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The researchers, from the University of Cambridge, combined flexible electronics and soft robotics techniques to develop the devices, which could be used for the diagnosis and treatment of a range of disorders, including epilepsy and chronic pain, or the control of prosthetic limbs.

Current tools for interfacing with the peripheral nerves – the 43 pairs of motor and sensory nerves that connect the brain and the spinal cord – are outdated, bulky and carry a high risk of nerve injury. However, the robotic nerve ‘cuffs’ developed by the Cambridge team are sensitive enough to grasp or wrap around delicate nerve fibres without causing any damage.

Tests of the nerve cuffs in rats showed that the devices only require tiny voltages to change shape in a controlled way, forming a self-closing loop around nerves without the need for surgical sutures or glues.

The researchers say the combination of soft electrical actuators with neurotechnology could be an answer to minimally invasive monitoring and treatment for a range of neurological conditions. The results are reported in the journal Nature Materials.

Electric nerve implants can be used to either stimulate or block signals in target nerves. For example, they might help relieve pain by blocking pain signals, or they could be used to restore movement in paralysed limbs by sending electrical signals to the nerves. Nerve monitoring is also standard surgical procedure when operating in areas of the body containing a high concentration of nerve fibres, such as anywhere near the spinal cord.

These implants allow direct access to nerve fibres, but they come with certain risks. “Nerve implants come with a high risk of nerve injury,” said Professor George Malliaras from Cambridge’s Department of Engineering, who led the research. “Nerves are small and highly delicate, so anytime you put something large, like an electrode, in contact with them, it represents a danger to the nerves.”

“Nerve cuffs that wrap around nerves are the least invasive implants currently available, but despite this they are still too bulky, stiff and difficult to implant, requiring significant handling and potential trauma to the nerve,” said co-author Dr Damiano Barone from Cambridge’s Department of Clinical Neurosciences.

The researchers designed a new type of nerve cuff made from conducting polymers, normally used in soft robotics. The ultra-thin cuffs are engineered in two separate layers. Applying tiny amounts of electricity – just a few hundred millivolts – causes the devices to swell or shrink.

The cuffs are small enough that they could be rolled up into a needle and injected near the target nerve. When activated electrically, the cuffs will change their shape to wrap around the nerve, allowing nerve activity to be monitored or altered.

“To ensure the safe use of these devices inside the body, we have managed to reduce the voltage required for actuation to very low values,” said Dr Chaoqun Dong, the paper’s first author. “What's even more significant is that these cuffs can change shape in both directions and be reprogrammed. This means surgeons can adjust how tightly the device fits around a nerve until they get the best results for recording and stimulating the nerve.”

Tests in rats showed that the cuffs could be successfully placed without surgery, and formed a self-closing loop around the target nerve. The researchers are planning further testing of the devices in animal models, and are hoping to begin testing in humans within the next few years.

“Using this approach, we can reach nerves that are difficult to reach through open surgery, such as the nerves that control, pain, vision or hearing, but without the need to implant anything inside the brain,” said Barone. “The ability to place these cuffs so they wrap around the nerves makes this a much easier procedure for surgeons, and it’s less risky for patients.”

“The ability to make an implant that can change shape through electrical activation opens up a range of future possibilities for highly targeted treatments,” said Malliaras. “In future, we might be able to have implants that can move through the body, or even into the brain – it makes you dream how we could use technology to benefit patients in future.”

The research was supported in part by the Swiss National Science Foundation, the Cambridge Trust, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

 

Reference:
Chaoqun Dong et al. ‘Electrochemically actuated microelectrodes for minimally invasive peripheral nerve interfaces.’ Nature Materials (2024). DOI: 10.1038/s41563-024-01886-0

Researchers have developed tiny, flexible devices that can wrap around individual nerve fibres without damaging them.

The ability to make an implant that can change shape through electrical activation opens up a range of future possibilities for highly targeted treatmentsGeorge MalliarasXH4D via iStock / Getty Images PlusIllustration of the human nervous system

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

The researchers, from the University of Cambridge, combined flexible electronics and soft robotics techniques to develop the devices, which could be used for the diagnosis and treatment of a range of disorders, including epilepsy and chronic pain, or the control of prosthetic limbs.

Current tools for interfacing with the peripheral nerves – the 43 pairs of motor and sensory nerves that connect the brain and the spinal cord – are outdated, bulky and carry a high risk of nerve injury. However, the robotic nerve ‘cuffs’ developed by the Cambridge team are sensitive enough to grasp or wrap around delicate nerve fibres without causing any damage.

Tests of the nerve cuffs in rats showed that the devices only require tiny voltages to change shape in a controlled way, forming a self-closing loop around nerves without the need for surgical sutures or glues.

The researchers say the combination of soft electrical actuators with neurotechnology could be an answer to minimally invasive monitoring and treatment for a range of neurological conditions. The results are reported in the journal Nature Materials.

Electric nerve implants can be used to either stimulate or block signals in target nerves. For example, they might help relieve pain by blocking pain signals, or they could be used to restore movement in paralysed limbs by sending electrical signals to the nerves. Nerve monitoring is also standard surgical procedure when operating in areas of the body containing a high concentration of nerve fibres, such as anywhere near the spinal cord.

These implants allow direct access to nerve fibres, but they come with certain risks. “Nerve implants come with a high risk of nerve injury,” said Professor George Malliaras from Cambridge’s Department of Engineering, who led the research. “Nerves are small and highly delicate, so anytime you put something large, like an electrode, in contact with them, it represents a danger to the nerves.”

“Nerve cuffs that wrap around nerves are the least invasive implants currently available, but despite this they are still too bulky, stiff and difficult to implant, requiring significant handling and potential trauma to the nerve,” said co-author Dr Damiano Barone from Cambridge’s Department of Clinical Neurosciences.

The researchers designed a new type of nerve cuff made from conducting polymers, normally used in soft robotics. The ultra-thin cuffs are engineered in two separate layers. Applying tiny amounts of electricity – just a few hundred millivolts – causes the devices to swell or shrink.

The cuffs are small enough that they could be rolled up into a needle and injected near the target nerve. When activated electrically, the cuffs will change their shape to wrap around the nerve, allowing nerve activity to be monitored or altered.

“To ensure the safe use of these devices inside the body, we have managed to reduce the voltage required for actuation to very low values,” said Dr Chaoqun Dong, the paper’s first author. “What's even more significant is that these cuffs can change shape in both directions and be reprogrammed. This means surgeons can adjust how tightly the device fits around a nerve until they get the best results for recording and stimulating the nerve.”

Tests in rats showed that the cuffs could be successfully placed without surgery, and formed a self-closing loop around the target nerve. The researchers are planning further testing of the devices in animal models, and are hoping to begin testing in humans within the next few years.

“Using this approach, we can reach nerves that are difficult to reach through open surgery, such as the nerves that control, pain, vision or hearing, but without the need to implant anything inside the brain,” said Barone. “The ability to place these cuffs so they wrap around the nerves makes this a much easier procedure for surgeons, and it’s less risky for patients.”

“The ability to make an implant that can change shape through electrical activation opens up a range of future possibilities for highly targeted treatments,” said Malliaras. “In future, we might be able to have implants that can move through the body, or even into the brain – it makes you dream how we could use technology to benefit patients in future.”

The research was supported in part by the Swiss National Science Foundation, the Cambridge Trust, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

 

Reference:
Chaoqun Dong et al. ‘Electrochemically actuated microelectrodes for minimally invasive peripheral nerve interfaces.’ Nature Materials (2024). DOI: 10.1038/s41563-024-01886-0

Researchers have developed tiny, flexible devices that can wrap around individual nerve fibres without damaging them.

The ability to make an implant that can change shape through electrical activation opens up a range of future possibilities for highly targeted treatmentsGeorge MalliarasXH4D via iStock / Getty Images PlusIllustration of the human nervous system

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

For the first time, the research has shown that this risk is higher in people living in areas of greater socioeconomic deprivation.

Breast cancer is the most commonly diagnosed cancer in the UK. Around 56,000 people in the UK are diagnosed each year, the vast majority (over 99%) of whom are women. Improvements in earlier diagnosis and in treatments mean that five year survival rates have been increasing over time, reaching 87% by 2017 in England.

People who survive breast cancer are at risk of second primary cancer, but until now the exact risk has been unclear. Previously published research suggested that women and men who survive breast cancer are at a 24% and 27% greater risk of a non-breast second primary cancer than the wider population respectively. There have been also suggestions that second primary cancer risks differ by the age at breast cancer diagnosis.

To provide more accurate estimates, a team led by researchers at the University of Cambridge analysed data from over 580,000 female and over 3,500 male breast cancer survivors diagnosed between 1995 and 2019 using the National Cancer Registration Dataset. The results of their analysis are published today in Lancet Regional Health – Europe.

First author Isaac Allen from the Department of Public Health and Primary Care at the University of Cambridge said: “It’s important for us to understand to what extent having one type of cancer puts you at risk of a second cancer at a different site. The female and male breast cancer survivors whose data we studied were at increased risk of a number of second cancers. Knowing this can help inform conversations with their care teams to look out for signs of potential new cancers.”

The researchers found significantly increased risks of cancer in the contralateral (that is, unaffected) breast and for endometrium and prostate cancer in females and males, respectively. Females who survived breast cancer were at double the risk of contralateral breast cancer compared to the general population and at 87% greater risk of endometrial cancer, 58% greater risk of myeloid leukaemia and 25% greater risk of ovarian cancer.

Age of diagnosis was important, too – females diagnosed with breast cancer under the age of 50 were 86% more likely to develop a second primary cancer compared to the general population of the same age, whereas women diagnosed after age 50 were at a 17% increased risk. One potential explanation is that a larger number of younger breast cancer survivors may have inherited genetic alterations that increase risk for multiple cancers. For example, women with inherited changes to the BRCA1 and BRCA2 genes are at increased risk of contralateral breast cancer, ovarian and pancreatic cancer.

