and dementia. McLoughlin will use cutting-edge imaging technology to track the loss of telomeres over time, understanding how telomeres are lost and why this stops cells from functioning.

Oldroyd and McLoughlin join a community of 209 Schmidt Science Fellows from nearly 40 countries who are leaders in interdisciplinary science.

“Philanthropic funding of scientific research, and especially support of early-career researchers, has never been more important,” said Wendy Schmidt, who co-founded Schmidt Science Fellows with her husband, Eric.

“By providing Schmidt Science Fellows with support, community, and freedom to work across disciplines and gain new insights, we hope they’ll tackle some of the world’s most vexing challenges, achieve breakthroughs and help create a healthier, more resilient world for all.”

Established in 2017, Schmidt Science Fellows is a programme of Schmidt Sciences delivered in partnership with the Rhodes Trust.

̽��ֱ��2025 Fellows represent 15 nationalities, including researchers from Jordan and the United Arab Emirates for the first time in the programme’s history.

This year’s cohort will work on a range of problems from cancer treatment to quantum technologies to sustainability.

Alongside their research Placement, Fellows participate in a 12-month interdisciplinary Science Leadership Programme.

Each year, Schmidt Science Fellows works in partnership with more than 100 universities to identify candidates for the Fellowship.

Nominees are selected via an application process that includes an academic review with panels of experts in their original disciplines and final interviews with a multidisciplinary panel of scientists and private sector leaders.

“ ̽��ֱ��Schmidt Science Fellows Program is cultivating a dynamic global community of remarkable scientists and champions of interdisciplinary research,” said Stu Feldman, Chief Scientist at Schmidt Sciences.

“Their work exemplifies Schmidt Sciences’ commitment to support pioneering approaches that will drive the next era of discovery and innovation.”

̽��ֱ��2025 Schmidt Science Fellows represent 27 nominating universities, including, for the first time, McGill ̽��ֱ�� in Canada, RWTH Aachen ̽��ֱ�� in Germany, Tecnológico de Monterrey in Mexico, ̽��ֱ�� of California, Los Angeles in the US, and ̽��ֱ�� of Groningen in the Netherlands.

Two ̽��ֱ�� of Cambridge researchers are among the thirty-two early career researchers, tackling issues from improving food security to developing better medical implants, who have been announced as the 2025 Schmidt Science Fellows.

Schmidt Science Fellows

Poppy Oldroyd (left) and Matthew McLoughlin (right)

Yes

̽��ֱ�� spin-out secures funding to improve AI energy efficiency and bandwidth

Anonymous — Mon, 24 Mar 2025 14:24:15 +0000

CamGraPhIC - co-founded Professor Andrea Ferrari, Director of the Cambridge Graphene Centre, and Dr Marco Romagnoli of CNIT in Italy - is developing new types of photonic circuits for energy-efficient, high-bandwidth, optical interconnect technology.

̽��ֱ��investment will support continued innovation in graphene photonics transceivers, a technology that could improve energy efficiency, reduce latency, and increase bandwidth for artificial intelligence (AI) and cellular data transmission.

With the investment, CamGraPhIC will enhance its research and development capabilities and establish a pilot manufacturing line. ̽��ֱ��facility will demonstrate a scalable mass production process compatible with commercial semiconductor and photonics foundries.

̽��ֱ��funding round was co-led by CDP Venture Capital, NATO Innovation Fund, Sony Innovation Fund, and Join Capital, with participation from Bosch Ventures, Frontier IP Group plc, and Indaco Ventures.

CamGraPhIC’s graphene-based transceivers provide a viable, stable, and scalable alternative to current silicon-based photonics. These transceivers deliver higher bandwidth density, and exceptional latency performance, while consuming 80% less energy than traditional pluggable data centre optical transceivers.

̽��ֱ��company say their innovation is particularly effective for transferring large volumes of data between graphic processing units (GPUs) and high bandwidth memory (HBM), which are fundamental to generative AI and high-performance computing.

̽��ֱ��transceivers operate efficiently across a broad temperature range, eliminating the need for complex and costly cooling systems. Thanks to a simplified device architecture enabled by the integration of graphene into the photonic structure, these transceivers are also more cost-effective to manufacture.

Thanks to this funding, CamGraPhIC will expand to applications in avionics, automotive advanced driver-assistance systems (ADAS), and space, where rugged, high-performance transceivers offer significant technical and commercial advantages over existing technologies.

“We are thrilled for this new phase in the journey towards commercialisation of CamGraPhIC groundbreaking and energy efficient devices, to speed up development of AI hardware, without impacting global emissions,” said Ferrari. “Having Sony, Bosch and NATO as shareholders and board members will help focus the work towards the most relevant applications, including defence and security.”

