A new device designed to improve the process of replacing heart valves through a minimally invasive procedure called transcatheter aortic valve replacement (TAVR).

 Professors Richard Schrock, Matthew Conley, and colleagues from the University of California, Riverside have developed a new Schrock catalysts for olefin metathesis that can be produced in fewer synthetic steps, activated with perfluorinated alcohols, and reactivated using light or heat. The method provides a more convenient route to a variety of Schrock catalysts that avoid corrosive triflic acid and reactive Grignard reagents to yield Schrock catalysts, which can then be converted readily into other catalyst variations. This technology is advantageous because it is a safer and less expensive way to synthesize and activate Schrock catalysts for industrial and research applications.  

Researchers at the University of California, Davis have developed a reversed feedback amplifier design for enhanced mm-wave signal amplification.

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A novel method for selectively targeting and modulating brain border-associated myeloid cells for the treatment of neurological disorders.

Researchers at the University of California, Davis have developed H2CBD, a fully synthetic analog of CBD designed to treat seizures without the psychoactive effects associated with Cannabis.

Professor Xiaoping Hu’s lab at the University of California, Riverside has developed a novel method that allows creatine to bypass the BBB and directly reach the brain. The technology works by delivering creatine intranasally using microparticles. These creatine particles have shown to not exhibit cytotoxicity, are highly stable, and are not disruptive to cell barriers. This technology is advantageous over traditional creatine monohydrate and anhydrous creatine because the smaller particle size ensures even distribution and greater permeability across the BBB. 

      Image quality and sample structure information from an optical microscope is in large part determined by optical aberrations. Optical aberrations originating from the microscope optics themselves or the sample can degrade the imaging performance of the system. Given the difficulty to find and correct all sources of aberration, a collection of methods termed adaptive optics is used to measure and correct optical aberrations in other ways, to recover imaging performance. However, state-of-the-art adaptive optics systems typically comprise complex hardware and software integration, which has impeded their wide adoption in microscopy. UC Berkeley researchers recently demonstrated how self-supervised machine learning (ML)-based adaptive optics can accurately estimate optical aberrations from a single 3D fluorescence image stack, without requiring external datasets for training. While demonstrated for widefield fluorescence microscopy, many optical microscopy modalities present unique challenges.       In the present technology, UC Berkeley researchers have developed a novel self-supervised ML-based adaptive optics system for two-photon fluorescence microscopy, which should also be extensible to confocal and other modalities. The system can effectively image tissues and samples for cell biology applications. Importantly, the method can address common errors in optical conjugation/alignment in commercial microscopy systems that have yet to be systematically addressed. It can also integrate advanced computational techniques to recover sample structure.