Females from the most socioeconomically deprived backgrounds were at 35% greater risk of a second primary cancer compared to females from the least deprived backgrounds. These differences were primarily driven by non-breast cancer risks, particularly for lung, kidney, head and neck, bladder, oesophageal and stomach cancers. This may be because smoking, obesity, and alcohol consumption – established risk factors for these cancers – are more common among more deprived groups.

Allen, a PhD student at Clare Hall, added: “This is further evidence of the health inequalities that people from more deprived backgrounds experience. We need to fully understand why they are at greater risk of second cancers so that we can intervene and reduce this risk.”

Male breast cancer survivors were 55 times more likely than the general male population to develop contralateral breast cancer – though the researchers stress that an individual’s risk was still very low. For example, for every 100 men diagnosed with breast cancer at age 50 or over, about three developed contralateral breast cancer during a 25 year period.  Male breast cancer survivors were also 58% more likely than the general male population to develop prostate cancer.

Professor Antonis Antoniou from the Department of Public Health and Primary Care at the University of Cambridge, the study’s senior author, said: “This is the largest study to date to look at the risk in breast cancer survivors of developing a second cancer. We were able to carry this out and calculate more accurate estimates because of the outstanding data sets available to researchers through the NHS.”

The research was funded by Cancer Research UK with support from the National Institute for Health and Care Research Cambridge Biomedical Research Centre.

Cancer Research UK’s senior cancer intelligence manager, Katrina Brown, said: “This study shows us that the risk of second primary cancers is higher in people who have had breast cancer, and this can differ depending on someone’s socioeconomic background. But more research is needed to understand what is driving this difference and how to tackle these health inequalities.”

People who are concerned about their cancer risk should contact their GP for advice. If you or someone close to you have been affected by cancer and you’ve got questions, you can call Cancer Research UK nurses on freephone 0808 800 4040, Monday to Friday.

Reference
Allen, I, et al. Risks of second primary cancers among 584,965 female and male breast cancer survivors in England: a 25-year retrospective cohort study. Lancet Regional Health – Europe; 24 April 2024: DOI: 10.1016/j.lanepe.2024.100903

Survivors of breast cancer are at significantly higher risk of developing second cancers, including endometrial and ovarian cancer for women and prostate cancer for men, according to new research studying data from almost 600,000 patients in England.

It’s important for us to understand to what extent having one type of cancer puts you at risk of a second cancer at a different site. Knowing this can help inform conversations with their care teams to look out for signs of potential new cancersIsaac AllenNational Cancer InstituteDoctor standing near woman patient doing breast cancer

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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.

YesLicence type: Public Domain

For the first time, the research has shown that this risk is higher in people living in areas of greater socioeconomic deprivation.

Breast cancer is the most commonly diagnosed cancer in the UK. Around 56,000 people in the UK are diagnosed each year, the vast majority (over 99%) of whom are women. Improvements in earlier diagnosis and in treatments mean that five year survival rates have been increasing over time, reaching 87% by 2017 in England.

People who survive breast cancer are at risk of second primary cancer, but until now the exact risk has been unclear. Previously published research suggested that women and men who survive breast cancer are at a 24% and 27% greater risk of a non-breast second primary cancer than the wider population respectively. There have been also suggestions that second primary cancer risks differ by the age at breast cancer diagnosis.

To provide more accurate estimates, a team led by researchers at the University of Cambridge analysed data from over 580,000 female and over 3,500 male breast cancer survivors diagnosed between 1995 and 2019 using the National Cancer Registration Dataset. The results of their analysis are published today in Lancet Regional Health – Europe.

First author Isaac Allen from the Department of Public Health and Primary Care at the University of Cambridge said: “It’s important for us to understand to what extent having one type of cancer puts you at risk of a second cancer at a different site. The female and male breast cancer survivors whose data we studied were at increased risk of a number of second cancers. Knowing this can help inform conversations with their care teams to look out for signs of potential new cancers.”

The researchers found significantly increased risks of cancer in the contralateral (that is, unaffected) breast and for endometrium and prostate cancer in females and males, respectively. Females who survived breast cancer were at double the risk of contralateral breast cancer compared to the general population and at 87% greater risk of endometrial cancer, 58% greater risk of myeloid leukaemia and 25% greater risk of ovarian cancer.

Age of diagnosis was important, too – females diagnosed with breast cancer under the age of 50 were 86% more likely to develop a second primary cancer compared to the general population of the same age, whereas women diagnosed after age 50 were at a 17% increased risk. One potential explanation is that a larger number of younger breast cancer survivors may have inherited genetic alterations that increase risk for multiple cancers. For example, women with inherited changes to the BRCA1 and BRCA2 genes are at increased risk of contralateral breast cancer, ovarian and pancreatic cancer.

Females from the most socioeconomically deprived backgrounds were at 35% greater risk of a second primary cancer compared to females from the least deprived backgrounds. These differences were primarily driven by non-breast cancer risks, particularly for lung, kidney, head and neck, bladder, oesophageal and stomach cancers. This may be because smoking, obesity, and alcohol consumption – established risk factors for these cancers – are more common among more deprived groups.

Allen, a PhD student at Clare Hall, added: “This is further evidence of the health inequalities that people from more deprived backgrounds experience. We need to fully understand why they are at greater risk of second cancers so that we can intervene and reduce this risk.”

Male breast cancer survivors were 55 times more likely than the general male population to develop contralateral breast cancer – though the researchers stress that an individual’s risk was still very low. For example, for every 100 men diagnosed with breast cancer at age 50 or over, about three developed contralateral breast cancer during a 25 year period.  Male breast cancer survivors were also 58% more likely than the general male population to develop prostate cancer.

Professor Antonis Antoniou from the Department of Public Health and Primary Care at the University of Cambridge, the study’s senior author, said: “This is the largest study to date to look at the risk in breast cancer survivors of developing a second cancer. We were able to carry this out and calculate more accurate estimates because of the outstanding data sets available to researchers through the NHS.”

The research was funded by Cancer Research UK with support from the National Institute for Health and Care Research Cambridge Biomedical Research Centre.

Cancer Research UK’s senior cancer intelligence manager, Katrina Brown, said: “This study shows us that the risk of second primary cancers is higher in people who have had breast cancer, and this can differ depending on someone’s socioeconomic background. But more research is needed to understand what is driving this difference and how to tackle these health inequalities.”

People who are concerned about their cancer risk should contact their GP for advice. If you or someone close to you have been affected by cancer and you’ve got questions, you can call Cancer Research UK nurses on freephone 0808 800 4040, Monday to Friday.

Reference
Allen, I, et al. Risks of second primary cancers among 584,965 female and male breast cancer survivors in England: a 25-year retrospective cohort study. Lancet Regional Health – Europe; 24 April 2024: DOI: 10.1016/j.lanepe.2024.100903

Survivors of breast cancer are at significantly higher risk of developing second cancers, including endometrial and ovarian cancer for women and prostate cancer for men, according to new research studying data from almost 600,000 patients in England.

It’s important for us to understand to what extent having one type of cancer puts you at risk of a second cancer at a different site. Knowing this can help inform conversations with their care teams to look out for signs of potential new cancersIsaac AllenNational Cancer InstituteDoctor standing near woman patient doing breast cancer

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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.

YesLicence type: Public Domain

The researchers, led by the University of Cambridge, are developing low-cost light-harvesting semiconductors that power devices for converting water into clean hydrogen fuel, using just the power of the sun. These semiconducting materials, known as copper oxides, are cheap, abundant and non-toxic, but their performance does not come close to silicon, which dominates the semiconductor market.

However, the researchers found that by growing the copper oxide crystals in a specific orientation so that electric charges move through the crystals at a diagonal, the charges move much faster and further, greatly improving performance. Tests of a copper oxide light harvester, or photocathode, based on this fabrication technique showed a 70% improvement over existing state-of-the-art oxide photocathodes, while also showing greatly improved stability.

The researchers say their results, reported in the journal Nature, show how low-cost materials could be fine-tuned to power the transition away from fossil fuels and toward clean, sustainable fuels that can be stored and used with existing energy infrastructure.

Copper (I) oxide, or cuprous oxide, has been touted as a cheap potential replacement for silicon for years, since it is reasonably effective at capturing sunlight and converting it into electric charge. However, much of that charge tends to get lost, limiting the material’s performance.

“Like other oxide semiconductors, cuprous oxide has its intrinsic challenges,” said co-first author Dr Linfeng Pan from Cambridge’s Department of Chemical Engineering and Biotechnology. “One of those challenges is the mismatch between how deep light is absorbed and how far the charges travel within the material, so most of the oxide below the top layer of material is essentially dead space.”

“For most solar cell materials, it’s defects on the surface of the material that cause a reduction in performance, but with these oxide materials, it’s the other way round: the surface is largely fine, but something about the bulk leads to losses,” said Professor Sam Stranks, who led the research. “This means the way the crystals are grown is vital to their performance.”

To develop cuprous oxides to the point where they can be a credible contender to established photovoltaic materials, they need to be optimised so they can efficiently generate and move electric charges – made of an electron and a positively-charged electron ‘hole’ – when sunlight hits them.

One potential optimisation approach is single-crystal thin films – very thin slices of material with a highly-ordered crystal structure, which are often used in electronics. However, making these films is normally a complex and time-consuming process.

Using thin film deposition techniques, the researchers were able to grow high-quality cuprous oxide films at ambient pressure and room temperature. By precisely controlling growth and flow rates in the chamber, they were able to ‘shift’ the crystals into a particular orientation. Then, using high temporal resolution spectroscopic techniques, they were able to observe how the orientation of the crystals affected how efficiently electric charges moved through the material.

“These crystals are basically cubes, and we found that when the electrons move through the cube at a body diagonal, rather than along the face or edge of the cube, they move an order of magnitude further,” said Pan. “The further the electrons move, the better the performance.”

“Something about that diagonal direction in these materials is magic,” said Stranks. “We need to carry out further work to fully understand why and optimise it further, but it has so far resulted in a huge jump in performance.” Tests of a cuprous oxide photocathode made using this technique showed an increase in performance of more than 70% over existing state-of-the-art electrodeposited oxide photocathodes.