“With the backing of renowned investors, we are excited to propel towards commercialisation the Graphene Photonics technology to overcome the interconnection bottleneck of regenerative AI processing systems and driving the next leap in scaling bandwidth and reducing energy consumption for the future of optical data communications, ” said Romagnoli, former Head of Research Sector - Advanced Technologies for Photonic Integration of the Pisa National Inter- ̽��ֱ�� Consortium for Telecommunications (CNIT), and now Chief Scientific Officer of CamGraPhIC.

A ̽��ֱ�� of Cambridge spin-out company working to improve AI efficiency and bandwidth has raised €25 million in new funding.

CamGraPhIC

Marco Romagnoli (L) and Andrea Ferrari (R)

Yes

Fully AI driven weather prediction system could start revolution in forecasting

sc604 — Thu, 20 Mar 2025 15:56:54 +0000

̽��ֱ��system, Aardvark Weather, has been supported by the Alan Turing Institute, Microsoft Research and the European Centre for Medium Range Weather Forecasts. It provides a blueprint for a new approach to weather forecasting with the potential to transform current practices. The results are reported in the journal Nature.

“Aardvark reimagines current weather prediction methods offering the potential to make weather forecasts faster, cheaper, more flexible and more accurate than ever before, helping to transform weather prediction in both developed and developing countries,” said Professor Richard Turner from Cambridge’s Department of Engineering, who led the research. “Aardvark is thousands of times faster than all previous weather forecasting methods.”

Current weather forecasts are generated through a complex set of stages, each taking several hours to run on powerful supercomputers. Aside from daily usage, the development, maintenance and use of these systems require significant time and large teams of experts.

More recently, research by Huawei, Google, and Microsoft has shown that one component of the weather forecasting pipeline, the numerical solver (which calculates how weather evolves over time), can be replaced with AI, resulting in faster and more accurate predictions. This combination of AI and traditional approaches is now being used by the European Centre for Medium Range Weather Forecasts (ECMWF).

But with Aardvark, researchers have replaced the entire weather prediction pipeline with a single, simple machine learning model. ̽��ֱ��new model takes in observations from satellites, weather stations and other sensors and outputs both global and local forecasts.

This fully AI driven approach means predictions that were once produced using many models – each requiring a supercomputer and a large support team to run – can now be produced in minutes on a desktop computer.

When using just 10% of the input data of existing systems, Aardvark already outperforms the United States national GFS forecasting system on many variables. It is also competitive with United States Weather Service forecasts that use input from dozens of weather models and analysis by expert human forecasters.

“These results are just the beginning of what Aardvark can achieve,” said first author Anna Allen, from Cambridge’s Department of Computer Science and Technology. “This end-to-end learning approach can be easily applied to other weather forecasting problems, for example hurricanes, wildfires, and tornadoes. Beyond weather, its applications extend to broader Earth system forecasting, including air quality, ocean dynamics, and sea ice prediction.”

̽��ֱ��researchers say that one of the most exciting aspects of Aardvark is its flexibility and simple design. Because it learns directly from data it can be quickly adapted to produce bespoke forecasts for specific industries or locations, whether that's predicting temperatures for African agriculture or wind speeds for a renewable energy company in Europe.

This contrasts to traditional weather prediction systems where creating a customised system takes years of work by large teams of researchers.

“ ̽��ֱ��weather forecasting systems we all rely on have been developed over decades, but in just 18 months, we’ve been able to build something that’s competitive with the best of these systems, using just a tenth of the data on a desktop computer,” said Turner, who is also Lead Researcher for Weather Prediction at the Alan Turing Institute.

This capability has the potential to transform weather prediction in developing countries where access to the expertise and computational resources required to develop conventional systems is not typically available.

“Unleashing AI’s potential will transform decision-making for everyone from policymakers and emergency planners to industries that rely on accurate weather forecasts,” said Dr Scott Hosking from ̽��ֱ��Alan Turing Institute. “Aardvark’s breakthrough is not just about speed, it’s about access. By shifting weather prediction from supercomputers to desktop computers, we can democratise forecasting, making these powerful technologies available to developing nations and data-sparse regions around the world.”

“Aardvark would not have been possible without decades of physical-model development by the community, and we are particularly indebted to ECMWF for their ERA5 dataset which is essential for training Aardvark,” said Turner.

“It is essential that academia and industry work together to address technological challenges and leverage new opportunities that AI offers,” said Matthew Chantry from ECMWF. “Aardvark’s approach combines both modularity with end-to-end forecasting optimisation, ensuring effective use of the available datasets."

“Aardvark represents not only an important achievement in AI weather prediction but it also reflects the power of collaboration and bringing the research community together to improve and apply AI technology in meaningful ways,” said Dr Chris Bishop, from Microsoft Research.

