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Researchers at the University of California, Davis have developed a system and method for accurately extracting fetal photoplethysmography information from mixed maternal-fetal signals obtained non-invasively through the maternal abdomen.

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This invention introduces a groundbreaking Foundation Model that achieves expert-level accuracy in ECG diagnostics across 68 conditions, leveraging deep learning to overcome the need for extensive labeled data and enabling advanced predictions in data-scarce environments.

Researchers at the University of California, Davis have developed a nanoreactor that allows for accurate measurements of radiation dose even in the vicinity of other chemicals.

A breakthrough one-wire EEG cap with embedded electrode chips provides ultra-sensitive, noise-immune, wide-band brain signal acquisition. It enables non-invasive, real-time, high-resolution recording using dry electrodes, ideal for wearable and clinical neuro-technology applications.

An innovative non-invasive device that accurately determines the age of bruises for all skin types and tones, designed to assist in forensic investigations and medical diagnostics.

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Researchers at the University of California, Davis have developed a novel mass spectrometry database cataloging >2,000 biomarker compounds in exhaled breath condensate (EBC) for breath metabolomics research.

Researchers at the University of California, Davis have developed a diagnostic screen using DNA methylation and genetic variant analysis from newborn blood spots that enables early prediction of autism spectrum disorder (ASD) risk.

Researchers at the University of California, Davis have developed isolated peptides that selectively bind M1 and M2 macrophages to enable precise diagnosis and targeted treatment of macrophage-associated diseases, including cancer.

Current methods for treating nervous system disorders often rely on generalized approaches that may not optimally address the individual patient's specific pathology, leading to suboptimal outcomes. This innovation, developed by UC Berkeley researchers, provides a method to identify the most critical, or "influential," nodes within a patient's functional connectivity network derived from time-series data of an organ or organ system. The method involves obtaining multiple time-series datasets from an affected organ/system, using them to map the functional connectivity network, and then determining the most influential nodes within that network. By providing this specific and personalized information to a healthcare provider, a treatment can be prescribed that precisely targets the respective organ corresponding to these influential nodes. This personalized, data-driven approach offers a significant advantage over conventional treatments by focusing intervention on the most impactful biological targets, potentially leading to more effective and efficient patient care.

Chronic wounds affect over 6.5 million people in the United States costing more than $25B annually. 23% of military blast and burn wounds do not close, affecting a military patient's bone, skin, nerves. Moreover, 64% of military trauma have abnormal bone growth into soft tissue. Slow healing of recalcitrant wounds is a known and persistent problem, with incomplete healing, scarring, and abnormal tissue regeneration. Precise control of wound healing depends on physician's evaluation, experience. Physicians generally provide conditions and time for body to either heal itself, or to accept and heal around direct transplantations, and their practice relies a lot on passive recovery. And while newer static approaches have demonstrated enhanced growth of non-regenerative tissue, they do not adapt to the changing state of wound, thus resulting in limited efficacy.

This technology represents a groundbreaking approach to generating and using biomolecule-loaded extracellular vesicles (EVs) for targeted cellular reprogramming.

Pressure injuries (commonly called bedsores or pressure ulcers) represent one of the most persistent and costly challenges in healthcare, affecting over 2.5 million US patients and costing almost $27B in 2019. Hospital-acquired pressure injury events occur in about 3% in general populations and about 6% in intensive care units (ICUs). Current prevention strategies still rely on the Braden Scale risk assessment tool as the gold standard. Developed in the 80s, it is used to stratify patients into risk categories based on factors like sensory perception, moisture, mobility, and friction. The Braden score directly informs turning frequency as the standard of protocol. Unfortunately, medical staff adherence to turning protocols remains low at ~50% nationally, creating a gap between prescribed care and actual implementation. Technologies to help assess by sensing pressure injuries have limitations, including discontinuous monitoring requiring manual interpretation, and lack of objective mobility metrics. These fail to account for the complex interplay between pressure distribution, patient movement patterns, and individual risk factors. The Braden-scoring approach is particularly problematic as it does not account for the presence of existing pressure injuries or patient-specific factors, and has been shown to have inadequate validity for ICU patients. Additionally, current pressure mapping systems are typically large, expensive, and require specialized training, limiting their practical deployment in routine clinical care.

Researchers at the University of California, Davis have developed non-invasive fetal monitoring that enables accurate, continuous measurement of fetal arterial blood oxygen saturation and blood pH.

A revolutionary method for improving the efficiency and quality of reprogramming adult cells into stem cells or other therapeutically relevant cell types via adhesome gene manipulation.

The rapid and accurate analysis of real-time quantitative polymerase chain reaction (qPCR) data is critical for precise disease diagnostics, genetic research, and pathogen detection. However, manual interpretation is prone to human error, and current automated systems often struggle with noise and variability, leading to misdiagnosis or inaccurate results. Researchers at UC Berkeley have developed a Deep Learning System for Enhanced qPCR Data Analysis that addresses these challenges. The system utilizes an advanced deep learning model to analyze raw qPCR data in real-time, significantly improving diagnostic accuracy by identifying subtle patterns and anomalies that are difficult for human experts or conventional software to detect. This innovative approach leads to more reliable and faster results compared to traditional methods.

A novel composition of particles with a magnetic core encapsulated in a polymeric gel, offering versatile applications in biotech and research.

Innovative cell lines enabling precise genetic modifications to advance research in gene function, disease modeling, and potential therapeutic interventions.

This technology offers a revolutionary approach to point-of-care diagnosis and large-scale health surveillance by enabling portable, high-accuracy detection of proteins, bioparticles, and cells.

An advanced geometric method for comprehensive 3D cardiac strain analysis, enhancing diagnosis and monitoring of myocardial diseases.

A novel device leveraging centrifugal microfluidics to accelerate bacterial growth and rapidly determine antibiotic susceptibility.