“In addition to the improved performance, we found that the orientation makes the films much more stable, but factors beyond the bulk properties may be at play,” said Pan.

The researchers say that much more research and development is still needed, but this and related families of materials could have a vital role in the energy transition.

“There’s still a long way to go, but we’re on an exciting trajectory,” said Stranks. “There’s a lot of interesting science to come from these materials, and it’s interesting for me to connect the physics of these materials with their growth, how they form, and ultimately how they perform.”

The research was a collaboration with École Polytechnique Fédérale de Lausanne, Nankai University and Uppsala University. The research was supported in part by the European Research Council, the Swiss National Science Foundation, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). Sam Stranks is Professor of Optoelectronics in the Department of Chemical Engineering and Biotechnology, and a Fellow of Clare College, Cambridge.

 

Reference:
Linfeng Pan, Linjie Dai et al. ‘High carrier mobility along the [111] orientation in Cu2O photoelectrodes.’ Nature (2024). DOI: 10.1038/s41586-024-07273-8

For more information on energy-related research in Cambridge, please visit the Energy IRC, which brings together Cambridge’s research knowledge and expertise, in collaboration with global partners, to create solutions for a sustainable and resilient energy landscape for generations to come. 

Researchers have found a way to super-charge the ‘engine’ of sustainable fuel generation – by giving the materials a little twist.

orange via Getty ImagesAbstract orange swirls

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

The researchers, led by the University of Cambridge, are developing low-cost light-harvesting semiconductors that power devices for converting water into clean hydrogen fuel, using just the power of the sun. These semiconducting materials, known as copper oxides, are cheap, abundant and non-toxic, but their performance does not come close to silicon, which dominates the semiconductor market.

However, the researchers found that by growing the copper oxide crystals in a specific orientation so that electric charges move through the crystals at a diagonal, the charges move much faster and further, greatly improving performance. Tests of a copper oxide light harvester, or photocathode, based on this fabrication technique showed a 70% improvement over existing state-of-the-art oxide photocathodes, while also showing greatly improved stability.

The researchers say their results, reported in the journal Nature, show how low-cost materials could be fine-tuned to power the transition away from fossil fuels and toward clean, sustainable fuels that can be stored and used with existing energy infrastructure.

Copper (I) oxide, or cuprous oxide, has been touted as a cheap potential replacement for silicon for years, since it is reasonably effective at capturing sunlight and converting it into electric charge. However, much of that charge tends to get lost, limiting the material’s performance.

“Like other oxide semiconductors, cuprous oxide has its intrinsic challenges,” said co-first author Dr Linfeng Pan from Cambridge’s Department of Chemical Engineering and Biotechnology. “One of those challenges is the mismatch between how deep light is absorbed and how far the charges travel within the material, so most of the oxide below the top layer of material is essentially dead space.”

“For most solar cell materials, it’s defects on the surface of the material that cause a reduction in performance, but with these oxide materials, it’s the other way round: the surface is largely fine, but something about the bulk leads to losses,” said Professor Sam Stranks, who led the research. “This means the way the crystals are grown is vital to their performance.”

To develop cuprous oxides to the point where they can be a credible contender to established photovoltaic materials, they need to be optimised so they can efficiently generate and move electric charges – made of an electron and a positively-charged electron ‘hole’ – when sunlight hits them.

One potential optimisation approach is single-crystal thin films – very thin slices of material with a highly-ordered crystal structure, which are often used in electronics. However, making these films is normally a complex and time-consuming process.

Using thin film deposition techniques, the researchers were able to grow high-quality cuprous oxide films at ambient pressure and room temperature. By precisely controlling growth and flow rates in the chamber, they were able to ‘shift’ the crystals into a particular orientation. Then, using high temporal resolution spectroscopic techniques, they were able to observe how the orientation of the crystals affected how efficiently electric charges moved through the material.

“These crystals are basically cubes, and we found that when the electrons move through the cube at a body diagonal, rather than along the face or edge of the cube, they move an order of magnitude further,” said Pan. “The further the electrons move, the better the performance.”

“Something about that diagonal direction in these materials is magic,” said Stranks. “We need to carry out further work to fully understand why and optimise it further, but it has so far resulted in a huge jump in performance.” Tests of a cuprous oxide photocathode made using this technique showed an increase in performance of more than 70% over existing state-of-the-art electrodeposited oxide photocathodes.

“In addition to the improved performance, we found that the orientation makes the films much more stable, but factors beyond the bulk properties may be at play,” said Pan.

The researchers say that much more research and development is still needed, but this and related families of materials could have a vital role in the energy transition.

“There’s still a long way to go, but we’re on an exciting trajectory,” said Stranks. “There’s a lot of interesting science to come from these materials, and it’s interesting for me to connect the physics of these materials with their growth, how they form, and ultimately how they perform.”

The research was a collaboration with École Polytechnique Fédérale de Lausanne, Nankai University and Uppsala University. The research was supported in part by the European Research Council, the Swiss National Science Foundation, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). Sam Stranks is Professor of Optoelectronics in the Department of Chemical Engineering and Biotechnology, and a Fellow of Clare College, Cambridge.

 

Reference:
Linfeng Pan, Linjie Dai et al. ‘High carrier mobility along the [111] orientation in Cu2O photoelectrodes.’ Nature (2024). DOI: 10.1038/s41586-024-07273-8

For more information on energy-related research in Cambridge, please visit the Energy IRC, which brings together Cambridge’s research knowledge and expertise, in collaboration with global partners, to create solutions for a sustainable and resilient energy landscape for generations to come. 

Researchers have found a way to super-charge the ‘engine’ of sustainable fuel generation – by giving the materials a little twist.

orange via Getty ImagesAbstract orange swirls

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

The virtual centres, supported by the charity LifeArc, will focus on areas where there are significant unmet needs. They will tackle barriers that ordinarily prevent new tests and treatments reaching patients with rare diseases and speed up the delivery of rare disease treatment trials.

The centres will bring together leading scientists and rare disease clinical specialists from across the UK for the first time, encouraging new collaborations across different research disciplines and providing improved access to facilities and training.

LifeArc Centre for Rare Mitochondrial Diseases

Professor Patrick Chinnery will lead the LifeArc Centre for Rare Mitochondrial Diseases, a national partnership with the Lily Foundation and Muscular Dystrophy UK, together with key partners at UCL, Newcastle University and three other centres (Oxford, Birmingham and Manchester).

Mitochondrial diseases are genetic disorders affecting 1 in 5,000 people. They often cause progressive damage to the brain, eyes, muscles, heart and liver, leading to severe disability and a shorter life. There is currently have no cure for most conditions, however, new opportunities to treat mitochondrial diseases have been identified in the last five years, meaning that it’s a critical time for research development. The £7.5M centre will establish a national platform that will connect patient groups, knowledge and infrastructure in order to accelerate new treatments getting to clinical trial.

Professor Chinnery said: “The new LifeArc centre unites scientific and clinical strengths from across the UK. For the first time we will form a single team, focussed on developing new treatments for mitochondrial diseases which currently have no cure.”

Adam Harraway has Mitochondrial Disease and says he lives in constant fear of what might go wrong next with his condition. “With rare diseases such as these, it can feel like the questions always outweigh the answers. The news of this investment from LifeArc fills me with hope for the future. To know that there are so many wonderful people and organisations working towards treatments and cures makes me feel seen and heard. It gives a voice to people who often have to suffer in silence, and I'm excited to see how this project can help Mito patients in the future."

LifeArc Centre for Rare Respiratory Diseases

Professor Stefan Marciniak will co-lead the LifeArc Centre for Rare Respiratory Diseases, a UK wide collaborative centre co-created in partnership with patients and charities. This Centre is a partnership between Universities and NHS Trusts across the UK, co-led by Edinburgh with Nottingham, Dundee, Cambridge, Southampton, University College London and supported by six other centres (Belfast, Cardiff, Leeds, Leicester, Manchester and Royal Brompton).

For the first time ever, it will provide a single ‘go to’ centre that will connect children and adults with rare respiratory disease with clinical experts, researchers, investors and industry leaders across the UK. The £9.4M centre will create a UK-wide biobank of patient samples and models of disease that will allow researchers to advance pioneering therapies and engage with industry and regulatory partners to develop innovative human clinical studies.

Professor Marciniak said: “There are many rare lung diseases, and together those affected constitute a larger underserved group of patients. The National Translational Centre for Rare Respiratory Diseases brings together expertise from across the UK to find effective treatments and train the next generation of rare disease researchers.”

Former BBC News journalist and presenter, Philippa Thomas, has the rare incurable lung disease, Lymphangioleiomyomatosis (LAM). Her condition has stabilised but for many people, the disease can be severely life-limiting. Philippa said: “There is so little research funding for rare respiratory diseases, that getting treatment - let alone an accurate diagnosis - really does feel like a lottery. It is also terrifying being diagnosed with something your GP will never have heard of.”

Globally, there are more than 300 million people living with rare diseases. However, rare disease research can be fragmented. Researchers can lack access to specialist facilities, as well as advice on regulation, trial designs, preclinical regulatory requirements, and translational project management, which are vital in getting new innovations to patients.

Dr Catriona Crombie, Head of Rare Disease at LifeArc, says: “We’re extremely proud to be launching four new LifeArc Translational Centres for Rare Diseases. Each centre has been awarded funding because it holds real promise for delivering change for people living with rare diseases. These centres also have the potential to create a blueprint for accelerating improvements across other disease areas, including common diseases.”

Adapted from a press release from LifeArc

Cambridge researchers will play key roles in two new centres dedicated to developing improved tests, treatments and potentially cures for thousands of people living with rare medical conditions.

The new LifeArc centre unites scientific and clinical strengths from across the UKPatrick ChinneryAlexander_Safonov (Getty)Woman inhaling from a mask nebulizer

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

The virtual centres, supported by the charity LifeArc, will focus on areas where there are significant unmet needs. They will tackle barriers that ordinarily prevent new tests and treatments reaching patients with rare diseases and speed up the delivery of rare disease treatment trials.

