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Comparing Stakeholders’ Perspectives on Parkinson Disease Management and Digital Technologies: Exploratory International Survey

Background: Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers, and health care professionals. With growing interest in digital and predictive artificial intelligence (AI) tools for disease management, understanding the needs and digital readiness of these stakeholder groups is crucial. Objective: This work aims to (1) identify digital practices for PD management among persons with PD, at-risk individuals, caregivers, and health care professionals; (2) compare these practices across groups; (3) explore stakeholder desires for AI-based tools; and (4) assess alignments and gaps to inform tailored AI solutions. Methods: An anonymous cross-sectional online survey of an exploratory nature was distributed (from December 2024 to October 2025) in 5 languages and completed by 255 respondents. Descriptive statistics summarized responses to 41 questions, including stakeholder-specific items. tests were performed to examine stakeholder differences in desired AI features. Results: Interest in predictive AI was high across stakeholder groups. Symptom tracking was the most desired feature (selected by more than 76% of the respondents), and personalized treatment recommendations came second for both persons with PD and health care professionals; however, stakeholder priorities diverged in other areas. Health care professionals rated improving patient and informal caregiver engagement as significantly more important than persons with PD did, (n=205)=34.78, <.001, and Cramer V=0.41. Despite considerable interest, the reported use of digital tools was limited, as most persons with PD did not use symptom-tracking apps or wearables, nor were they currently monitoring their condition, although many expressed intentions to begin. Conclusions: While predictive AI tools were viewed positively across groups, there were significant gaps in stakeholder preferences, highlighting the importance of tailored, context-aware design. Early diagnosis was not prioritized by persons with PD or health care professionals, likely reflecting the complexity of diagnosing PD in the absence of disease-modifying therapies. Coupled with the emphasis placed on preventive lifestyle guidance by persons with PD and those at risk, this highlights the importance of actionability in AI-based monitoring and prediction. Such actionability may also enhance perceived relevance and uptake, given that reported interest in digital health tools and self-tracking exceeded actual use. These findings offer early-stage insight to guide the development of future AI-based solutions for PD.
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HELIX AI Model Accurately Predicts RNA Splicing, Unlocks Precision Medicine

RNA splicing, in which different coding RNA, or exons, are joined together after noncoding regions, or introns, are removed, allows for a large array of RNA transcript isoforms with distinct sequences, and functions in tissue- and cell-type-specific patterns. Conversely, transcript isoform alterations can sensitively reflect dynamic changes in cellular states. Aberrant splicing is closely associated with major diseases, such as cancer.  

In a new study published in Nature Computational Science titled, “HELIX: a scalable model for predicting context-dependent regulation of RNA splicing and isoform usage,” researchers from the Chinese Academy of Sciences have developed an AI-driven framework that enables highly accurate prediction of RNA splicing and isoform usage by integrating genomic sequence features with tissue-specific RNA binding protein (RBP) expression profiles. The work offers valuable insights for splicing regulatory patterns, pathogenic variant interpretation, and precision medicine research. 

Isoform usage is jointly regulated by multiple layers of control, including regulatory elements, such as splicing enhancers and silencers on exons and introns, and tissue microenvironments. Scientists have been challenged to accurately characterize and predict RNA splicing and isoform usage across tissues, cell types, and disease states. 

The study’s AI framework, Hierarchical Explainable LSTM for Isoform eXpression (HELIX), overcomes the limitations of conventional approaches via a two-layer deep-learning architecture. 

First, the framework integrates DNA sequence information with the expression profiles of 1,499 RBPs. Long short-term memory (LSTM) networks are then employed to effectively capture the complex dependencies and competitive relationships among multiple splice sites. 

This design enables precise, reliable prediction of RNA splicing and transcript isoform usage. The model was trained and optimized on large-scale short- and long-read RNA-seq datasets covering 30 distinct human tissues, allowing accurate quantification of complex transcript structures and isoform usage. Results show that HELIX substantially outperforms existing mainstream methods in both splicing strength prediction and overall isoform usage prediction. 

In disease-related studies, HELIX deciphered aberrant RNA splicing and transcript isoform alterations. Notably, the researchers identified widespread splicing dysregulation and abnormal isoform usage in tumor cells using large colorectal cancer cohorts. 

The results reveal strong correlations among such alterations and genomic mutations, RBP dysregulation, and patient clinical profiles. Results support that splicing abnormalities can serve as key molecular signatures for tumor progression and guiding patient stratification. 

