Rapid antimicrobial susceptibility testing (AST) is a method for quickly determining the most effective antibiotic therapy for patients with bacterial infections. These techniques enable the detection and quantification of antibiotic-resistant and susceptible bacteria metabolites at concentrations near or below ng/mL in complex media. Employing bacterial metabolites as a sensing platform, the system integrates machine learning data analysis processes to differentiate between antibiotic susceptibility and resistance in clinical infections within an hour. With the results, a clinician can prescribe appropriate medicine for the patient's bacterial infection.

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.

Researchers at the University of California, Davis have developed an invention that utilizes an integrated Raman scanning head and machine vision for high throughput chemical analysis of liquid biopsy samples.

The invention presents a microfluidic platform equipped with specialized trapping arrays and droplet generation capabilities, enabling precise control over the formation of microvortices within cell-laden droplets. This novel system facilitates the study of cell-cell interactions at a single-cell level, offering configurable microenvironments for analyzing cell dynamics and pair relationships.

An innovative mass spectrometry platform that utilizes sulfoxide-containing MS-cleavable heterobifunctional photoactivated cross-linkers to enhance protein structural elucidation.

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.

Professor Min Xue and colleagues from the University of California, Riverside have developed a new method of construction of a bicyclic peptide library featuring a novel stereo-diversified structure and a simplified construction strategy.  MYC inhibitors were synthesized to demonstrate this method. The method works by using a tandem ring-opening metathesis (ROM) and ring-closing metathesis (RCM) reaction (ROM-RCM) to cyclize the linear peptide library in a single step. This technology is advantageous because the resulting bicyclic peptide may be easily linearized for MS/MS sequencing with a one-step chemistry procedure. 

Researchers at the University of California, Davis have developed a method of using artificial intelligence for assessing the effectiveness or efficacy of drugs that is cheaper, faster, and more accurate than commonly used assay analyses.

Dysfluent speech modeling requires time-accurate and silence-aware transcription at both the word-level and phonetic-level. However, current research in dysfluency modeling primarily focuses on either transcription or detection, and the performance of each aspect remains limited.To address this problem, UC Berkeley researchers have developed a new unconstrained dysfluency modeling (UDM) approach that addresses both transcription and detection in an automatic and hierarchical manner. Furthermore, a simulated dysfluent dataset called VCTK++ enhances the capabilities of UDM in phonetic transcription. The effectiveness and robustness of UDM in both transcription and detection tasks has been demonstrated experimentally.UDM eliminates the need for extensive manual annotation by providing a comprehensive solution.

Lipoxygenases (LOX) are enzymes that catalyze the peroxidation of certain fatty acids. The cell membrane is mostly made of lipids (which include fatty acids), and peroxidation can cause damage to the cell membrane. The human genome contains six functional LOX genes that encode for six LOX enzyme variants, or isozymes. The role that each LOX isozyme plays in health and disease varies greatly, spanning issues such as asthma, diabetes, and stroke. LOX enzymes are extremely difficult to target due to high hydrophobicity. Potential leads are often ineffective because they are either not readily soluble or not selective for a particular LOX enzyme.  Studies have implicated human epithelial 15-lipoxygenase-2 (h15-LOX-2, ALOX15B) in various diseases. h15-LOX-2 is highly expressed in atherosclerotic plaques and is linked to the progression of macrophages to foam cells, which are present in atherosclerotic plaques. h15-LOX-2 mRNA levels are also highly elevated in human macrophages isolated from carotid atherosclerotic lesions in symptomatic patients. Children with cystic fibrosis had reduced levels of h15-LOX-2, which affects the lipoxin A4 to leukotriene B4 ratio. Furthermore, the interactions of h15-LOX-2 and PEBP1 changes the substrate specificity of h15-LOX-2 from free polyunsaturated fatty acids (PUFA) to PUFA-phosphatidylethanolamines (PE), leading to the generation of hydroperoxyeicosatetraenoic acid (HpETE) esterified into PE (HpETE-PE). Accumulation of these hydroperoxyl membrane phospholipids has been shown to cause ferroptotic cell death, which implicates h15-LOX-2 in neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and Huntington’s diseases.  

Engineers from UC San Diego have disclosed a new patent-pending technology (SHARP, VERTICALLY ALIGNED NANOWIRE ELECTRODE ARRAYS, HIGH-YIELD FABRICATION ANDINTRACELLULAR RECORDING) that minimizes the electrode size to an intracellular probe, and is scalable to integrate multiple channels at one platform and overcomes the previous disadvantages such as invasiveness and insensitivity. This newly disclosed improved technology reduces the number of steps and the number of metal layers used to increase the biocompatibility and device yield, as compared to an earlier disclosure for NEAs that were fabricated using a different process.