The centres will bring together leading scientists and rare disease clinical specialists from across the UK for the first time, encouraging new collaborations across different research disciplines and providing improved access to facilities and training.

LifeArc Centre for Rare Mitochondrial Diseases

Professor Patrick Chinnery will lead the LifeArc Centre for Rare Mitochondrial Diseases, a national partnership with the Lily Foundation and Muscular Dystrophy UK, together with key partners at UCL, Newcastle University and three other centres (Oxford, Birmingham and Manchester).

Mitochondrial diseases are genetic disorders affecting 1 in 5,000 people. They often cause progressive damage to the brain, eyes, muscles, heart and liver, leading to severe disability and a shorter life. There is currently have no cure for most conditions, however, new opportunities to treat mitochondrial diseases have been identified in the last five years, meaning that it’s a critical time for research development. The £7.5M centre will establish a national platform that will connect patient groups, knowledge and infrastructure in order to accelerate new treatments getting to clinical trial.

Professor Chinnery said: “The new LifeArc centre unites scientific and clinical strengths from across the UK. For the first time we will form a single team, focussed on developing new treatments for mitochondrial diseases which currently have no cure.”

Adam Harraway has Mitochondrial Disease and says he lives in constant fear of what might go wrong next with his condition. “With rare diseases such as these, it can feel like the questions always outweigh the answers. The news of this investment from LifeArc fills me with hope for the future. To know that there are so many wonderful people and organisations working towards treatments and cures makes me feel seen and heard. It gives a voice to people who often have to suffer in silence, and I'm excited to see how this project can help Mito patients in the future."

LifeArc Centre for Rare Respiratory Diseases

Professor Stefan Marciniak will co-lead the LifeArc Centre for Rare Respiratory Diseases, a UK wide collaborative centre co-created in partnership with patients and charities. This Centre is a partnership between Universities and NHS Trusts across the UK, co-led by Edinburgh with Nottingham, Dundee, Cambridge, Southampton, University College London and supported by six other centres (Belfast, Cardiff, Leeds, Leicester, Manchester and Royal Brompton).

For the first time ever, it will provide a single ‘go to’ centre that will connect children and adults with rare respiratory disease with clinical experts, researchers, investors and industry leaders across the UK. The £9.4M centre will create a UK-wide biobank of patient samples and models of disease that will allow researchers to advance pioneering therapies and engage with industry and regulatory partners to develop innovative human clinical studies.

Professor Marciniak said: “There are many rare lung diseases, and together those affected constitute a larger underserved group of patients. The National Translational Centre for Rare Respiratory Diseases brings together expertise from across the UK to find effective treatments and train the next generation of rare disease researchers.”

Former BBC News journalist and presenter, Philippa Thomas, has the rare incurable lung disease, Lymphangioleiomyomatosis (LAM). Her condition has stabilised but for many people, the disease can be severely life-limiting. Philippa said: “There is so little research funding for rare respiratory diseases, that getting treatment - let alone an accurate diagnosis - really does feel like a lottery. It is also terrifying being diagnosed with something your GP will never have heard of.”

Globally, there are more than 300 million people living with rare diseases. However, rare disease research can be fragmented. Researchers can lack access to specialist facilities, as well as advice on regulation, trial designs, preclinical regulatory requirements, and translational project management, which are vital in getting new innovations to patients.

Dr Catriona Crombie, Head of Rare Disease at LifeArc, says: “We’re extremely proud to be launching four new LifeArc Translational Centres for Rare Diseases. Each centre has been awarded funding because it holds real promise for delivering change for people living with rare diseases. These centres also have the potential to create a blueprint for accelerating improvements across other disease areas, including common diseases.”

Adapted from a press release from LifeArc

Cambridge researchers will play key roles in two new centres dedicated to developing improved tests, treatments and potentially cures for thousands of people living with rare medical conditions.

The new LifeArc centre unites scientific and clinical strengths from across the UKPatrick ChinneryAlexander_Safonov (Getty)Woman inhaling from a mask nebulizer

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

Artificial intelligence techniques can be helpful for multiple medical applications, such as radiology or oncology, where the ability to recognise patterns in large volumes of data is vital. For these types of applications, the AI compares information against learned examples, draws conclusions, and makes extrapolations.

Now, an international team led by researchers from Ludwig-Maximilians-Universität München (LMU) and including researchers from the University of Cambridge, is exploring the potential of a comparatively new branch of AI for diagnostics and therapy.

The researchers found that causal machine learning (ML) can estimate treatment outcomes – and do so better than the machine learning methods generally used to date. Causal machine learning makes it easier for clinicians to personalise treatment strategies, which individually improves the health of patients.

The results, reported in the journal Nature Medicine, suggest how causal machine learning could improve the effectiveness and safety of a variety of medical treatments.

Classical machine learning recognises patterns and discovers correlations. However, the principle of cause and effect remains closed to machines as a rule; they cannot address the question of why. When making therapy decisions for a patient, the ‘why’ is vital to achieve the best outcomes.

“Developing machine learning tools to address why and what if questions is empowering for clinicians, because it can strengthen their decision-making processes,” said senior author Professor Michaela van der Schaar, Director of the Cambridge Centre for AI in Medicine. “But this sort of machine learning is far more complex than assessing personalised risk.”

For example, when attempting to determine therapy decisions for someone at risk of developing diabetes, classical ML would aim to predict how probable it is for a given patient with a range of risk factors to develop the disease. With causal ML, it would be possible to answer how the risk changes if the patient receives an anti-diabetes drug; that is, gauge the effect of a cause. It would also be possible to estimate whether metformin, the commonly-prescribed medication, would be the best treatment, or whether another treatment plan would be better.

To be able to estimate the effect of a hypothetical treatment, the AI models must learn to answer ‘what if?’ questions. “We give the machine rules for recognising the causal structure and correctly formalising the problem,” said Professor Stefan Feuerriegel from LMU, who led the research. “Then the machine has to learn to recognise the effects of interventions and understand, so to speak, how real-life consequences are mirrored in the data that has been fed into the computers.”

Even in situations for which reliable treatment standards do not yet exist or where randomised studies are not possible for ethical reasons because they always contain a placebo group, machines could still gauge potential treatment outcomes from the available patient data and form hypotheses for possible treatment plans, so the researchers hope.

With such real-world data, it should generally be possible to describe the patient cohorts with ever greater precision in the estimates, bringing individualised therapy decisions that much closer. Naturally, there would still be the challenge of ensuring the reliability and robustness of the methods.

“The software we need for causal ML methods in medicine doesn’t exist out of the box,” says Feuerriegel. “Rather, complex modelling of the respective problem is required, involving close collaboration between AI experts and doctors.”

In other fields, such as marketing, explains Feuerriegel, the work with causal ML has already been in the testing phase for some years now. “Our goal is to bring the methods a step closer to practice,” he said. The paper describes the direction in which things could move over the coming years.”

“I have worked in this area for almost 10 years, working relentlessly in our lab with generations of students to crack this problem,” said van der Schaar, who is affiliated with the Departments of Applied Mathematics and Theoretical Physics, Engineering and Medicine. “It’s an extremely challenging area of machine learning, and seeing it come closer to clinical use, where it will empower clinicians and patients alike, is very satisfying.”

Van der Schaar is continuing to work closely with clinicians to validate these tools in diverse clinical settings, including transplantation, cancer and cardiovascular disease.

Reference:
Stefan Feuerriegel et al. ‘Causal machine learning for predicting treatments.’ Nature Medicine (2024). DOI: 10.1038/s41591-024-02902-1

Adapted from an LMU media release.

Machines can learn not only to make predictions, but to handle causal relationships. An international research team shows how this could make medical treatments safer, more efficient, and more personalised.

Yuichiro Chino via Getty ImagesComputer-generated image of human brain

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

Artificial intelligence techniques can be helpful for multiple medical applications, such as radiology or oncology, where the ability to recognise patterns in large volumes of data is vital. For these types of applications, the AI compares information against learned examples, draws conclusions, and makes extrapolations.

Now, an international team led by researchers from Ludwig-Maximilians-Universität München (LMU) and including researchers from the University of Cambridge, is exploring the potential of a comparatively new branch of AI for diagnostics and therapy.

The researchers found that causal machine learning (ML) can estimate treatment outcomes – and do so better than the machine learning methods generally used to date. Causal machine learning makes it easier for clinicians to personalise treatment strategies, which individually improves the health of patients.

The results, reported in the journal Nature Medicine, suggest how causal machine learning could improve the effectiveness and safety of a variety of medical treatments.

Classical machine learning recognises patterns and discovers correlations. However, the principle of cause and effect remains closed to machines as a rule; they cannot address the question of why. When making therapy decisions for a patient, the ‘why’ is vital to achieve the best outcomes.

“Developing machine learning tools to address why and what if questions is empowering for clinicians, because it can strengthen their decision-making processes,” said senior author Professor Michaela van der Schaar, Director of the Cambridge Centre for AI in Medicine. “But this sort of machine learning is far more complex than assessing personalised risk.”

For example, when attempting to determine therapy decisions for someone at risk of developing diabetes, classical ML would aim to predict how probable it is for a given patient with a range of risk factors to develop the disease. With causal ML, it would be possible to answer how the risk changes if the patient receives an anti-diabetes drug; that is, gauge the effect of a cause. It would also be possible to estimate whether metformin, the commonly-prescribed medication, would be the best treatment, or whether another treatment plan would be better.

To be able to estimate the effect of a hypothetical treatment, the AI models must learn to answer ‘what if?’ questions. “We give the machine rules for recognising the causal structure and correctly formalising the problem,” said Professor Stefan Feuerriegel from LMU, who led the research. “Then the machine has to learn to recognise the effects of interventions and understand, so to speak, how real-life consequences are mirrored in the data that has been fed into the computers.”

Even in situations for which reliable treatment standards do not yet exist or where randomised studies are not possible for ethical reasons because they always contain a placebo group, machines could still gauge potential treatment outcomes from the available patient data and form hypotheses for possible treatment plans, so the researchers hope.

