Perelman School of Medicine researchers at the University of Pennsylvania are developing a new type of positron emission tomography (PET) scan for imaging the brains of patients with neurodegenerative disorders, including Parkinson’s disease.

Penn Medicine researchers will work with scientists from the Washington University-St Louis, the Universities of Pittsburgh and California-San Francisco and Yale University.

The alliance, Center Without Walls, secured a $20m National Institute of Neurological Disorders and Stroke (NINDS) grant to support the project over five years.

Center Without Walls principal investigator Robert Mach said: “At the end of five years, we hope to have a radioactive tracer that will be able to detect Parkinson’s early on and provide detailed information about the disease’s progression, which is critical for discovering and testing new treatments.”

Parkinson’s disease, which currently lacks a diagnostic test, could remain undetected or be misdiagnosed until its symptoms become severe.

Treatments could become less effective with disease progression and an imaging biomarker or indicator is expected to allow early diagnosis, accelerating clinical trials of drugs.

During a PET scan, a radioactive drug or tracer binds to proteins or sugars to highlight areas of the body with higher chemical activity levels, which indicate disease.

As part of the project, researchers are working to detect Parkinson’s and other neurodegenerative diseases driven by proteinopathies, which form when some proteins ‘misfold’ and become structurally abnormal.

The team plans to create a radiotracer that will bind to the alpha-synuclein protein in the brain for imaging of Parkinson’s and multiple system atrophy.

Also, they will develop another radiotracer, which will bind to the 4R tau protein, to image frontotemporal degeneration and progressive supranuclear palsy.

To develop the radiotracers, the team will leverage a computational technology that screens for molecules, synthesises them and interprets binding data depending on crosslinking.

A research team led by the University of Strathclyde in the UK has developed an artificial intelligence (AI)-based blood test for the diagnosis of brain cancer.

The test uses infrared light to generate a ‘bio-signature’ of a blood sample and then applies AI to identify any signs of cancer.

Strathclyde researchers developed the technology in collaboration with researchers at the University of Liverpool, the Walton NHS Foundation Trust in Liverpool and the Western General Hospital in Edinburgh.

The researchers had their work published in the journal Nature Communications.

According to the publication, the blood test demonstrated favourable sensitivity and specificity in distinguishing cancer and control patients in a clinical validation study.

The university spin-out company ClinSpec Diagnostics is commercialising the blood test to allow quick diagnosis and treatment.

University of Strathclyde pure and applied chemistry department reader Matthew Baker said: “This is the first publication of data from our clinical feasibility study and it is the first demonstration that our blood test works in the clinic.

“Earlier detection of brain tumours in the diagnostic pathway brings the potential to significantly improve patient quality of life and survival, whilst also providing savings to the health services.”

Brain cancer patients often have non-specific symptoms and the final diagnosis can be time-consuming.

When the team examined samples from a 104-patient prospective cohort, the blood test was able to correctly differentiate brain cancer patients from healthy people in 87% of the cases.

Based on the findings, researchers concluded that the test could help doctors prioritise individuals who require brain scans for tumour diagnosis.

The test does not provide the final diagnosis but can aid the diagnostic process as a triage tool.

A survey of 9,000 customers conducted by MuleSoft has found that 75% of patients feel digital experiences have improved the standard of care they receive from clinicians.

The Customer Experience and Connectivity Chasm (CECC) report highlights how digital experiences and personal health data from wearable tech and health apps are impacting healthcare providers’ standards. A grand total of 9,030 adults were surveyed from across Europe, Asia and the US during August 2019.

Of the 75% of customers who said digital experiences – such as online booking or remote video consultations – had improved their standard of care, 57% felt it had improved their experience with their GP. Standard of care in hospitals was considered to have been improved by digital experiences in 55% of cases, while for pharmacies this statistic rose to 60%.

Consumers were also positive about how healthcare providers were making use of their personal data, with 61% saying providers made effective use of that data to deliver a better standard of care. Consumers aged 18-34 were the most positive about this data usage, with 70% of responders in this age group sharing this belief. Consumers in the US (71%), France (63%) and Singapore (79%) were the most positive about healthcare providers making effective use of the data available to them, while consumers in Germany (49%) are the least positive.

However, the healthcare sector continues to face challenges in bringing together digital and traditional platforms to provide a connected healthcare experience.

In total, 55% of consumers said they received a disconnected experience from healthcare providers, and 53% said this could make then consider changing to another service.