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.

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.

 As currently available antibiotics become ineffective due to the rise in antibiotic resistance among pathogenic bacteria, development of completely new classes of antibiotics is critical. Classic antibiotics target pathogens and commensal bacteria indiscriminately; therefore, their use puts selective pressure on both populations. Because of the abundance of commensals within a mammalian host, antibiotic resistance is thought to arise more frequently in commensal bacteria and is horizontally transferred to pathogens. In contrast to classic antibiotics, virulence blockers are compounds that selectively inhibit the expression or function of a virulence factor in a pathogen or group of pathogens. Advantages of virulence blockers are twofold. For one, selective pressure on a limited number of microbes, i.e., only pathogens expressing the molecular target of the virulence blocker, should limit the evolution of resistance. Second, the decreased commensal killing by virulence blockers has the potential to preserve a healthy microbiota, which is critical for maintaining gut homeostasis and defending against opportunistic pathogens. Type III secretion systems (T3SS) are bacterial appendages required by dozens of pathogens to cause disease, including Salmonella, enteropathogenic Escherichia coli (EPEC), Shigella, Pseudomonas, and Yersinia, but they are largely absent in nonpathogenic bacteria. Bacteria use T3SS to inject bacterial effector proteins into target host cells to manipulate host processes for the benefit of the pathogen. Seven T3SS injectisome families have been identified and share a number of homologous membrane-associated components with the flagellar basal body. Agents that target T3SS would be key virulance blockers for a set of pathogens that are very important to human and animal health as are methods of screening for such agents. 

During initial stages of development, the human brain self assembles from a vast network of billions of neurons into a system capable of sophisticated cognitive behaviors. The human brain maintains these capabilities over a lifetime of homeostasis, and neuroscience helps us explore the brain’s capabilities. The pace of progress in neuroscience depends on experimental toolkits available to researchers. New tools are required to explore new forms of experiments and to achieve better statistical certainty.Significant challenges remain in modern neuroscience in terms of unifying processes at the macroscopic and microscopic scale. Recently, brain organoids, three-dimensional neural tissue structures generated from human stem cells, are being used to model neural development and connectivity. Organoids are more realistic than two-dimensional cultures, recapitulating the brain, which is inherently three-dimensional. While progress has been made studying large-scale brain patterns or behaviors, as well as understanding the brain at a cellular level, it’s still unclear how smaller neural interactions (e.g., on the order of 10,000 cells) create meaningful cognition. Furthermore, systems for interrogation, observation, and data acquisition for such in vitro cultures, in addition to streaming data online to link with these analysis infrastructures, remains a challenge.

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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.

Researchers at UC Irvine have redesigned the vaginal speculum, a medical device routinely used for pap smears, and other medical procedures that involve inspection of the vaginal canal (i.e. IUD insertions, STD testing, and hysterectomies). The novel design addresses several patient discomforts associated with currently used speculums and is more time- and cost-effective for health professionals.

DP-L4056 is a prophage-cured strain of Listeria monocytogenes based on wild-type strain 10403S. A prophage is a bacteriophage genome that is integrated into a bacterial genome. It remains latent until activation by an external factor, and activation leads to production of new bacteriophage particles that lyse the bacterial cell and spread. Curing the prophages in Listeria monocytogenes strain 10403S, which is ubiquitous in the microbiology community as a wild-type reference strain, allows for more predictable engineering and performance of Listeria monocytogenes.

Existing screening tools for respiratory pathogens, including PCR-based methods and antibody-based methods, are generally time-consuming to perform and analyze, difficult to manufacture at scale, and reliant on a detailed understanding of the targeted pathogen. Additionally, these traditional methods give little insight into the extent to which an individual is capable of spreading the disease. All of these features hamstring early responses to emerging pathogens and early-stage epidemics, as can be seen from the ongoing SARS-COV-2 pandemic. To address these problems, researchers at UC Berkeley have developed a device which ionizes large biomolecules from aerosol droplets and routes them to the inlet of a mass spectrometer or ion mobility spectrometer for identification based on size and/or mass. This can serve as the basis for a screening tool which measures the concentration of pathogenic particles, including common respiratory viruses and bacteria, in the breath. Results from this test could be read out in a matter of seconds, and it does not depend on detailed knowledge of the pathogen in question. Researchers have demonstrated the efficacy of such a device in detecting both large human proteins and virus-sized styrofoam particles.

Researchers at the University of California, Davis have developed a method of biofluid assessment capable of real-time monitoring as well as compatible with machine learning and neural network processing.