With such real-world data, it should generally be possible to describe the patient cohorts with ever greater precision in the estimates, bringing individualised therapy decisions that much closer. Naturally, there would still be the challenge of ensuring the reliability and robustness of the methods.

“The software we need for causal ML methods in medicine doesn’t exist out of the box,” says Feuerriegel. “Rather, complex modelling of the respective problem is required, involving close collaboration between AI experts and doctors.”

In other fields, such as marketing, explains Feuerriegel, the work with causal ML has already been in the testing phase for some years now. “Our goal is to bring the methods a step closer to practice,” he said. The paper describes the direction in which things could move over the coming years.”

“I have worked in this area for almost 10 years, working relentlessly in our lab with generations of students to crack this problem,” said van der Schaar, who is affiliated with the Departments of Applied Mathematics and Theoretical Physics, Engineering and Medicine. “It’s an extremely challenging area of machine learning, and seeing it come closer to clinical use, where it will empower clinicians and patients alike, is very satisfying.”

Van der Schaar is continuing to work closely with clinicians to validate these tools in diverse clinical settings, including transplantation, cancer and cardiovascular disease.

Reference:
Stefan Feuerriegel et al. ‘Causal machine learning for predicting treatments.’ Nature Medicine (2024). DOI: 10.1038/s41591-024-02902-1

Adapted from an LMU media release.

Machines can learn not only to make predictions, but to handle causal relationships. An international research team shows how this could make medical treatments safer, more efficient, and more personalised.

Yuichiro Chino via Getty ImagesComputer-generated image of human brain

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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 led by the University of Cambridge used experimental and computer modelling techniques to study the porous carbon electrodes used in supercapacitors. They found that electrodes with a more disordered chemical structure stored far more energy than electrodes with a highly ordered structure.

Supercapacitors are a key technology for the energy transition and could be useful for certain forms of public transport, as well as for managing intermittent solar and wind energy generation, but their adoption has been limited by poor energy density.

The researchers say their results, reported in the journal Science, represent a breakthrough in the field and could reinvigorate the development of this important net-zero technology.

Like batteries, supercapacitors store energy, but supercapacitors can charge in seconds or a few minutes, while batteries take much longer. Supercapacitors are far more durable than batteries, and can last for millions of charge cycles. However, the low energy density of supercapacitors makes them unsuitable for delivering long-term energy storage or continuous power.

“Supercapacitors are a complementary technology to batteries, rather than a replacement,” said Dr Alex Forse from Cambridge’s Yusuf Hamied Department of Chemistry, who led the research. “Their durability and extremely fast charging capabilities make them useful for a wide range of applications.”

A bus, train or metro powered by supercapacitors, for example, could fully charge in the time it takes to let passengers off and on, providing it with enough power to reach the next stop. This would eliminate the need to install any charging infrastructure along the line. However, before supercapacitors are put into widespread use, their energy storage capacity needs to be improved.

While a battery uses chemical reactions to store and release charge, a supercapacitor relies on the movement of charged molecules between porous carbon electrodes, which have a highly disordered structure. “Think of a sheet of graphene, which has a highly ordered chemical structure,” said Forse. “If you scrunch up that sheet of graphene into a ball, you have a disordered mess, which is sort of like the electrode in a supercapacitor.”

Because of the inherent messiness of the electrodes, it’s been difficult for scientists to study them and determine which parameters are the most important when attempting to improve performance. This lack of clear consensus has led to the field getting a bit stuck.

Many scientists have thought that the size of the tiny holes, or nanopores, in the carbon electrodes was the key to improved energy capacity. However, the Cambridge team analysed a series of commercially available nanoporous carbon electrodes and found there was no link between pore size and storage capacity.

Forse and his colleagues took a new approach and used nuclear magnetic resonance (NMR) spectroscopy – a sort of ‘MRI’ for batteries – to study the electrode materials. They found that the messiness of the materials – long thought to be a hindrance – was the key to their success.

“Using NMR spectroscopy, we found that energy storage capacity correlates with how disordered the materials are – the more disordered materials can store more energy,” said first author Xinyu Liu, a PhD candidate co-supervised by Forse and Professor Dame Clare Grey. “Messiness is hard to measure – it’s only possible thanks to new NMR and simulation techniques, which is why messiness is a characteristic that’s been overlooked in this field.”

When analysing the electrode materials with NMR spectroscopy, a spectrum with different peaks and valleys is produced. The position of the peak indicates how ordered or disordered the carbon is. “It wasn’t our plan to look for this, it was a big surprise,” said Forse. “When we plotted the position of the peak against energy capacity, a striking correlation came through – the most disordered materials had a capacity almost double that of the most ordered materials.”

So why is mess good? Forse says that’s the next thing the team is working on. More disordered carbons store ions more efficiently in their nanopores, and the team hope to use these results to design better supercapacitors. The messiness of the materials is determined at the point they are synthesised.

“We want to look at new ways of making these materials, to see how far messiness can take you in terms of improving energy storage,” said Forse. “It could be a turning point for a field that’s been stuck for a little while. Clare and I started working on this topic over a decade ago, and it’s exciting to see a lot of our previous fundamental work now having a clear application.”

The research was supported in part by the Cambridge Trusts, the European Research Council, and UK Research and Innovation (UKRI).

Reference:
Xinyu Liu et al. ‘Structural disorder determines capacitance in nanoporous carbons.’ Science (2024). DOI: 10.1126/science.adn6242

For more information on energy-related research in Cambridge, please visit the Energy IRC, which brings together Cambridge’s research knowledge and expertise, in collaboration with global partners, to create solutions for a sustainable and resilient energy landscape for generations to come. 

The energy density of supercapacitors – battery-like devices that can charge in seconds or a few minutes – can be improved by increasing the ‘messiness’ of their internal structure.

This could be a turning point for a field that’s been stuck for a little while. Alex ForseNathan PittLeft to right: Clare Grey, Xinyu Liu, Alex Forse

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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 led by the University of Cambridge used experimental and computer modelling techniques to study the porous carbon electrodes used in supercapacitors. They found that electrodes with a more disordered chemical structure stored far more energy than electrodes with a highly ordered structure.

Supercapacitors are a key technology for the energy transition and could be useful for certain forms of public transport, as well as for managing intermittent solar and wind energy generation, but their adoption has been limited by poor energy density.

The researchers say their results, reported in the journal Science, represent a breakthrough in the field and could reinvigorate the development of this important net-zero technology.

Like batteries, supercapacitors store energy, but supercapacitors can charge in seconds or a few minutes, while batteries take much longer. Supercapacitors are far more durable than batteries, and can last for millions of charge cycles. However, the low energy density of supercapacitors makes them unsuitable for delivering long-term energy storage or continuous power.

“Supercapacitors are a complementary technology to batteries, rather than a replacement,” said Dr Alex Forse from Cambridge’s Yusuf Hamied Department of Chemistry, who led the research. “Their durability and extremely fast charging capabilities make them useful for a wide range of applications.”

A bus, train or metro powered by supercapacitors, for example, could fully charge in the time it takes to let passengers off and on, providing it with enough power to reach the next stop. This would eliminate the need to install any charging infrastructure along the line. However, before supercapacitors are put into widespread use, their energy storage capacity needs to be improved.

While a battery uses chemical reactions to store and release charge, a supercapacitor relies on the movement of charged molecules between porous carbon electrodes, which have a highly disordered structure. “Think of a sheet of graphene, which has a highly ordered chemical structure,” said Forse. “If you scrunch up that sheet of graphene into a ball, you have a disordered mess, which is sort of like the electrode in a supercapacitor.”

Because of the inherent messiness of the electrodes, it’s been difficult for scientists to study them and determine which parameters are the most important when attempting to improve performance. This lack of clear consensus has led to the field getting a bit stuck.

Many scientists have thought that the size of the tiny holes, or nanopores, in the carbon electrodes was the key to improved energy capacity. However, the Cambridge team analysed a series of commercially available nanoporous carbon electrodes and found there was no link between pore size and storage capacity.

Forse and his colleagues took a new approach and used nuclear magnetic resonance (NMR) spectroscopy – a sort of ‘MRI’ for batteries – to study the electrode materials. They found that the messiness of the materials – long thought to be a hindrance – was the key to their success.

“Using NMR spectroscopy, we found that energy storage capacity correlates with how disordered the materials are – the more disordered materials can store more energy,” said first author Xinyu Liu, a PhD candidate co-supervised by Forse and Professor Dame Clare Grey. “Messiness is hard to measure – it’s only possible thanks to new NMR and simulation techniques, which is why messiness is a characteristic that’s been overlooked in this field.”

When analysing the electrode materials with NMR spectroscopy, a spectrum with different peaks and valleys is produced. The position of the peak indicates how ordered or disordered the carbon is. “It wasn’t our plan to look for this, it was a big surprise,” said Forse. “When we plotted the position of the peak against energy capacity, a striking correlation came through – the most disordered materials had a capacity almost double that of the most ordered materials.”

So why is mess good? Forse says that’s the next thing the team is working on. More disordered carbons store ions more efficiently in their nanopores, and the team hope to use these results to design better supercapacitors. The messiness of the materials is determined at the point they are synthesised.

“We want to look at new ways of making these materials, to see how far messiness can take you in terms of improving energy storage,” said Forse. “It could be a turning point for a field that’s been stuck for a little while. Clare and I started working on this topic over a decade ago, and it’s exciting to see a lot of our previous fundamental work now having a clear application.”

The research was supported in part by the Cambridge Trusts, the European Research Council, and UK Research and Innovation (UKRI).

Reference:
Xinyu Liu et al. ‘Structural disorder determines capacitance in nanoporous carbons.’ Science (2024). DOI: 10.1126/science.adn6242

For more information on energy-related research in Cambridge, please visit the Energy IRC, which brings together Cambridge’s research knowledge and expertise, in collaboration with global partners, to create solutions for a sustainable and resilient energy landscape for generations to come. 

The energy density of supercapacitors – battery-like devices that can charge in seconds or a few minutes – can be improved by increasing the ‘messiness’ of their internal structure.