The team also developed scHELIX, a single-cell RNA sequencing extension of HELIX. scHELIX supports high-resolution profiling of transcript isoform usage across different cell types and tumor subpopulations, which offer a refined view of intratumoral heterogeneity. 

The findings reveal distinct RNA splicing and isoform usage patterns among tumor subclones, providing new clues for tumor evolution research and potential therapeutic target discovery. 

The post HELIX AI Model Accurately Predicts RNA Splicing, Unlocks Precision Medicine appeared first on GEN – Genetic Engineering and Biotechnology News.

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Transmission Dominance Under Random-Contact Intensification in Epidemic Networks: Multilayer Contact Network Simulation Study

Background: In the context of COVID-19, infection spread through human contact networks remains a major public health challenge. Beyond cumulative infections and deaths, it is necessary to understand which contacts matter most, and which population segments contribute most to transmission under different social conditions. In multilayer urban networks with community structure, routine contacts coexist with incidental encounters, and it remains unclear whether incidental encounters can alter epidemic burden and the main contributors to transmission when per-layer contact caps and routine-contact minima are unchanged (for the nonrandom layers). Objective: Under explicit daily-contact constraints, we examined (1) how changing overall contact opportunities affects epidemic speed and burden when incidental encounters are held fixed, and (2) whether increasing incidental encounters alone, per-layer contact caps, and routine-contact minima fixed (for the nonrandom layers), shifts the main contributors to transmission from a high-contact group to a medium-contact group, and the underlying network mechanism. Methods: We constructed a multilayer potential contact network for a synthetic urban population of 10,038 individuals, representing household, school, workplace, distance-driven activities, and incidental encounters as separate layers. Daily contact networks were sampled from the potential network each day, and transmission was simulated for 120 days using a Susceptible-Exposed-Infectious-Removed model with vaccination. Individuals were classified into high-contact and medium-contact groups based on baseline contact intensity, and group contribution combined each group’s share of infectious individuals and its per-infectious effective transmission yield. Contact-constraint parameters were calibrated using an online survey in Tokyo and Kanagawa (n=1089), and scenario comparisons and parameter sweeps were used to locate the transition point. Results: With incidental encounters held fixed, higher overall contact opportunities produced earlier and higher epidemic peaks and larger cumulative infections and deaths, whereas reduced opportunities slowed and prolonged spread. Holding overall contact opportunities and routine contacts fixed, increasing incidental encounters shifted the main contributors to transmission: higher-contact individuals accounted for more effective transmissions at low incidental contact, whereas medium-contact individuals accounted for more beyond a clear transition point. Network visualization and schematics suggest a bridge-allocation mechanism, where stronger incidental contact adds cross-community bridges that more often terminate at medium-contact individuals and carry infection into less-affected communities. Across R=30 replicate runs under fixed settings, the dominance flip was consistently observed, and the estimated threshold W∗ showed a narrow but nonzero distribution (reported as median and IQR). Conclusions: In multilayer urban contact networks with community structure, our results indicate that intensifying incidental encounters can change the main contributors to transmission even when overall contact opportunities and routine contacts are unchanged. We present an analysis framework under explicit daily-contact constraints to identify this contributor shift and its transition point, supporting comparisons of intervention priorities across social contact conditions.
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STAT+: Pioneering trial for treating genetic disease before birth nears reality

SAN FRANCISCO — While Tippi MacKenzie was a postdoctoral fellow in the early 2000s, she and her lab mates experimented with using the then-new technology of gene replacement therapy to try to treat inherited disorders in mice before they were born. Over and over it worked. They cured mice with hemophilia and mice with tyrosinemia. And the whole time, people kept telling her that gene therapy in human fetuses was just around the corner, just five years away. 

It’s now been 25 years, and such a reality has yet to materialize. But after promising discussions with the Food and Drug Administration, MacKenzie is now closer than anyone’s ever been.

Her team has submitted an investigational new drug (IND) application to the agency seeking approval for a small trial that aims to treat five fetal patients with a rare lysosomal storage disorder. The agency told them they could bypass animal testing, because the safety profile of the vector they plan to use already has been so well characterized by other academics and companies developing gene therapies for kids and adults.

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AI Shows Value for Breast Cancer Prediction

AI is increasingly being used to help determine breast cancer risk, with two studies showing its value for primary prevention and in women of diverse ancestry.

