Researchers at the University of California, Davis have developed engineered amyloid fibrils composed of modified β-solenoid proteins fused with pathogen-binding domains that provide ultra-sensitive, stable, and versatile platforms for detecting viruses and other pathogens.

Researchers at the University of California, Davis have developed a machine learning system for accurately separating fetal signals from mixed maternal-fetal photoplethysmography signals acquired non-invasively to enable fetal physiological parameter monitoring.

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Researchers at the University of California, Davis have developed a computer-implemented method for accurately classifying congenital heart defects in newborns using pulse oximetry and machine learning.

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An advanced wearable bio-electronic device for non-invasive abnormality prediction, early diagnostics, and disease prevention.

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Chronic and complex wounds represent a substantial clinical and economic burden, affecting more than 6.5 million individuals in the United States and accounting for annual healthcare expenditures exceeding $25 billion. These wounds, including those arising from trauma such as blast and burn injuries, frequently involve multiple tissue types—e.g., skin, bone, and nerve—and are often associated with delayed or incomplete closure. In certain severe trauma populations, complications such as heterotopic ossification, characterized by abnormal bone formation within soft tissue, are observed at elevated incidence. More broadly, recalcitrant wounds are characterized by impaired healing dynamics, including persistent inflammation, fibrosis, and aberrant tissue regeneration. There are barriers to effective recovery because current standards of care have several critical limitations. Most therapies are “reactive” rather than “proactive” and they fail to adapt to the wound’s shifting physiological state, such as fluctuating pH or oxygen levels. Conventional devices use rigid or semi-rigid components, and this mismatch does not conform to contoured or mobile areas like the heel or joints. Moreover, semi-flexible electronics often lose contact during patient movement, and this inconsistent contact leading to sub-therapeutic dosing and persistent inflammation. Bridging this gap requires conformal, bio-integrated systems capable of sustained contact and autonomous, responsive therapeutic delivery to overcome the stagnant healing dynamics of recalcitrant wounds.

Chronic and complex wounds present a massive challenge for both patients and the healthcare system. In the United States alone, over 6.5 million people struggle with these injuries. Recent clinical data suggests that treatment costs now exceed $30 billion dollars annually. These wounds often include diabetic foot ulcers, bedsores, and severe trauma from accidents or combat. These wounds rarely heal on their own because they frequently suffer from poor blood flow and stalled healing processes. In extreme cases such as combat-related amputations, patients may even develop heterotopic ossification, which is a specific complication where bone mistakenly grows inside soft muscle tissue, making the recovery process even harder. Standard wound care is often reactive rather than proactive. Doctors usually check a wound every few days or weeks and apply treatments that do not change until the next visit. While tools like vacuum-assisted healing or lab-grown skin have helped to a certain degree, they have major drawbacks, including too bulky or complicated to administer and use at home. Moreover, these do not address the biggest flaw in today's wound care in that it is essentially "blind" between doctor visits, so while your body’s chemistry can change over hours and days, the current standard of care remains stubbornly static. Recent clinical data shows that this lack of precision is more than just an inconvenience; it is a primary reason why chronic wounds stall.

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A mechanically adaptive hydrogel that changes color in response to force exerted by living cells, enabling force sensing through optical signals.

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A novel method for detecting, diagnosing, monitoring, and treating gastrointestinal cancers by analyzing DNA methylation levels in patient samples.

Researchers at the University of California, Davis have developed a machine learning system that accurately detects and estimates retinal inflammation severity in uveitis patients using non-invasive OCT B-scan images.

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