Researchers at the University of California, Davis (“UC Davis”) have developed a unisex swimwear garment designed to prevent swimming-related injuries and to assist in injury recovery during training.

This technology revolves around Optical Coherence Tomography (OCT), a noninvasive imaging method that provides detailed cross-sectional images of tissue microstructure and blood flow. OCT utilizes either time domain (TDOCT) or Fourier domain (FDOCT) approaches, with FDOCT offering superior sensitivity and speed. Doppler OCT combines Doppler principles with OCT to visualize tissue structure and blood flow concurrently. Additionally, polarization-sensitive OCT detects tissue birefringence. Advanced methods aim to enhance the speed and sensitivity of Doppler OCT, crucial for various clinical applications such as ocular diseases and cancer diagnosis. Swept source FDOCT systems further improve imaging capabilities by increasing range and sensitivity. Overall, this technology represents significant advancements in biomedical imaging, offering insights into both structural and functional aspects of tissue physiology.

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Researchers at the University of California, Davis have developed a sleep quality measuring device to measure waking electroencephalogram (EEG) test to determine the adequacy of sleep

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The invention entails a unique catheter device utilizing electromotive drug administration (EMDA) to enhance drug penetrance into tissues of the ureter, renal pelvis, and calyces. By incorporating a conductive wire and fluid delivery system, the catheter enables targeted drug delivery, potentially revolutionizing the treatment of kidney stones, urothelial carcinoma, infections, and inflammation without systemic side effects.

The invention pertains to a prosthetic valve featuring a saddle-shaped annulus that synchronously transforms between concave and convex configurations, facilitating seamless opening and closure synchronized with cardiac cycles. Comprising leaflets and support elements, the valve mimics natural heart valve function, enabling effective blood flow regulation and offering versatile deployment options for cardiac and vascular applications.

This technology describes a prosthetic heart valve system designed to accommodate the growth of children.

This technology is an innovative endoscopic device designed specifically for use in the ureter to remove ureteral and renal stone particle debris from patients. The device is equipped with a force sensor to ensure safe passage without injuring the ureteral wall.

Researchers at the University of California, Davis have developed a computer-based method to synthesize continuous speech from biosignals, including brain activity, in real-time.

Researchers at the University of California, Davis have developed a computer-based method to evaluate/quantify the intelligibility of speech synthesized by a brain-computer interface or other speech prosthesis.

An innovative direct drive hearing device, with a removable outside component that allows high quality sound and prolonged usage.

This patent application describes methods for non-invasive, label-free imaging of the cellular immune response in human skin using a nonlinear optical imaging system.

A novel method and device for high-throughput sorting of cells in suspension, particularly focusing on the separation of key cellular blood components of the immune system. The patent application presents a novel approach to high-throughput cell sorting, particularly suitable for applications in medicine and biotechnology where precise separation of cell populations is crucial.

The disclosed methods offer a robust approach to accurately quantify skin optical properties across different skin tones, facilitating improved diagnosis, monitoring, and treatment in dermatology.

An innovative technology that uses a device to move any imaging device precisely through a path in 3D space, enabling the generation of high-resolution 3D models.

Electrical properties (EP), such as permittivity and conductivity, dictate the interactions between electromagnetic waves and biological tissue. EP are biomarkers for pathology characterization, such as cancer. Imaging of EP helps monitor the health of the tissue and can provide important information in therapeutic procedures. Magnetic resonance (MR)-based electrical properties tomography (MR-EPT) uses MR measurements, such as the magnetic transmit field B1+, to reconstruct EP. These reconstructions rely on the calculations of spatial derivatives of the measured B1+. However, the numerical approximation of derivatives leads to noise amplifications introducing errors and artifacts in the reconstructions. Recently, a supervised learning-based method (DL-EPT) has been introduced to reconstruct robust EP maps from noisy measurements. Still, the pattern-matching nature of this method does not allow it to generalize for new samples since the network’s training is done on a limited number of simulated data pairs, which makes it unrealistic in clinical applications. Thus, there is a need for a robust and realistic method for EP map construction.

One of the key limitations for devices used in point-of-care diagnostics (POCD) is their limit of detection; patient samples used for POCD devices often contain too low of the target analyte. FlexThrough is a newly developed, centrifugal disk (CD)-based method that utilizes the entirety of a liquid sample via recirculation of the sample for efficient mixing as it iteratively passes through the system.

The LaserPack is an easily manufacturable solution for liquid storage in point-of-care devices that is low-cost, has dimensional variability, and is reproducible, while also serving as a valve for liquid access in microfluidic devices. Current liquid storage techniques rely on lyophilization, or freeze-drying, to minimize occupied space, but lyophilization is not applicable to all liquid reagents nor is it optimal for some biological components of point-of-care devices.

Researchers at UC Irvine have developed an optical detection system for SARS-CoV-2 and other pathogens that features improvements in screening time, cost, sensitivity, and practicality. As vaccine availability, economic pressure, and mental health considerations has gradually returned society to pre-pandemic activities that require frequent and close interactions, it is imperative that SARS-CoV-2 detection systems remain effective.

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Sensor-based patient monitoring is a promising approach to assess risk, which can then be used by healthcare clinics to focus efforts on the highest-risk patients without having to spend the time manually assessing risk. For example, pressure ulcers/injuries are localized damage to the skin and/or underlying tissue that usually occur over a bony prominence and are most common to develop in individuals who have low-mobility, such as those who are bedridden or confined to a wheelchair and consequently are attributed to some combination of pressure, friction, shear force, temperature, humidity, and restriction of blood flow and are more prevalent in patients with chronic health problems. Sensor-based patient monitoring can be tuned to the individual based on the relative sensor readings. However, existing sensor-based monitoring techniques, such as pressure monitoring, are one-off solutions that are not supported by a comprehensive system which integrates sensing, data collection, storage, data analysis, and visualization. While traditional monitoring solutions are suitable for its intended purpose, these approaches require substantial re-programming as the suites of monitoring sensors change over time.

Cardiovascular disease is the leading cause of death both worldwide and in the United States, with associated costs in the U.S. reaching approximately $229 billion, each, in 2017 and 2018. Early detection, which can drastically reduce both rates of death and treatment costs, requires access to facilities and highly-trained physicians that can be difficult to access in rural areas and developing countries—despite their prevalence of cardiovascular disease. Computer-based models that use, e.g., PCG (phonocardiogram), EKG (electrocardiogram), or other cardiac data, are a promising route to bridge the gap in standard-of-care for these underserved areas. However, current algorithms are unable to account for demographic features, such as race, sex, or other characteristics, which are known to affect both the structure of the heart and presentation of heart disease. To address this problem, UC Berkeley researchers have developed a new, cloud-based system for collecting a patient's continuous cardiovascular data, monitoring for and detecting disease, and keeping a doctor informed about the cardiac health of the patient. The system sends an alarm when disease or heart attack are detected. To generate the most accurate diagnoses by taking into account demographic information, the system includes private and ethical dataset collection and model-training techniques.

Researchers at UC Irvine have developed a novel, machine learning-assisted biochip for rapid, affordable, and practical analysis of single cell tumor heterogeneity. The technology’s low cost and ease of manufacture makes it an optimal point-of-care diagnostic in developing countries, where early cancer detection is severely lacking.