Health Tech World explores the latest research developments in the world of health technology.
A molecular ‘reset button’ for reading the brain through a blood test
Tracking how genes switch on and off in the brain is essential for understanding many neurological diseases, yet the tools to monitor this activity are often invasive or unable to capture subtler changes over time.
One emerging alternative is to use engineered serum markers ⎯ small proteins produced by targeted brain cells that can travel into the bloodstream, where they can be measured with a simple blood test.
Referred to as released markers of activity, or RMAs, these molecules can serve as a sensitive tool for monitoring brain activity, but they also persist in the bloodstream for many hours. That long half-life can drown out intervening changes in the biological signal of interest.
Rice University bioengineers have developed a way to make RMAs even more sensitive, pointing to other diagnostic scenarios where they could be deployed.
For the study, the team designed an erasable marker that can be cut apart inside the bloodstream by an enzyme which acts like a pair of molecular scissors: Once it cleaves the RMAs, the previous signal disappears and a new reading becomes possible.
In an animal model, a single injection of the cleaving enzyme removed about 90 per cent of the RMAs’ background signal within half an hour. That reset made it possible to observe subtle gene-expression changes that had previously remained undetected.
The researchers also showed they could repeat this process and measure how quickly the marker reappeared, offering a clearer picture of how gene activity evolves over time.
This approach could eventually enable clinicians to detect problems or measure any changes in how a patient responds to treatment with greater precision, using simple, minimally-invasive testing.
It could have an impact on areas of medicine beyond neurology: If markers can be edited inside the body, their behavior can be tuned for many diagnostic purposes, including, for example, using RMAs for detecting tumors or lung disease using urine tests.
Automation helps relieve symptoms to keep cancer patients out of the ER
For many people living with cancer, symptoms such as pain, anxiety or insomnia can quickly spiral into an emergency room visit. Such visits can be financially costly and take an emotional toll on patients and their caregivers.
A new study led by Mayo Clinic researchers found that using digital check-ins and a remote care team can help patients manage symptoms before they reach a crisis point.
“Our goal was simple but ambitious,” says study lead Andrea Cheville, M.D., professor of Physical Medicine and Rehabilitation in the Mayo Clinic Comprehensive Cancer Center.
“We wanted to see if automating symptom check-ins and care through the electronic health record could improve patients’ lives without adding to the burden on oncology teams. What we found is that this approach not only eased symptoms like anxiety and depression but also kept thousands of patients out of the hospital.
“That tells us technology can help us successfully extend the reach and efficacy of care.”
For Becky Johnson, participating in the Enhanced EHR-Facilitated Cancer Symptom Control Trial (E2C2) meant better sleep — despite the anxiety she felt about her double breast cancer diagnosis in 2022 at the age of 40.
As part of the trial, Johnson regularly submitted digital surveys about various aspects of her health. Insomnia quickly rose to the top, prompting a phone call from a nurse, who counseled Johnson on ways to get a better night’s rest.
The nurse also gave Johnson a link to a self-paced online class with sleep strategies based in cognitive behavioural therapy.
To make such interventions possible, the researchers developed automations in the Plummer Chart, the software system that manages patients’ electronic health records (EHRs) and helps Mayo teams coordinate care.
Between 2019 and 2023, just over 50,200 patients across 15 cancer specialties at Mayo Clinic enrolled in the E2C2 trial. Like Johnson, they filled out short surveys about pain, fatigue, sleep, anxiety and other symptoms before clinic visits or monthly between visits.
The system automatically sorted their responses. Mild scores were simply logged, moderate ones triggered the system to send the patient self-care tips, and severe scores prompted outreach by a remote symptom care manager — a nurse or social worker who could work with patients via phone or video.
Behind the scenes, the software became both an automated traffic controller and safety net. For care teams, it routed information to patients automatically so they could focus on the work that required their expertise. For patients, it made getting support easier, with no additional appointments or travel.
The trial results showed that the automated surveys and responses built into the EHR made care more efficient and, ultimately, improved patients’ symptoms.
Patients reported less anxiety and depression, with modest improvements in other symptoms.
Patients also had 40 per cent to 60 per cent fewer acute care encounters, including emergency visits, hospitalisations, and ICU admissions.
All of this was achieved with just two to three full-time care managers plus 20 per cent of one physician’s time supporting more than 50,000 study participants.