̽��ֱ��next steps for Aardvark include developing a new team within the Alan Turing Institute led by Turner, who will explore the potential to deploy Aardvark in the global south and integrate the technology into the Institute’s wider work to develop high-precision environmental forecasting for weather, oceans and sea ice.

Reference:
Anna Allen, Stratis Markou et al. ‘End-to-end data-driven weather prediction.’ Nature (2025). DOI: 10.1038/s41586-025-08897-0

Adapted from a media release by ̽��ֱ��Alan Turing Institute

A new AI weather prediction system, developed by researchers from the ̽��ֱ�� of Cambridge, can deliver accurate forecasts tens of times faster and using thousands of times less computing power than current AI and physics-based forecasting systems.

̽��ֱ��Alan Turing Institute

Professor Richard Turner using Aardvark Weather

Yes

Scientists develop ‘smart pyjamas’ to monitor sleep disorders

sc604 — Tue, 18 Feb 2025 11:06:44 +0000

̽��ֱ��team, led by the ̽��ֱ�� of Cambridge, developed printed fabric sensors that can monitor breathing by detecting tiny movements in the skin, even when the pyjamas are worn loosely around the neck and chest.

̽��ֱ��sensors embedded in the smart pyjamas were trained using a ‘lightweight’ AI algorithm and can identify six different sleep states with 98.6% accuracy, while ignoring regular sleep movements such as tossing and turning. ̽��ֱ��energy-efficient sensors only require a handful of examples of sleep patterns to successfully identify the difference between regular and disordered sleep.

̽��ֱ��researchers say that their smart pyjamas could be useful for the millions of people in the UK who struggle with disordered sleep to monitor their sleep, and how it might be affected by lifestyle changes. ̽��ֱ��results are reported in the Proceedings of the National Academy of Sciences (PNAS).

Sleep is vital for human health, yet more than 60% of adults experience poor sleep quality, leading to the loss of between 44 and 54 annual working days, and an estimated one percent reduction in global GDP. Sleep behaviours such as mouth breathing, sleep apnoea and snoring are major contributors to poor sleep quality, and can lead to chronic conditions such as cardiovascular disease, diabetes and depression.

“Poor sleep has huge effects on our physical and mental health, which is why proper sleep monitoring is vital,” said Professor Luigi Occhipinti from the Cambridge Graphene Centre, who led the research. “However, the current gold standard for sleep monitoring, polysomnography or PSG, is expensive, complicated and isn’t suitable for long-term use at home.”

Home devices that are simpler than PSG, such as home sleep tests, typically focus on a single condition and are bulky or uncomfortable. Wearable devices such as smartwatches, while more comfortable to wear, can only infer sleep quality, and are not effective for accurately monitoring disordered sleep.

“We need something that is comfortable and easy to use every night, but is accurate enough to provide meaningful information about sleep quality,” said Occhipinti.

To develop the smart pyjamas, Occhipinti and his colleagues built on their earlier work on a smart choker for people with speech impairments. ̽��ֱ��team re-designed the graphene-based sensors for breath analysis during sleep, and made several design improvements to increase sensitivity.

“Thanks to the design changes we made, the sensors are able to detect different sleep states, while ignoring regular tossing and turning,” said Occhinpinti. “ ̽��ֱ��improved sensitivity also means that the smart garment does not need to be worn tightly around the neck, which many people would find uncomfortable. As long as the sensors are in contact with the skin, they provide highly accurate readings.”

̽��ֱ��researchers designed a machine learning model, called SleepNet, that uses the signals captured by the sensors to identify sleep states including nasal breathing, mouth breathing, snoring, teeth grinding, central sleep apnoea (CSA), and obstructive sleep apnoea (OSA). SleepNet is a ‘lightweight’ AI network, that reduces computational complexity to the point where it can be run on portable devices, without the need to connect to computers or servers.

“We pruned the AI model to the point where we could get the lowest computational cost with the highest degree of accuracy,” said Occhinpinti. “This way we are able to embed the main data processors in the sensors directly.”

̽��ֱ��smart pyjamas were tested on healthy patients and those with sleep apnoea, and were able to detect a range of sleep states with an accuracy of 98.6%. By treating the smart pyjamas with a special starching step, they were able to improve the durability of the sensors so they can be run through a regular washing machine.

̽��ֱ��most recent version of the smart pyjamas are also capable of wireless data transfer, meaning the sleep data can be securely transferred to a smartphone or computer.

“Sleep is so important to health, and reliable sleep monitoring can be key in preventative care,” said Occhipinti. “Since this garment can be used at home, rather than in a hospital or clinic, it can alert users to changes in their sleep that they can then discuss with their doctor. Sleep behaviours such as nasal versus mouth breathing are not typically picked up in an NHS sleep analysis, but it can be an indicator of disordered sleep.”