This could be a turning point for a field that’s been stuck for a little while. Alex ForseNathan PittLeft to right: Clare Grey, Xinyu Liu, Alex Forse

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

Professor Steven Barrett has been appointed Regius Professor of Engineering at the University of Cambridge, effective 1 June. He joins the University from the Massachusetts Institute of Technology (MIT), where he is head of the Department of Aeronautics and Astronautics (AeroAstro).

Barrett’s appointment marks his return to Cambridge, where he was an undergraduate at Pembroke College, and received his PhD. He was a Lecturer in the Department of Engineering from 2008 until 2010, when he joined the faculty at MIT.

The Regius Professorships are royal academic titles created by the monarch. The Regius Professorship in Engineering was announced in 2011, in honour of HRH Prince Philip, The Duke of Edinburgh’s 35 years as Chancellor of the University.

“It’s a pleasure to welcome Steven back to Cambridge to take up one of the University’s most prestigious roles,” said Vice-Chancellor Professor Deborah Prentice. “His work on sustainable aviation will build on Cambridge’s existing strengths, and will help us develop the solutions we need to address the threat posed by climate change.”

Barrett’s research focuses on the impact aviation has on the environment. He has developed a number of solutions to mitigate the impact aviation has on air quality, climate, and noise pollution. The overall goal of his research is to help develop technologies that eliminate the environmental impact of aviation. His work on the first-ever plane with no moving propulsion parts was named one of the 10 Breakthroughs of 2018 by Physics World.

“This is an exciting time to work on sustainable aviation, and Cambridge, as well as the UK more generally, is a wonderful platform to advance that,” said Barrett. “Cambridge’s multidisciplinary Department of Engineering, as well as the platform that the Regius Professorship provides, makes this a great opportunity. I’ve learned a lot at MIT, but I’d always hoped to come back to Cambridge at some point.”

Much of Barrett’s research focuses on the elimination of contrails, line-shaped clouds produced by aircraft engine exhaust in cold and humid conditions. Contrails cause half of all aviation-related global warming – more than the entirety of the UK economy. Barrett uses a combination of satellite observation and machine learning techniques to help determine whether avoiding certain regions of airspace could reduce or eliminate contrail formation.

“It will take several years to make this work, but if it does, it could drastically reduce emissions at a very low cost to the consumer,” said Barrett. “We could make the UK the first ‘Blue Skies’ country in the world – the first without any contrails in the sky.”

“Steven’s pioneering work on contrail formation and avoidance is a key element in reducing the environmental impact of aviation, and will strengthen the UK’s position as a world leader in this area,” said Professor Colm Durkan, Head of Cambridge’s Department of Engineering. “Together with Steven’s work on alternative aviation propulsion systems, this will strengthen Cambridge’s vision of helping us all achieve net zero at an accelerated rate.”

In addition to the Professorship in Engineering, there are seven other Regius Professorships at Cambridge: Divinity, Hebrew, Greek, Civil Law and Physic (all founded by Henry VIII in 1540), History (founded by George I in 1724) and Botany (founded in 2009, to mark the University’s 800th anniversary).

An expert on the environmental impacts of aviation, Barrett joins the University of Cambridge from MIT.

MITSteven Barrett

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

Professor Steven Barrett has been appointed Regius Professor of Engineering at the University of Cambridge, effective 1 June. He joins the University from the Massachusetts Institute of Technology (MIT), where he is head of the Department of Aeronautics and Astronautics (AeroAstro).

Barrett’s appointment marks his return to Cambridge, where he was an undergraduate at Pembroke College, and received his PhD. He was a Lecturer in the Department of Engineering from 2008 until 2010, when he joined the faculty at MIT.

The Regius Professorships are royal academic titles created by the monarch. The Regius Professorship in Engineering was announced in 2011, in honour of HRH Prince Philip, The Duke of Edinburgh’s 35 years as Chancellor of the University.

“It’s a pleasure to welcome Steven back to Cambridge to take up one of the University’s most prestigious roles,” said Vice-Chancellor Professor Deborah Prentice. “His work on sustainable aviation will build on Cambridge’s existing strengths, and will help us develop the solutions we need to address the threat posed by climate change.”

Barrett’s research focuses on the impact aviation has on the environment. He has developed a number of solutions to mitigate the impact aviation has on air quality, climate, and noise pollution. The overall goal of his research is to help develop technologies that eliminate the environmental impact of aviation. His work on the first-ever plane with no moving propulsion parts was named one of the 10 Breakthroughs of 2018 by Physics World.

“This is an exciting time to work on sustainable aviation, and Cambridge, as well as the UK more generally, is a wonderful platform to advance that,” said Barrett. “Cambridge’s multidisciplinary Department of Engineering, as well as the platform that the Regius Professorship provides, makes this a great opportunity. I’ve learned a lot at MIT, but I’d always hoped to come back to Cambridge at some point.”

Much of Barrett’s research focuses on the elimination of contrails, line-shaped clouds produced by aircraft engine exhaust in cold and humid conditions. Contrails cause half of all aviation-related global warming – more than the entirety of the UK economy. Barrett uses a combination of satellite observation and machine learning techniques to help determine whether avoiding certain regions of airspace could reduce or eliminate contrail formation.

“It will take several years to make this work, but if it does, it could drastically reduce emissions at a very low cost to the consumer,” said Barrett. “We could make the UK the first ‘Blue Skies’ country in the world – the first without any contrails in the sky.”

“Steven’s pioneering work on contrail formation and avoidance is a key element in reducing the environmental impact of aviation, and will strengthen the UK’s position as a world leader in this area,” said Professor Colm Durkan, Head of Cambridge’s Department of Engineering. “Together with Steven’s work on alternative aviation propulsion systems, this will strengthen Cambridge’s vision of helping us all achieve net zero at an accelerated rate.”

In addition to the Professorship in Engineering, there are seven other Regius Professorships at Cambridge: Divinity, Hebrew, Greek, Civil Law and Physic (all founded by Henry VIII in 1540), History (founded by George I in 1724) and Botany (founded in 2009, to mark the University’s 800th anniversary).

An expert on the environmental impacts of aviation, Barrett joins the University of Cambridge from MIT.

MITSteven Barrett

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

The clinical knowledge and reasoning skills of GPT-4 are approaching the level of specialist eye doctors, a study led by the University of Cambridge has found.

GPT-4 - a ‘large language model’ - was tested against doctors at different stages in their careers, including unspecialised junior doctors, and trainee and expert eye doctors. Each was presented with a series of 87 patient scenarios involving a specific eye problem, and asked to give a diagnosis or advise on treatment by selecting from four options.

GPT-4 scored significantly better in the test than unspecialised junior doctors, who are comparable to general practitioners in their level of specialist eye knowledge.

GPT-4 gained similar scores to trainee and expert eye doctors - although the top performing doctors scored higher.

The researchers say that large language models aren’t likely to replace healthcare professionals, but have the potential to improve healthcare as part of the clinical workflow.

They say state-of-the-art large language models like GPT-4 could be useful for providing eye-related advice, diagnosis, and management suggestions in well-controlled contexts, like triaging patients, or where access to specialist healthcare professionals is limited.

“We could realistically deploy AI in triaging patients with eye issues to decide which cases are emergencies that need to be seen by a specialist immediately, which can be seen by a GP, and which don’t need treatment,” said Dr Arun Thirunavukarasu, lead author of the study, which he carried out while a student at the University of Cambridge’s School of Clinical Medicine.

He added: “The models could follow clear algorithms already in use, and we’ve found that GPT-4 is as good as expert clinicians at processing eye symptoms and signs to answer more complicated questions.

“With further development, large language models could also advise GPs who are struggling to get prompt advice from eye doctors. People in the UK are waiting longer than ever for eye care.

Large volumes of clinical text are needed to help fine-tune and develop these models, and work is ongoing around the world to facilitate this.

The researchers say that their study is superior to similar, previous studies because they compared the abilities of AI to practicing doctors, rather than to sets of examination results.

“Doctors aren't revising for exams for their whole career. We wanted to see how AI fared when pitted against to the on-the-spot knowledge and abilities of practicing doctors, to provide a fair comparison,” said Thirunavukarasu, who is now an Academic Foundation Doctor at Oxford University Hospitals NHS Foundation Trust.

He added: “We also need to characterise the capabilities and limitations of commercially available models, as patients may already be using them - rather than the internet - for advice.”

The test included questions about a huge range of eye problems, including extreme light sensitivity, decreased vision, lesions, itchy and painful eyes, taken from a textbook used to test trainee eye doctors. This textbook is not freely available on the internet, making it unlikely that its content was included in GPT-4’s training datasets.

The results are published today in the journal PLOS Digital Health.

“Even taking the future use of AI into account, I think doctors will continue to be in charge of patient care. The most important thing is to empower patients to decide whether they want computer systems to be involved or not. That will be an individual decision for each patient to make,” said Thirunavukarasu.

GPT-4 and GPT-3.5 – or ‘Generative Pre-trained Transformers’ - are trained on datasets containing hundreds of billions of words from articles, books, and other internet sources. These are two examples of large language models; others in wide use include Pathways Language Model 2 (PaLM 2) and Large Language Model Meta AI 2 (LLaMA 2).

The study also tested GPT-3.5, PaLM2, and LLaMA with the same set of questions. GPT-4 gave more accurate responses than all of them.

GPT-4 powers the online chatbot ChatGPT to provide bespoke responses to human queries. In recent months, ChatGPT has attracted significant attention in medicine for attaining passing level performance in medical school examinations, and providing more accurate and empathetic messages than human doctors in response to patient queries.

The field of artificially intelligent large language models is moving very rapidly. Since the study was conducted, more advanced models have been released - which may be even closer to the level of expert eye doctors.

Reference: Thirunavukarasu, A.J. et al: ‘Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study.’ PLOS Digital Health, April 2024. DOI: 10.1371/journal.pdig.0000341

A study has found that the AI model GPT-4 significantly exceeds the ability of non-specialist doctors to assess eye problems and provide advice.

We could realistically deploy AI in triaging patients with eye issues to decide which cases are emergencies.Arun ThirunavukarasuMavocado on Getty

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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.