MuleSoft vice president of product marketing David Chao said: “With the growing popularity of wearable technology and health apps, consumers’ expectations of healthcare are rising. It’s encouraging to see that the healthcare sector is making strides when it comes to its effective use of digital technology and personal health data.

“The challenge now facing both private and public healthcare providers is being able to deliver a more connected healthcare experience. By creating an application network using APIs, healthcare providers can better integrate clinical and non-clinical applications to provide improved patient experiences, all while making secure data sharing more efficient. By taking this approach, healthcare providers will be able to continue to improve the standard of care they offer.”

A digital kidney is teaching researchers at the University of Waterloo about the impact of medicines on the dehydrated body.

The model has found that unless a patient is properly hydrated, taking two blood pressure drugs and an aspirin could cause acute kidney injury.

University of Waterloo professor of applied mathematics, pharmacy and biology Anita Layton said: “People who have high blood pressure are typically given a water pill, so they pee a lot to lower their blood volume and in so doing lower their blood pressure. These patients are frequently also given another drug that targets a hormonal system which will affect the kidney as well.

“A lot of people are on these two drugs, and they will be fine. But one day they might have a headache and take an aspirin, and the three of these drugs together can hurt your kidneys.”

Elderly people, those with impaired kidney function and patients taking certain drugs or drug combinations need to be mindful of their water intake, or they can face unpleasant side effects. This is because their kidneys can struggle to maintain water balance. Vital functions, like producing highly concentrated urine to get rid of waste using as little water as possible when dehydrated, can be impaired.

Layton has built the first computational model of a kidney which simulates the muscle contractions which move urine from the kidney to the bladder. The computer model simulated the effects of various drugs on the urinary system, and applied mathematical techniques to analyse the data.

The model found that in dehydrated patients, combining two blood pressure drugs and an aspirin can cause injury when there is an insufficient water balance in the body. This leads to a build-up of waste product in concentrated urine.

Layton said: “Incredibly, how mammals produce a highly concentrated urine is not well understood. We’re now a step closer to understanding how water balance is maintained in mammals.”

Researchers at Brown University in the US have partnered with Rhode Island Hospital, Intel and Micro-Leads Medical to develop an ‘intelligent spinal interface’ to restore muscle control and sensation in patients with spinal cord injuries.

The device is intended to bridge the gap that a spinal injury creates in neural circuitry, helping patients regain limb movement and bladder control.

Designed to record signals that travel down the spinal cord above an injury site, the device will simultaneously capture signals coming up the spinal cord.

The information from signals travelling down will be leveraged for electrical spinal stimulation below the lesion, while that from signals coming up will be used for stimulation above the injury.

The spinal interface will help to restore volitional control of limbs muscles along with feeling and sensation lost because of injury.

Brown University School of Engineering assistant professor David Borton said: “We know that circuits around a spinal lesion often remain active and functional. The hope is that by using information from either side of a lesion in a bidirectional way, we could make a significant impact on the treatment of spinal cord injuries.

“This exploratory study aims to build the toolset, the mix of hardware, software and functional understanding of the spinal cord, to make such a system possible.”

The team plans to enrol spinal cord injury patients over the coming two years for implanting an experimental interface for up to 29 days.

During the study period, the device will record and stimulate the spine as participants receive standard physical therapy for spinal injuries.

The focus will be on signals associated with control of the legs, for walking and standing, as well as bladder control. The two-year research aims to demonstrate the ability of the device to target the neural circuits influencing these activities.

Furthermore, the data from the project is expected to facilitate future therapeutic technologies.

Brown University researchers and Intel will work to create artificial intelligence (AI) and machine learning tools for decoding the recorded spinal signals.

Meanwhile, Micro-Leads will provide a spinal cord stimulation technology called HD64 for the project.

Baylor University researchers have developed and studied a prototype smartphone app, Cradle, for the detection of early signs of various eye disorders, including an aggressive eye cancer called retinoblastoma, in children.

Study findings indicate that the Cradle app can effectively help in clinical screenings for leukocoria, a primary symptom of retinoblastoma.

The app detects leukocoria by looking through family photographs for traces of any abnormal reflections from the retina. Also, it can help parents screen for other common eye diseases.

During the study, Baylor University research team evaluated the sensitivity, specificity and accuracy of the prototype app via examination of more than 50,000 photographs taken before diagnosis.

The app identified leukocoria in 80% of the children diagnosed with eye diseases. Cradle was able to detect the condition in photographs taken on an average of 1.3 years before the official diagnosis.

Conventional screening approaches detect leukocoria in only 8% of cases, said the researchers.