The first study, in Science Translational Medicine, revealed that AI could outperform standard clinical risk tools at identifying women of European ancestry who were at greatest risk of developing breast cancer within the next decade.

Because current AI-based models mostly predict breast cancer risk in the short term, Mikael Eriksson, PhD, from the Karolinska Institute, and fellow researchers created a model for invasive and in situ breast cancer that covered a 10-year period.

“Considering that a tumor can take five to 20 years to develop into a screen- or clinically detected cancer, a 10-year or lifetime risk projection time is reasonable,” they noted.

The researchers validated their image-derived AI-based 10-year risk model using digital mammograms from 8676 women in two cohorts from the U.S. and Sweden, of whom 1633 had breast cancer.

They compared their model with three other risk assessment tools: the Tyrer-Cuzick-v8, which uses personal and family history to determine the lifetime risk of breast cancer; the Breast Cancer Surveillance Consortium v3 tool, which estimates the risk of developing invasive risk cancer over five years; and the Mirai AI algorithm for predicting breast cancer risk.

The model calculated that the 10-year risk of breast cancer was 3.83% for the North-American group and 3.14% for the Scandinavian group, and it showed promise for predicting the risk of invasive tumors.

The AI model performed significantly better at predicting invasive breast cancer than the three comparator models both overall, in women aged 50 to 69 years and those with estrogen-receptor-positive breast cancers.

“An image-derived AI-based risk model developed for 10-year risk assessment for identifying individuals in mammographic screening who may benefit from primary prevention strategies identifies up to 40% of breast cancers to be at high risk at study entry in U.S. and Swedish validation case cohorts per clinical guidelines,” summarized Eriksson and co-workers.

The second study, in Science Advances, revealed how AI could be of value for women from more diverse backgrounds, and that it had value regardless of race and ethnicity.

The research was driven by the observation that conventional prediction models incorporating genetic and clinical factors including breast density underperform in women who do not have European ancestry.

Shu Jiang, PhD, from Washington University School of Medicine in St Louis, and co-workers set out to evaluate the generalizability of an AI-derived mammogram risk score (MRS), a summary of texture features which captures intrinsic breast-tissue characteristics that are the basis for cancer to develop.

To do this, they used data from two large North American cohorts that represented more than 226,000 racially diverse women undergoing routine breast screening with mammography.

This was then used to examine the generalizability of MRS with breast cancer risk across non-Hispanic white (NHW), non-Hispanic Black (NHB), East Asian, South Asian, and Indigenous women.

Jiang and team found that the MRS was a strong predictor of breast cancer risk independent of race or ethnicity, demonstrating its potential for broader clinical utility.

Across the two independent screening cohorts, the MRS increased with age in line with rising breast cancer risk. It was a strong and consistent predictor of breast cancer risk across race and ethnic groups as shown by the association study and comparison of distributions and demonstrated excellent calibration in all groups.

Its remained robust across full-field digital mammograms or synthetic two-dimensional digital breast tomosynthesis, although the limited numbers of images precluded an analysis of how performance varied across manufacturers.

“Overall, these results support that MRS is a powerful breast cancer risk predictor that does not depend on race or ethnicity, supporting its potential for broader clinical adoption and use in varied populations of women undergoing routine screening mammography to identify those at increased risk,” Jiang and coworkers concluded.

The post AI Shows Value for Breast Cancer Prediction appeared first on Inside Precision Medicine.

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Molecular Imaging Could Detect Early Kidney Fibrosis

Scientists have developed a new biomarker test that can noninvasively detect kidney fibrosis from a urine sample. A study published today in Science Translational Medicine reports the test could accurately differentiate between mild and severe disease, something that is not yet possible with the diagnostic methods available today. 

“Noninvasive and accurate diagnosis of kidney fibrosis remains unavailable in clinics, limiting effective patient management,” write the authors of the study, led by Zhou Jiaguo, PhD, professor of pharmacology at Sun Yat-sen University in China. “Although biopsy is considered the gold standard for assessing fibrotic progression, this invasive approach is subject to sampling error and interobserver variability, which may bias the diagnosis and is improper for repeated monitoring.”

Chronic kidney disease affects approximately 12% of the global population, with rates steadily rising as the world’s population ages. Characterized by the excessive formation of scar tissue, kidney fibrosis is a hallmark of chronic kidney disease that causes the proggressive impairment of kidney function, eventually leading to end-stage renal disease that requires dialysis or transplantation. 

