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Method to Identify Vomocytosis Events via Time-Lapse Fluorescence Microscopy

Researchers at the University of California, Davis have developed a method to identify vomocytosis events in time-lapse microscopy videos.

Nuclear Delivery and Transcriptional Repression with a Cell-penetrant MeCP2

Methyl-CpG-binding-protein 2 (MeCP2) is a nuclear protein expressed in all cell types, especially neurons. Mutations in the MECP2 gene cause Rett syndrome (RTT), an incurable neurological disorder that disproportionately affects young girls. Strategies to restore MeCP2 expression phenotypically reverse RTT-like symptoms in male and female MeCP2-deficient mice, suggesting that direct nuclear delivery of functional MeCP2 could restore MeCP2 activity.The inventors have discovered that ZF-tMeCP2, a conjugate of MeCP2(aa13-71, 313-484) and the cell-permeant mini-protein ZF5.3, binds DNA in a methylation-dependent manner and reaches the nucleus of model cell lines intact at concentrations above 700 nM. When delivered to live cells, ZF-tMeCP2 engages the NCoR/SMRT co-repressor complex and selectively represses transcription from methylated promoters. Efficient nuclear delivery of ZF-tMeCP2 relies on a unique endosomal escape portal provided by HOPS-dependent endosomal fusion.In a comparative evaluation, the inventors observed the Tat conjugate of MeCP2 (Tat-tMeCP2) (1) degrades within the nucleus, (2) is not selective for methylated promoters, and (3) traffics in a HOPS-independent manner. These results support the feasibility of a HOPS-dependent portal for delivering functional macromolecules to the cell interior using the cell-penetrant mini-protein ZF5.3. Such a strategy could broaden the impact of multiple families of protein-derived therapeutics.

(SD2022-180) Method of viral nanoparticle functionalization for therapy and imaging applications

Plant viral nanoparticles (plant VNPs) are promising biogenetic nanosystems for the delivery of therapeutic, immunotherapeutic, and diagnostic agents. The production of plant VNPs is simple and highly scalable through molecular farming in plants. Some of the important advances in VNP nanotechnology include genetic modification, disassembly/reassembly, and bioconjugation. Although effective, these methods often involve complex and time-consuming multi-step protocols.

Scanning Mechanism For Multimodality Intravascular Imaging Catheters

See patent application publication no. US20210282642A16. The present invention is directed to a system for multimodal imaging through the use of a dual-rotational imaging catheter. The system may comprise a swept-source laser for providing a light source for OCT and OCE imaging, and an optical fiber coupler that splits said light source into one for a compensation arm and the other for the imaging catheter. The imaging catheter may comprise a rotary apparatus for a first scanning method, and a distal motor for a second scanning method. The dual-rotational model may allow for optimal performance of multiple imaging modalities. The imaging catheter may utilize optical imaging and acoustic imaging. A balanced photodetector receives input from the destinations of both light sources to offset DC noise. An US pulser/receiver is used for US imaging, a multifunction I/O module, a function generator, and an amplifier are used for generating an acoustic excitation force for OCE imaging.

Modular Piezoelectric Sensor Array with Beamforming Channels for Ultrasound Imaging

Researchers at the University of California, Davis have developed a large area sensor array for ultrasound imaging systems that utilizes high-bandwidth piezoelectric sensors and modular design elements.

Magnetically Responsive Photonic Nanochains

Brief description not available

(SD2020-421) Virtual Electrodes for Imaging of Cortex-Wide Brain Activity: Decoding of cortex-wide brain activity from local recordings of neural potentials