The sensitivity of the app was found to be more than 80% in children aged two years and below. The team added that the ability of Cradle’s algorithm to identify even slight instances of leukocoria has improved.

Baylor University chemistry and biochemistry associate professor Bryan Shaw said: “We suspected that the app would detect leukocoria associated with other more common disorders and some rare ones.

“We were right. So far parents and some doctors have used it to detect cataract, myelin retinal nerve fibre layer, refractive error, Coats’ disease, and of course retinoblastoma.”

The Science Advances journal has published the study findings.

Currently, researchers are using the app to analyse nearly 100,000 photos to identify additional features that could help reduce false positives.

Singapore-based health IT start-up Biofourmis has received 510(k) clearance from the US Food and Drug Administration (FDA) for the use of Biovitals Analytics Engine for ambulatory physiological monitoring.

The FDA approval for the Biovitals Analytics Engine, powered by machine learning and artificial intelligence (AI), is the second market authorisation secured by Biofourmis.

In May, the company won FDA approval for its Cloud-based Biovitals RhythmAnalytics platform that automates the interpretation of over 15 types of cardiac arrhythmias.

The Analytics Engine is capable of processing multiple physiology signals, as the technology forms the basis of the company’s digital therapeutics product pipeline.

According to Biofourmis, the Biovitals Analytics Engine receives data, including heart rate, respiratory rate and activity, in near real-time from sensors cleared by the FDA.

The device uses AI and machine learning to correlate between multiple vital signs and the patient’s daily activities and builds a biometric signature, which is updated based on incoming data.

A time-series Biovitals Index alerts providers in case of changes in patients’ measured vital signs from their baseline much before an event, providing time for the clinician to take steps to change the trajectory of the disease.

The company claims that the Biovitals platform can reduce hospital readmissions and decrease the need for emergency department (ED) visits.

Biofourmis applied for FDA clearance based on clinical studies involving monitoring patients with chronic conditions such as heart failure, COPD and atrial fibrillation, at home, in a real-world setting.

Research initiatives involving patients with heart failure are also using the Analytics Engine.

Biofourmis has entered into partnerships with the Yale University-Mayo Clinic Center of Excellence in Regulatory Science and Innovation (CERSI) and Brigham and Women’s Hospital to use BiovitalsHF and Biovitals Analytics Engine and Biovitals RhythmAnalytics to monitor functional capacity and quality of life of patients with heart failure and cardiac arrhythmias.

Biofourmis CEO and founder Kuldeep Singh Rajput said: “This milestone approval is foundational to the Biovitals ecosystem, which includes not only our most advanced solution, BiovitalsHF for heart failure, but also our range of solutions across therapeutic areas, such as pain, oncology, sleep disorders and others in development.”

Digital mental health company SilverCloud Health is partnering with Microsoft to enhance its product with artificial intelligence (AI).

The company has been working with Microsoft Labs for the last 18 months, exploring how machine learning and AI can be used to enhance the delivery of digital cognitive behavioural therapy (CBT) and improve patient outcomes on SliverCloud’s platform.

SilverCloud provides digital behavioural healthcare through more than 30 eight-week courses designed to help manage stress, anxiety and depression through CBT. The programmes allow patients semi-regular contact with a trained human clinician as they complete the courses to review their progress.

The digital format of the therapy helps remove many of the barriers to traditional mental health services faced by patients, such as cost, physical accessibility and stigma.

SilverCloud Health CEO Ken Cahill said: “Through this exciting research collaboration with Microsoft, SilverCloud Health will be able to leverage the latest in artificial intelligence and machine learning to further enhance our digital mental health platform.

“This truly is therapy for the 21st century; enabling more personalised treatment, earlier and easier access, and most importantly delivering ever increasing clinical outcomes.”

Microsoft Research Cambridge clinicians believe that by analysing different types of engagement behaviours through a machine learning framework, they will be able to better identify which CBT strategies are most effective for individual patients. This would enable them to receive personalised treatment that better fits their needs.

SilverCloud is the most widely-used digital CBT platform in the UK, employed by over 70% of NHS Improving Access to Psychological Therapies (IAPT) services. One in four UK adults are affected by a mental health condition each year, care for which is often difficult to access due to constrained services.

Microsoft Research Cambridge lab director Christopher Bishop said: “SilverCloud Health is one of the very few digital mental health platforms that has been deployed at scale in routine clinical care, and currently has the largest real-world patient user base of its kind.

“The aim of this project is very much aligned to our ambition to empower healthcare workers and patients through access to effective, intelligent technologies.”