YesLicence type: Attribution-Noncommerical

The clinical knowledge and reasoning skills of GPT-4 are approaching the level of specialist eye doctors, a study led by the University of Cambridge has found.

GPT-4 - a ‘large language model’ - was tested against doctors at different stages in their careers, including unspecialised junior doctors, and trainee and expert eye doctors. Each was presented with a series of 87 patient scenarios involving a specific eye problem, and asked to give a diagnosis or advise on treatment by selecting from four options.

GPT-4 scored significantly better in the test than unspecialised junior doctors, who are comparable to general practitioners in their level of specialist eye knowledge.

GPT-4 gained similar scores to trainee and expert eye doctors - although the top performing doctors scored higher.

The researchers say that large language models aren’t likely to replace healthcare professionals, but have the potential to improve healthcare as part of the clinical workflow.

They say state-of-the-art large language models like GPT-4 could be useful for providing eye-related advice, diagnosis, and management suggestions in well-controlled contexts, like triaging patients, or where access to specialist healthcare professionals is limited.

“We could realistically deploy AI in triaging patients with eye issues to decide which cases are emergencies that need to be seen by a specialist immediately, which can be seen by a GP, and which don’t need treatment,” said Dr Arun Thirunavukarasu, lead author of the study, which he carried out while a student at the University of Cambridge’s School of Clinical Medicine.

He added: “The models could follow clear algorithms already in use, and we’ve found that GPT-4 is as good as expert clinicians at processing eye symptoms and signs to answer more complicated questions.

“With further development, large language models could also advise GPs who are struggling to get prompt advice from eye doctors. People in the UK are waiting longer than ever for eye care.

Large volumes of clinical text are needed to help fine-tune and develop these models, and work is ongoing around the world to facilitate this.

The researchers say that their study is superior to similar, previous studies because they compared the abilities of AI to practicing doctors, rather than to sets of examination results.

“Doctors aren't revising for exams for their whole career. We wanted to see how AI fared when pitted against to the on-the-spot knowledge and abilities of practicing doctors, to provide a fair comparison,” said Thirunavukarasu, who is now an Academic Foundation Doctor at Oxford University Hospitals NHS Foundation Trust.

He added: “We also need to characterise the capabilities and limitations of commercially available models, as patients may already be using them - rather than the internet - for advice.”

The test included questions about a huge range of eye problems, including extreme light sensitivity, decreased vision, lesions, itchy and painful eyes, taken from a textbook used to test trainee eye doctors. This textbook is not freely available on the internet, making it unlikely that its content was included in GPT-4’s training datasets.

The results are published today in the journal PLOS Digital Health.

“Even taking the future use of AI into account, I think doctors will continue to be in charge of patient care. The most important thing is to empower patients to decide whether they want computer systems to be involved or not. That will be an individual decision for each patient to make,” said Thirunavukarasu.

GPT-4 and GPT-3.5 – or ‘Generative Pre-trained Transformers’ - are trained on datasets containing hundreds of billions of words from articles, books, and other internet sources. These are two examples of large language models; others in wide use include Pathways Language Model 2 (PaLM 2) and Large Language Model Meta AI 2 (LLaMA 2).

The study also tested GPT-3.5, PaLM2, and LLaMA with the same set of questions. GPT-4 gave more accurate responses than all of them.

GPT-4 powers the online chatbot ChatGPT to provide bespoke responses to human queries. In recent months, ChatGPT has attracted significant attention in medicine for attaining passing level performance in medical school examinations, and providing more accurate and empathetic messages than human doctors in response to patient queries.

The field of artificially intelligent large language models is moving very rapidly. Since the study was conducted, more advanced models have been released - which may be even closer to the level of expert eye doctors.

Reference: Thirunavukarasu, A.J. et al: ‘Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study.’ PLOS Digital Health, April 2024. DOI: 10.1371/journal.pdig.0000341

A study has found that the AI model GPT-4 significantly exceeds the ability of non-specialist doctors to assess eye problems and provide advice.

We could realistically deploy AI in triaging patients with eye issues to decide which cases are emergencies.Arun ThirunavukarasuMavocado on Getty

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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.

YesLicence type: Attribution-Noncommerical

The researchers, from the University of Cambridge, designed and used an AI-based strategy to identify compounds that block the clumping, or aggregation, of alpha-synuclein, the protein that characterises Parkinson’s.

The team used machine learning techniques to quickly screen a chemical library containing millions of entries, and identified five highly potent compounds for further investigation.

Parkinson’s affects more than six million people worldwide, with that number projected to triple by 2040. No disease-modifying treatments for the condition are currently available. The process of screening large chemical libraries for drug candidates – which needs to happen well before potential treatments can be tested on patients – is enormously time-consuming and expensive, and often unsuccessful.

Using machine learning, the researchers were able to speed up the initial screening process ten-fold, and reduce the cost by a thousand-fold, which could mean that potential treatments for Parkinson’s reach patients much faster. The results are reported in the journal Nature Chemical Biology.

Parkinson’s is the fastest-growing neurological condition worldwide. In the UK, one in 37 people alive today will be diagnosed with Parkinson’s in their lifetime. In addition to motor symptoms, Parkinson’s can also affect the gastrointestinal system, nervous system, sleeping patterns, mood and cognition, and can contribute to a reduced quality of life and significant disability.

Proteins are responsible for important cell processes, but when people have Parkinson’s, these proteins go rogue and cause the death of nerve cells. When proteins misfold, they can form abnormal clusters called Lewy bodies, which build up within brain cells stopping them from functioning properly.

“One route to search for potential treatments for Parkinson’s requires the identification of small molecules that can inhibit the aggregation of alpha-synuclein, which is a protein closely associated with the disease,” said Professor Michele Vendruscolo from the Yusuf Hamied Department of Chemistry, who led the research. “But this is an extremely time-consuming process – just identifying a lead candidate for further testing can take months or even years.”

While there are currently clinical trials for Parkinson’s currently underway, no disease-modifying drug has been approved, reflecting the inability to directly target the molecular species that cause the disease.

This has been a major obstacle in Parkinson’s research, because of the lack of methods to identify the correct molecular targets and engage with them. This technological gap has severely hampered the development of effective treatments.

The Cambridge team developed a machine learning method in which chemical libraries containing millions of compounds are screened to identify small molecules that bind to the amyloid aggregates and block their proliferation.

A small number of top-ranking compounds were then tested experimentally to select the most potent inhibitors of aggregation. The information gained from these experimental assays was fed back into the machine learning model in an iterative manner, so that after a few iterations, highly potent compounds were identified.

“Instead of screening experimentally, we screen computationally,” said Vendruscolo, who is co-Director of the Centre for Misfolding Diseases. “By using the knowledge we gained from the initial screening with our machine learning model, we were able to train the model to identify the specific regions on these small molecules responsible for binding, then we can re-screen and find more potent molecules.”

Using this method, the Cambridge team developed compounds to target pockets on the surfaces of the aggregates, which are responsible for the exponential proliferation of the aggregates themselves. These compounds are hundreds of times more potent, and far cheaper to develop, than previously reported ones.

“Machine learning is having a real impact on drug discovery – it’s speeding up the whole process of identifying the most promising candidates,” said Vendruscolo. “For us, this means we can start work on multiple drug discovery programmes – instead of just one. So much is possible due to the massive reduction in both time and cost – it’s an exciting time.”

The research was conducted in the Chemistry of Health Laboratory in Cambridge, which was established with the support of the UK Research Partnership Investment Fund (UKRPIF) to promote the translation of academic research into clinical programmes.

 

Reference:
Robert I. Horne et al. ‘Discovery of Potent Inhibitors of α-Synuclein Aggregation Using Structure-Based Iterative Learning.’ Nature Chemical Biology (2024). DOI: 10.1038/s41589-024-01580-x

Researchers have used artificial intelligence techniques to massively accelerate the search for Parkinson’s disease treatments.

Machine learning is having a real impact on drug discovery – it’s speeding up the whole process of identifying the most promising candidatesMichele Vendruscolo Nathan PittMichele Vendruscolo

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

The researchers, from the University of Cambridge, designed and used an AI-based strategy to identify compounds that block the clumping, or aggregation, of alpha-synuclein, the protein that characterises Parkinson’s.

The team used machine learning techniques to quickly screen a chemical library containing millions of entries, and identified five highly potent compounds for further investigation.

Parkinson’s affects more than six million people worldwide, with that number projected to triple by 2040. No disease-modifying treatments for the condition are currently available. The process of screening large chemical libraries for drug candidates – which needs to happen well before potential treatments can be tested on patients – is enormously time-consuming and expensive, and often unsuccessful.

Using machine learning, the researchers were able to speed up the initial screening process ten-fold, and reduce the cost by a thousand-fold, which could mean that potential treatments for Parkinson’s reach patients much faster. The results are reported in the journal Nature Chemical Biology.

Parkinson’s is the fastest-growing neurological condition worldwide. In the UK, one in 37 people alive today will be diagnosed with Parkinson’s in their lifetime. In addition to motor symptoms, Parkinson’s can also affect the gastrointestinal system, nervous system, sleeping patterns, mood and cognition, and can contribute to a reduced quality of life and significant disability.

Proteins are responsible for important cell processes, but when people have Parkinson’s, these proteins go rogue and cause the death of nerve cells. When proteins misfold, they can form abnormal clusters called Lewy bodies, which build up within brain cells stopping them from functioning properly.

“One route to search for potential treatments for Parkinson’s requires the identification of small molecules that can inhibit the aggregation of alpha-synuclein, which is a protein closely associated with the disease,” said Professor Michele Vendruscolo from the Yusuf Hamied Department of Chemistry, who led the research. “But this is an extremely time-consuming process – just identifying a lead candidate for further testing can take months or even years.”

While there are currently clinical trials for Parkinson’s currently underway, no disease-modifying drug has been approved, reflecting the inability to directly target the molecular species that cause the disease.

This has been a major obstacle in Parkinson’s research, because of the lack of methods to identify the correct molecular targets and engage with them. This technological gap has severely hampered the development of effective treatments.