As an important tool for electrophysiological recordings, neural electrodes implanted on the brain surface have been instrumental in basic neuroscience research to study large-scale neural dynamics in various cognitive processes, such as sensorimotor processing as well as learning and memory. In clinical settings, neural recordings have been adopted as a standard tool to monitor the brain activity in epilepsy patients before surgery for detection and localization of epileptogenic zones initiating seizures and functional cortical mapping. Neural activity recorded from the brain surface exhibits rich information content about the collective neural activities reflecting the cognitive states and brain functions. For the interpretation of surface potentials in terms of their neural correlates, most research has focused on local neural activities.   From basic neuroscience research to clinical treatments and neural engineering, electrocorticography (ECoG) has been widely used to record surface potentials to evaluate brain function and develop neuroprosthetic devices. However, the requirement of invasive surgeries for implanting ECoG arrays significantly limits the coverage of different cortical regions, preventing simultaneous recordings from spatially distributed cortical networks. However, this rich information content of surface potentials encoded for the large-scale cortical activity remains unexploited and little is known on how local surface potentials are correlated with the spontaneous neural activities of distributed large-scale cortical networks. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

(SD2021-212) A tool to assess and monitor wound health

Background. Existing wound care practices use visual cues that are largely superficial in nature. The visual nature of the exams makes them very subjective and there is extensive inhomogeneity in wound evaluations between different healthcare professionals. Imaging is an indispensable tool to see what the eye cannot. Current techniques are limited to image a few millimeters deep into wounded tissue, thus visual examination is limited to the skin surface whereas wounds can exacerbate from deep within soft tissues.

Viral Capsid Mutants with Dramatically Enhanced Cancer Cell Uptake

The inventors have identified two double mutant capsids with positively charged surfaces for improved delivery of small molecule- and nucleic acid-based therapeutics. Fitness landscape engineering of bacteriophage MS2 virus-like particles (VLPs) was used as a guide for the discovery. The engineered capsid variants have internalization efficiencies as much as 67-times higher than wild-type MS2 VLPs. Internalization of these cationic variants depends on interactions with cell surface sulfated proteoglycans. They are bio-physically similar to wild-type MS2 with low cytotoxicity. The best-performing cationic MS2 capsids can deliver a potent anticancer small molecule therapeutic with efficacy levels similar to antibody-drug conjugates.  

(SD2019-220) Spatiotemporal resolution enhancement of biomedical images

Cardiac MRI is the clinical reference standard for visual and quantitative assessment of heart function. Specifically, cine balanced steady-state free precession (SSFP) can yield cardiac images with high myocardium–blood pool contrast for evaluation of left ventricular (LV) function. However, MRI suffers from long acquisition times, often requiring averaging across multiple heartbeats, and necessitates a trade-off among spatial resolution, temporal resolution, and scan time. Clinically, radiologists are forced to balance acquisition time with resolution to fit clinical needs, and certain applications such as real-time imaging may require small acquisition matrices. Image scaling is typically performed by using conventional upscaling methods, such as Fourier domain zero padding and bicubic interpolation. These methods, however, do not readily recover spatial detail, such as the myocardium–blood pool interface or delineation of papillary muscles.

Fetal Oximetry Measurement via Maternal Transabdominal Spectroscopy

Researchers at the University of California, Davis have developed a non-invasive, near-infrared, spectroscopy technique that measures fetal oxygen saturation via the maternal abdomen.

Motor Drive Unit for Combined Optical Coherence Tomography and Fluorescence Lifetime Imaging of Intraluminal Structures

Researchers at the University of California, Davis have designed a motor drive unit that enables combined fluorescence lifetime imaging and optical coherence tomography of luminal structures.

(SD2021-402) Fully Automated Deep Learning‐Based Background Phase Error Correction for Abdominopelvic 4D Flow MRI

4D Flow MRI has become increasingly valuable for the qualitative and quantitative assessment of cardiovascular disease. Since all measurements can be obtained following image acquisition without the need for targeted ultrasonographic windows or placement of 2D phase contrast planes at the time of the exam, 4D Flow provides versatility that can be essential in the diagnostic process.However, the correction of magnetic eddy current-related background phase error remains a critical bottleneck in abdominal applications.

(SD2021-221) Automated deep correction of MRI phase‐error

Time-resolved 3D phase-contrast MRI with three-dimensional velocity encoding (4D Flow MRI) has become increasingly valuable for the evaluation of cardiovascular disease. While cardiothoracic and neurovascular applications have grown rapidly, a limiting factor for abdominal applications is the correction of magnetic eddy current-related background phase error, which can be more challenging to reliably correct in abdominopelvic regions due to complex vascular and soft tissue geometry. Phase-error correction is essential for both quantification of blood flow as well as for visualization.