The E2C2 trial showcases a new approach to using digital tools in cancer care. By automating routine monitoring and triaging of patient symptoms and wellbeing through the patient’s electronic health record, a small care team can support a large patient population.
New technique maps genetic variants driving neurodegenerative disease risk
A team led by researchers from Penn State College of Medicine has developed a new method that substantially improves the ability to map the genetic variants that drive disease, particularly neurodegenerative diseases.
Instead of analysing genetic effects by grouping cells into specific categories and determining genetic effects for each type individually, the team modeled the effects shared among seven different brain cell types.
The new approach, published in Nature Communications, outperforms existing methodologies, identifying approximately 75 per cent more genes of interest.
The researchers also found new genes linked to the risk of Alzheimer’s disease and amyotrophic lateral sclerosis (ALS) and therapeutic targets, some of which have already-existing promising treatments.
The research team’s work focuses on understanding how genes influence disease risk. They explained that, for example, genes like APOE can increase the risk of developing Alzheimer’s disease between three-fold and nine-fold.
Neuroinflammation may also play a role in the development of neurodegenerative diseases, something that Jiang explained specialized cells in the brain like microglia, the brain’s immune cells, may contribute to.
Scientists often use genome-wide association studies to identify regions of the genome associated with particular diseases. However, genome-wide association studies often rely on bulk tissue samples where different cell types are mixed together.
More recent studies might instead utilize single-cell data, which allows scientists to investigate cell types individually, but sample sizes are typically small, especially for rare cell types like microglia.
Here, the team developed a new method, dubbed BASIC for Bulk And Single cell eQTL Integration across Cell states, which integrates both bulk tissue samples and single-cell data.
The researchers looked across cell types to identify combinations of genes that, when expressed, produced similar effects across multiple cell types as well as effects that were unique to certain cell types.
Compared to conventional methods, the researchers identified roughly 75 percent more genes that may play a role in disease risk using BASIC.
The improvement is equivalent to bolstering sample size by nearly 77 per cent. When they applied this method to analyze 12 different brain-related diseases, like Alzheimer’s disease, ALS and addiction, the team was able to more accurately identify genetic targets linked to disease by over 53 per cent compared to single-cell data alone and by 111 per cent over bulk tissue analysis.
They also identified new genes linked to neurodegenerative disease, including ALS and Alzheimer’s disease, that have been overlooked by conventional approaches.
The researchers then used this information to identify drug compounds that could reverse gene expression associated with disease, such as alfacalcidol, a synthetic version of vitamin D, for schizophrenia and cabergoline for Alzheimer’s disease.
These are existing medications that have already been approved by the Food and Drug Administration as safe and effective for treating other diseases and could potentially be repurposed.
More research is needed to fully understand the implications of their findings.
Engineered virus boosts immune response against glioblastoma in preclinical models
Researchers at Mass General Brigham have modified a herpes simplex virus (HSV-1) that stimulates the immune system to attack glioblastoma cells.
A single dose of the modified virus increased T-cell, natural killer cell, and myeloid cell responses in the tumour microenvironment and increased the overall survival in preclinical models.
Glioblastoma is among the most aggressive and treatment-resistant brain tumours. Previous attempts to stimulate the immune system to attack tumor cells in the brain have shown limited benefit, in part because glioblastoma cells release multiple molecules that dampen immune responses.
To overcome the barriers, researchers modified an HSV-1 virus to recognize markers found only on glioblastoma cells.
They engineered the virus to express five different immunomodulatory molecules to help reprogram the tumour environment, including IL-12, anti-PD1, a bispecific T cell engager, 15-hydroxyprostaglandin dehydrogenase and anti-TREM2.
Researchers also added safety mutations, or “off-switches,” that prevent the virus from spreading to neurons or healthy central nervous system cells. So that the reach of the virus could be visualized on PET scan, the team inserted a gene that expresses a protein capable of trapping a PET-tracer molecule.
Mice treated with the virus showed increased infiltration of tumor-fighting T cells, as well as reduced T-cell exhaustion markers. Mice injected with the virus also lived longer than glioblastoma-harboring mice not injected with the virus.
Future research will focus on evaluating the safety and efficacy of the oncolytic virus in human trials as well as adapting the viral platform to remodel the tumor microenvironment in other cancers.