The Cambridge team developed a machine learning method in which chemical libraries containing millions of compounds are screened to identify small molecules that bind to the amyloid aggregates and block their proliferation.

A small number of top-ranking compounds were then tested experimentally to select the most potent inhibitors of aggregation. The information gained from these experimental assays was fed back into the machine learning model in an iterative manner, so that after a few iterations, highly potent compounds were identified.

“Instead of screening experimentally, we screen computationally,” said Vendruscolo, who is co-Director of the Centre for Misfolding Diseases. “By using the knowledge we gained from the initial screening with our machine learning model, we were able to train the model to identify the specific regions on these small molecules responsible for binding, then we can re-screen and find more potent molecules.”

Using this method, the Cambridge team developed compounds to target pockets on the surfaces of the aggregates, which are responsible for the exponential proliferation of the aggregates themselves. These compounds are hundreds of times more potent, and far cheaper to develop, than previously reported ones.

“Machine learning is having a real impact on drug discovery – it’s speeding up the whole process of identifying the most promising candidates,” said Vendruscolo. “For us, this means we can start work on multiple drug discovery programmes – instead of just one. So much is possible due to the massive reduction in both time and cost – it’s an exciting time.”

The research was conducted in the Chemistry of Health Laboratory in Cambridge, which was established with the support of the UK Research Partnership Investment Fund (UKRPIF) to promote the translation of academic research into clinical programmes.

 

Reference:
Robert I. Horne et al. ‘Discovery of Potent Inhibitors of α-Synuclein Aggregation Using Structure-Based Iterative Learning.’ Nature Chemical Biology (2024). DOI: 10.1038/s41589-024-01580-x

Researchers have used artificial intelligence techniques to massively accelerate the search for Parkinson’s disease treatments.

Machine learning is having a real impact on drug discovery – it’s speeding up the whole process of identifying the most promising candidatesMichele Vendruscolo Nathan PittMichele Vendruscolo

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

Climate has long been held responsible for the emergence and extinction of hominin species. In most vertebrates, however, interspecies competition is known to play an important role.

Now, research shows for the first time that competition was fundamental to “speciation” – the rate at which new species emerge – across five million years of hominin evolution.

The study, published today in Nature Ecology & Evolution, also suggests that the species formation pattern of our own lineage was closer to island-dwelling beetles than other mammals.  

“We have been ignoring the way competition between species has shaped our own evolutionary tree,” said lead author Dr Laura van Holstein, a University of Cambridge biological anthropologist at Clare College. “The effect of climate on hominin species is only part of the story.” 

In other vertebrates, species form to fill ecological “niches” says van Holstein. Take Darwin’s finches: some evolved large beaks for nut-cracking, while others evolved small beaks for feeding on certain insects. When each resource niche gets filled, competition kicks in, so no new finches emerge and extinctions take over.

Van Holstein used Bayesian modelling and phylogenetic analyses to show that, like other vertebrates, most hominin species formed when competition for resources or space were low.

“The pattern we see across many early hominins is similar to all other mammals. Speciation rates increase and then flatline, at which point extinction rates start to increase. This suggests that interspecies competition was a major evolutionary factor.”

However, when van Holstein analysed our own group, Homo, the findings were “bizarre”.

For the Homo lineage that led to modern humans, evolutionary patterns suggest that competition between species actually resulted in the appearance of even more new species – a complete reversal of the trend seen in almost all other vertebrates.

“The more species of Homo there were, the higher the rate of speciation. So when those niches got filled, something drove even more species to emerge. This is almost unparalleled in evolutionary science.”

The closest comparison she could find was in beetle species that live on islands, where contained ecosystems can produce unusual evolutionary trends.

“The patterns of evolution we see across species of Homo that led directly to modern humans is closer to those of island-dwelling beetles than other primates, or even any other mammal.”

Recent decades have seen the discovery of several new hominin species, from Australopithecus sediba to Homo floresiensis. Van Holstein created a new database of “occurrences” in the hominin fossil record: each time an example of a species was found and dated, around 385 in total.

Fossils can be an unreliable measure of species’ lifetimes. “The earliest fossil we find will not be the earliest members of a species,” said van Holstein.

“How well an organism fossilises depends on geology, and on climatic conditions: whether it is hot or dry or damp. With research efforts concentrated in certain parts of the world, and we might well have missed younger or older fossils of a species as a result.”

Van Holstein used data modelling to address this problem, and factor in likely numbers of each species at the beginning and end of their existence, as well as environmental factors on fossilisation, to generate new start and end dates for most known hominin species (17 in total).

She found that some species thought to have evolved through “anagenesis” – when one slowly turns into another, but lineage doesn’t split – may have actually “budded”: when a new species branches off from an existing one.*

This meant that several more hominin species than previously assumed were co-existing, and so possibly competing.

While early species of hominins, such as Paranthropus, probably evolved physiologically to expand their niche – adapting teeth to exploit new types of food, for example – the driver of the very different pattern in our own genus Homo may well have been technology.

“Adoption of stone tools or fire, or intensive hunting techniques, are extremely flexible behaviours. A species that can harness them can quickly carve out new niches, and doesn’t have to survive vast tracts of time while evolving new body plans,” said van Holstein

She argues that an ability to use technology to generalise, and rapidly go beyond ecological niches that force other species to compete for habitat and resources, may be behind the exponential increase in the number of Homo species detected by the latest study.

But it also led to Homo sapiens – the ultimate generalisers. And competition with an extremely flexible generalist in almost every ecological niche may be what contributed to the extinction of all other Homo species.

Added van Holstein: “These results show that, although it has been conventionally ignored, competition played an important role in human evolution overall. Perhaps most interestingly, in our own genus it played a role unlike that across any other vertebrate lineage known so far.”

Competition between species played a major role in the rise and fall of hominins, and produced a “bizarre” evolutionary pattern for the Homo lineage.

This is almost unparalleled in evolutionary scienceLaura van HolsteinThe Duckworth LaboratoryA cast of the skull of Homo Heidelbergensis, one of the hominin species analysed in the latest study.

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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

Climate has long been held responsible for the emergence and extinction of hominin species. In most vertebrates, however, interspecies competition is known to play an important role.

Now, research shows for the first time that competition was fundamental to “speciation” – the rate at which new species emerge – across five million years of hominin evolution.

The study, published today in Nature Ecology & Evolution, also suggests that the species formation pattern of our own lineage was closer to island-dwelling beetles than other mammals.  

“We have been ignoring the way competition between species has shaped our own evolutionary tree,” said lead author Dr Laura van Holstein, a University of Cambridge biological anthropologist at Clare College. “The effect of climate on hominin species is only part of the story.” 

In other vertebrates, species form to fill ecological “niches” says van Holstein. Take Darwin’s finches: some evolved large beaks for nut-cracking, while others evolved small beaks for feeding on certain insects. When each resource niche gets filled, competition kicks in, so no new finches emerge and extinctions take over.

Van Holstein used Bayesian modelling and phylogenetic analyses to show that, like other vertebrates, most hominin species formed when competition for resources or space were low.

“The pattern we see across many early hominins is similar to all other mammals. Speciation rates increase and then flatline, at which point extinction rates start to increase. This suggests that interspecies competition was a major evolutionary factor.”

However, when van Holstein analysed our own group, Homo, the findings were “bizarre”.

For the Homo lineage that led to modern humans, evolutionary patterns suggest that competition between species actually resulted in the appearance of even more new species – a complete reversal of the trend seen in almost all other vertebrates.

“The more species of Homo there were, the higher the rate of speciation. So when those niches got filled, something drove even more species to emerge. This is almost unparalleled in evolutionary science.”

The closest comparison she could find was in beetle species that live on islands, where contained ecosystems can produce unusual evolutionary trends.

“The patterns of evolution we see across species of Homo that led directly to modern humans is closer to those of island-dwelling beetles than other primates, or even any other mammal.”

Recent decades have seen the discovery of several new hominin species, from Australopithecus sediba to Homo floresiensis. Van Holstein created a new database of “occurrences” in the hominin fossil record: each time an example of a species was found and dated, around 385 in total.

Fossils can be an unreliable measure of species’ lifetimes. “The earliest fossil we find will not be the earliest members of a species,” said van Holstein.

“How well an organism fossilises depends on geology, and on climatic conditions: whether it is hot or dry or damp. With research efforts concentrated in certain parts of the world, and we might well have missed younger or older fossils of a species as a result.”

Van Holstein used data modelling to address this problem, and factor in likely numbers of each species at the beginning and end of their existence, as well as environmental factors on fossilisation, to generate new start and end dates for most known hominin species (17 in total).

She found that some species thought to have evolved through “anagenesis” – when one slowly turns into another, but lineage doesn’t split – may have actually “budded”: when a new species branches off from an existing one.*

This meant that several more hominin species than previously assumed were co-existing, and so possibly competing.

While early species of hominins, such as Paranthropus, probably evolved physiologically to expand their niche – adapting teeth to exploit new types of food, for example – the driver of the very different pattern in our own genus Homo may well have been technology.

“Adoption of stone tools or fire, or intensive hunting techniques, are extremely flexible behaviours. A species that can harness them can quickly carve out new niches, and doesn’t have to survive vast tracts of time while evolving new body plans,” said van Holstein

She argues that an ability to use technology to generalise, and rapidly go beyond ecological niches that force other species to compete for habitat and resources, may be behind the exponential increase in the number of Homo species detected by the latest study.

But it also led to Homo sapiens – the ultimate generalisers. And competition with an extremely flexible generalist in almost every ecological niche may be what contributed to the extinction of all other Homo species.

Added van Holstein: “These results show that, although it has been conventionally ignored, competition played an important role in human evolution overall. Perhaps most interestingly, in our own genus it played a role unlike that across any other vertebrate lineage known so far.”

Competition between species played a major role in the rise and fall of hominins, and produced a “bizarre” evolutionary pattern for the Homo lineage.

This is almost unparalleled in evolutionary scienceLaura van HolsteinThe Duckworth LaboratoryA cast of the skull of Homo Heidelbergensis, one of the hominin species analysed in the latest study.

The text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright ©University 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