(SD2021-401) Automated Correction of Background Phase Error for Cerebrovascular 4D Flow MRI

Currently, there are no automated solutions for phase‐error correction that are effective for brain imaging.

Improving Perfusion Magnetic Resonance Imaging Using Ultra-Fast Arterial Spin Labeling

Prof. Jia Guo and colleagues from the University of California, Riverside have developed a method for improving perfusion Magnetic Resonance Imaging (MRI) using Velocity Selective Arterial Spin Labeling (VSASL). This method uses VS labeling pulses that are capable to only label the blood that is moving within a narrow band of velocities and keep the blood moving at higher velocities unperturbed. This creates a small bolus of label that can be detected readily and quickly. This method provides MRI imaging that is far superior than conventional ASL MRI techniques with a doubled temporal resolution, improved signal-to-noise ratio (SNR) efficiency and quantification accuracy. Fig 1: Schematics showing how UCR’s narrow-band velocity selectivity enables ultra-fast perfusion imaging  

Advanced Imaging By LASER-Trained Algorithms Used To Process Broad-Field Light Photography and Videography

Diagnosing retinal disease, which affects over 200 million people worldwide, requires expensive and complicated analysis of the structure and function of retinal tissue. Recently, UCI developed a training algorithm which, for the first time, is able to assess tissue health from images collected using more common and less expensive optics.

Protein Inhibitor of Type VI-B CRISPR-Cas System

The inventors have discovered the first protein inhibitor of the type VI-B CRISPR-Cas system. By controlling this CRISPR system, one could possibly ameliorate the toxicity and off-target cleavage activity observed with the use of the type VI CRISPR system. Moreover, these proteins can also serve as an antidote for instances where the use of CRISPR-Cas technology poses a safety risk. Additionally, this technology can also be used for engineering genetic circuits in mammalian cells. This finding is of potential importance to many companies in the CRISPR space. 

Single Catheter System Combining Intravascular Ultrasound and Fiber-Based Fluorescence Lifetime Imaging

Researchers at the University of California, Davis have developed a catheter device that combines intravascular ultrasound with fluorescence lifetime imaging to better detect significant vascular conditions.

Deep Learning Techniques For In Vivo Elasticity Imaging

Imaging the material property distribution of solids has a broad range of applications in materials science, biomechanical engineering, and clinical diagnosis. For example, as various diseases progress, the elasticity of human cells, tissues, and organs can change significantly. If these changes in elasticity can be measured accurately over time, early detection and diagnosis of different disease states can be achieved. Elasticity imaging is an emerging method to qualitatively image the elasticity distribution of an inhomogeneous body. A long-standing goal of this imaging is to provide alternative methods of clinical palpation (e.g. manual breast examination) for reliable tumor diagnosis. The displacement distribution of a body under externally applied forces (or displacements) can be acquired by a variety of imaging techniques such as ultrasound, magnetic resonance, and digital image correlation. A strain distribution, determined by the gradient of a displacement distribution, can be computed (or approximated) from measured displacements. If the strain and stress distributions of a body are both known, the elasticity distribution can be computed using the constitutive elasticity equations. However, there is currently no technique that can measure the stress distribution of a body in vivo. Therefore, in elastography, the stress distribution of a body is commonly assumed to be uniform and a measured strain distribution can be interpreted as a relative elasticity distribution. This approach has the advantage of being easy to implement. The uniform stress assumption in this approach, however, is inaccurate for an inhomogeneous body. The stress field of a body can be distorted significantly near a hole, inclusion, or wherever the elasticity varies. Though strain-based elastography has been deployed on many commercial ultrasound diagnostic-imaging devices, the elasticity distribution predicted based on this method is prone to inaccuracies.To address these inaccuracies, researchers at UC Berkeley have developed a de novo imaging method to learn the elasticity of solids from measured strains. Our approach involves using deep neural networks supervised by the theory of elasticity and does not require labeled data for the training process. Results show that the Berkeley method can learn the hidden elasticity of solids accurately and is robust when it comes to noisy and missing measurements.

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