Diagnosing kidney fibrosis at early stages can be critical for preventing irreversible damage to the kidneys. However, current diagnostic methods present some major limitations. While biopsies are invasive and difficult to accurately interpret, blood biomarker tests cannot distinguish kidney fibrosis from other kidney conditions or from fibrosis in other organs.

To circumvent these limitations, Jiaguo’s team identified two biomarkers that are specific to kidney fibrosis and can accurately differentiate this diagnosis from other related conditions. These are the transglutaminase 2 (TG2) enzyme and its substrate, lysyl oxidase–derived allysine (LysAld), which are both upregulated during the formation of fibrotic kidney tissue. 

The researchers developed a fluorescent reporter that binds to LysAld in the kidney and is then cleaved by TG2, turning on its fluorescence. Higher biomarker levels result in a stronger signal, providing an indication of how advanced fibrosis is in the patient’s kidneys. 

In a small cohort of 35 participants, the molecular imaging test could distinguish patients with kidney fibrosis from healthy controls with 84% sensitivity and 94% specificity. In addition, the test could differentiate mild and severe cases of fibrosis, with results confirmed with histological imaging. In contrast, traditional clinical measurements such as glomerular filtration rate, serum creatinine, and blood urea nitrogen were not able to classify patients according to the severity of the condition. 

Large-scale studies will be needed to establish this novel approach as a diagnostic and prognostic. Going forward, Jiaguo and colleagues are also interested in exploring using this test to monitor disease progression over time as a potential tool to evaluate treatment response and help clinicians make critical decisions to improve patient outcomes. 

“This noninvasive activatable reporter holds translational potential for early identification of renal fibrosis and patient stratification of [chronic kidney disease] to improve clinical management and patient outcome,” write the researchers. “We do not envision the probe as a standalone replacement for current standards; synergistic combinations with other biomarkers could further enhance its clinical utility.”

The post Molecular Imaging Could Detect Early Kidney Fibrosis appeared first on Inside Precision Medicine.

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Surgeon general’s office issues warning on screen time for children

WASHINGTON — Health secretary Robert F. Kennedy Jr. is issuing a U.S. surgeon general’s advisory urging families, schools, and providers to reduce children’s screen time, according to a draft of the report reviewed by STAT.

The report, which comes from the surgeon general’s office even though there is no Senate-confirmed nominee, calls on parents, schools, and all levels of government to work together to shift the use of devices with screens, saying some patterns of use “can pose real harm to children.”

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Ultra-Short Radiotherapy Schedule for Prostate Cancer Supported by HERMES Trial

U.K. research has shown that condensing prostate cancer radiotherapy into two sessions, but with a higher dose per session, rather than the traditional five sessions does not lead to increased side effects when treatment is delivered using state-of-the-art magnetic resonance imaging (MRI)-guided technology.

Lead researcher Sian Cooper, clinical research fellow at The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research in London, told Inside Precision Medicine that the results of the HERMES trial “point toward a future where prostate radiotherapy is tailored to the disease, to the individual’s anatomy and symptom profile, whilst being mindful of the impact of treatment time on patients’ lives.”

Presenting at the 2026 Congress of the European Society for Radiotherapy and Oncology (ESTRO), Cooper said that the number of people diagnosed with prostate cancer is projected to double by 2040, meaning the demand for effective and efficient treatment has never been greater.

At present, localized prostate cancer is typically treated with stereotactic body radiotherapy over five sessions, but there has been increasing interest delivering the treatment in fewer sessions, with a larger dose each session.

“For patients, a two-session treatment course would be far less disruptive than the weeks of daily hospital visits that radiotherapy has traditionally required. This convenience comes with clear benefits for work, leisure, family life and travel. For clinicians and health systems, fewer fractions mean faster workflow throughput, and getting patients the right treatment, quicker,” said Cooper.

“We wanted to find out whether giving the equivalent dose in just two treatment sessions could be feasible and safe for patients, and to understand how it might affect the potential side effects patients can experience, such as problems with urinary and bowel function.”

The move toward giving higher radiotherapy doses in fewer sessions has been made possible by improvements in radiotherapy delivery technology over recent years.

“It has allowed us to harness the power of modern computing and discoveries in clinical physics, to create radiotherapy doses which conform very tightly to the edge of the prostate,” Cooper explained. “This results in vastly less dose to the normal, healthy tissues around the cancer.”