̽��ֱ��researchers are hoping to adapt the sensors for a range of health conditions or home uses, such as baby monitoring, and have been in discussions with different patient groups. They are also working to improve the durability of the sensors for long-term use.

̽��ֱ��research was supported in part by the EU Graphene Flagship, Haleon, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
Chenyu Tang, Wentian Yi et al. ‘A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life.’ PNAS (2025). DOI: 10.1073/pnas.2420498122

Researchers have developed comfortable, washable ‘smart pyjamas’ that can monitor sleep disorders such as sleep apnoea at home, without the need for sticky patches, cumbersome equipment or a visit to a specialist sleep clinic.

We need something that is comfortable and easy to use every night, but is accurate enough to provide meaningful information about sleep quality

Luigi Occhipinti

Illustration and photograph of 'smart pyjamas'

Yes

Cambridge ̽��ֱ�� engineer backed by Formula 1 star Sir Lewis Hamilton

sb726 — Mon, 03 Feb 2025 13:53:00 +0000

Chris Tagnon is the first Cambridge ̽��ֱ�� student to be awarded a Masters in Motorsport scholarship.

Cambridge researchers developing brain implants for treating Parkinson’s disease

sc604 — Thu, 23 Jan 2025 10:33:21 +0000

As part of a £69 million funding programme supported by the Advanced Research + Invention Agency (ARIA), Professor George Malliaras from Cambridge’s Department of Engineering will co-lead a project that uses small clusters of brain cells called midbrain organoids to develop a new type of brain implant, which will be tested in animal models of Parkinson’s disease.

̽��ֱ��project led by Malliaras and Professor Roger Barker from the Department of Clinical Neurosciences, which involves colleagues from the ̽��ֱ�� of Oxford, the ̽��ֱ�� of Lund and BIOS Health, is one of 18 projects funded by ARIA as part of its Precision Neurotechnologies programme, which is supporting research teams across academia, non-profit R&D organisations, and startups dedicated to advancing brain-computer interface technologies.

̽��ֱ��programme will direct £69 million over four years to unlock new methods for interfacing with the human brain at the neural circuit level, to treat many of the most complex neurological and neuropsychiatric disorders, from Alzheimer’s to epilepsy to depression.

By addressing bottlenecks in funding and the lack of precision offered by current approaches, the outputs of this programme will pave the way for addressing a much broader range of conditions than ever before, significantly reducing the social and economic impact of brain disorders across the UK.

Parkinson’s disease occurs when the brain cells that make dopamine (a chemical that helps control movement) die off, causing movement problems and other symptoms. Current treatments, like dopamine-based drugs, work well early on, but can cause serious side effects over time.

In the UK, 130,000 people have Parkinson’s disease, and it costs affected families about £16,000 per year on average – more than £2 billion in the UK annually. As more people age, the number of cases will grow, and new treatments are urgently needed.

One idea is to replace the lost dopamine cells by transplanting new ones into the brain. But these cells need to connect properly to the brain’s network to fix the problem, and current methods don’t fully achieve that.

In the ARIA-funded project, Malliaras and his colleagues are working on a new approach using small clusters of brain cells called midbrain organoids. These will be placed in the right part of the brain in an animal model of Parkinson’s disease. They’ll also use advanced materials and electrical stimulation to help the new cells connect and rebuild the damaged pathways.

“Our ultimate goal is to create precise brain therapies that can restore normal brain function in people with Parkinson’s,” said Malliaras.

“To date, there’s been little serious investment into methodologies that interface precisely with the human brain, beyond ‘brute force’ approaches or highly invasive implants,” said ARIA Programme Director Jacques Carolan. “We’re showing that it’s possible to develop elegant means of understanding, identifying, and treating many of the most complex and devastating brain disorders. Ultimately, this could deliver transformative impact for people with lived experiences of brain disorders.”

Other teams funded by the programme include one at Imperial College London who is developing an entirely new class of biohybridised technology focused on engineering transplanted neurons with bioelectric components. A Glasgow-led team will build advanced neural robots for closed-loop neuromodulation, specifically targeting epilepsy treatment, while London-based Navira will develop a technology for delivering gene therapies across the blood-brain barrier, a crucial step towards developing safer and more effective treatments.

Adapted from an ARIA media release.

Cambridge researchers are developing implants that could help repair the brain pathways damaged by Parkinson’s disease.

Our ultimate goal is to create precise brain therapies that can restore normal brain function in people with Parkinson’s

George Malliaras

Science Photo Library via Getty Images

Substantia nigra in the human brain, illustration

Yes