The HERMES trial used a Unity MR-Linac (Elekta AB, Sweden) machine, which Cooper describes as “the ultimate evolution of this progress,” to deliver radiotherapy to participants.

The device combines real-time MR scanning with a linear accelerator and is known as MRI-guided adaptive radiotherapy. It allows adaptation of the radiotherapy beam design to changes in patient anatomy on the treatment day as well as moment by moment motion management, meaning that if there are any changes in the target or healthy bystander organs, the radiation beam can be switched off.

“This level of precision was needed to safely deliver the high dose of radiation necessary to maintain the biologically equivalent dose in just two fractions,” Cooper noted.

In all, 46 participants (median age 74 years) with intermediate or lower high-risk prostate cancer were randomly assigned to receive radiotherapy at a dose of 24 Gy in two fractions over 8 days, with a 27 Gy integrated boost to the MRI defined tumor (n=22), or 36.25 Gy in five fractions over two weeks to the planning target volume, with 40 Gy to the prostate and proximal one cm seminal vesicles (n=24). All participants had androgen deprivation treatment for at least six months.

Cooper reported that, at two years, four (18%) participants in the two-fraction arm and six (25%) in the five-fraction arm experienced moderate (grade 2) urinary adverse events (AEs) such as increased frequency or urgency. Grade 2 gastrointestinal AEs occurred in one participant in each arm (5% and 4%, respectively).

There were no grade 3 or worse genitourinary or gastrointestinal events in either group.

The team also showed that quality of life, measured by the International Prostate Symptom Score and the International Index of Erectile Function, showed minimal change up to two years but will continue to be monitored up to five years.

Cooper said that the investigators will present efficacy data, a secondary endpoint, when it matures but she pointed out that as “the cancer control rate for prostate cancer is so high, it often takes many years for failures to appear, whereas trial data for ultra hypofractionation show that the genitourinary adverse event rate is the primary concern after treatment.”

ESTRO president, Matthias Guckenberger, MD, from University Hospital Zurich, Switzerland, who was not involved in the research said: “While the technology used in this trial is currently available in only a limited number of specialist centers worldwide, they are growing rapidly. These results can help guide how they are used and help us understand whether two-session radiotherapy should become a new standard of care.”

The post Ultra-Short Radiotherapy Schedule for Prostate Cancer Supported by HERMES Trial appeared first on Inside Precision Medicine.

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STAT+: Florida hospitals win $8 billion in extra Medicaid funds

The federal government is sending nearly $8 billion in supplemental Medicaid funds to hospitals in Florida for care they delivered last year, delivering a windfall to facilities in the politically influential state ahead of the imposition of new limits stemming from President Trump’s 2025 tax cut bill.

The extra cash is another victory for hospitals, which now are routinely getting paid much-higher commercial prices to treat Medicaid patients. Florida’s hospitals, which lobbied to get these extra funds approved, could get billions more on top of that once federal Medicaid officials mull over this year’s application.

On April 30, officials with the Centers for Medicare and Medicaid Services approved the money through a mechanism called a state directed payment program. CMS released the approval letters this week. The $8 billion, which also was authorized by state lawmakers, covers Medicaid patients treated from Oct. 1, 2024, through Sept. 30, 2025.

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STAT+: Scientists track cellular disruptions that lead to type 1 diabetes

The story of type 1 diabetes begins in the pancreas, long seen as a battleground between insulin-producing beta cells and misdirected immune defenders. Scientists have been searching for ways to spot this internal warfare early enough to prevent a lifelong disease that depletes the body’s source of insulin. 

Two new papers published Wednesday in Science Translational Medicine offer new clues to what happens in these beta cells before type 1 diabetes emerges. Experiments in human cells and in mouse models used biosensors and genetic analyses to illuminate this pathway and detect possible ways to halt beta-cell destruction.

In the first study, a team from the Indiana University School of Medicine explored how certain immune cells involved in inflammation, known as signaling cytokine interferon-alpha, normally trigger beta cells to produce other molecules  that play a role in inflammation, cell proliferation, and cell death. These reactive oxygen species, ROS for short, sometimes cause collateral damage. But cells from patients with type 1 diabetes did not have ROS-producing beta cells, suggesting they lacked the cytokines that stimulate their ROS production. That dearth might be useful in flagging the decline of beta cells early on in type 1 diabetes, the study authors surmised. 

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