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.

Researchers at the University of California, Davis have developed a technology that introduces a TCTS (Two-component Two-step) drug delivery system designed to enhance cancer treatment efficacy while minimizing toxicity.

Researchers at the University of California, Davis have developed a nanoparticle system combining photothermal therapy and chemotherapy for enhanced cancer treatment.

Something that is chiral cannot be superposed over its mirror image, no matter how it is shifted (ex. our hands). These two mirror images, called enantiomers, rotate plane-polarized light in opposite directions.Chiral nanostructures have unique materials properties that can be used in many applications. In pharmaceutical research and development, chiral analysis is critical, as one enantiomer may be more effective than the other. Researchers at UC Santa Cruz have developed new ways of performing enantiomeric analyses using the plasmonic circular dichroism absorption qualities of nanostructures. 

Researchers at the University of California, Davis have developed a technology that introduces an approach to creating semi-living, non-replicating cellular systems for advanced therapeutic applications.

Scaled neural sensing has been pursued for decades. Physical limitations associated with electrical (electrode-based) field recordings hinder advances in both field of view and spatial resolution. Electrochromic plasmonics (electro-plasmonics) has emerged as a rapidly advancing field combining traditional electrochromic materials with plasmonic nanostructures, including recent demonstrations of electrochromic-loaded plasmonic nanoantennas for optical voltage sensing. Existing optical electrophysiology techniques face critical limitations including poor signal-to-noise ratios due to low photon counts from genetically encoded voltage indicators, which have small cross-sections and low quantum yields. Fluorescent voltage indicators suffer from photobleaching, phototoxicity, and require genetic modifications that limit their clinical applicability. Current electrochromic devices also struggle with limited cycling stability, slow switching times, and restricted color options, and conventional plasmonic sensors exhibit inherently low electric field sensitivity due to high electron densities of metals like gold and silver. Current approaches to electro-plasmonics lack stable, high-contrast optical modulators that can operate at sub-millisecond speeds while maintaining human biocompatibility.

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. 

      Single-particle/single-molecule tracking (SPT) is a key tool for quantifying molecular motion in cells and in vitro. Wide-field SPT, in particular, can yield super-resolution mapping of physicochemical parameters and molecular interactions at the nanoscale, especially when integrated with single-molecule localization microscopy techniques like photoactivation and fluorophore exchange. However, wide-field SPT is often limited to the slow (<10 μm2/s) diffusion of molecules bound to membranes, chromosomes, or the small volume of bacteria, in part due to the ~10 ms framerate of common single-molecule cameras like electron-multiplying charge-coupled devices (EM-CCDs); for unbound diffusion in the mammalian cell and in solution, a molecule readily diffuses out of the <1 μm focal range of high-numerical-aperture objective lenses within 10 ms. While recent advances such as ultra-highspeed intensified CMOS cameras, feedback control by locking onto a molecule, trapping, and tandem excitation pulse schemes address the framerate issue, each also introduces drawbacks in light/signal efficiency, speed, uninterrupted diffusion paths, and/or trajectory resolution, e.g., number of time points.      UC Berkeley researchers have overcome these myriad challenges by introducing spatially-encoded dynamics tracking (SpeedyTrack), a strategy to enable direct microsecond wide-field single-molecule tracking/imaging on common microscopy setups. Wide-field tracking is achieved for freely diffusing molecules at down to 50 microsecond temporal resolutions for >30 timepoints, permitting trajectory analysis to quantify diffusion coefficients up to 1,000 um2/s. Concurrent acquisition of single-molecule diffusion trajectories and Forster resonance energy transfer (FRET) time traces further elucidates conformational dynamics and binding states for diffusing molecules. Moreover, spatial and temporal information is deconvolved to map long, fast single-molecule trajectories at the super-resolution level, thus resolving the diffusion mode of a fluorescent protein in live cells with nanoscale resolution. Already substantially outperforming existing approaches, SpeedyTrack stands out further for its simplicity—directly working off the built-in functionalities of EM-CCDs without the need to modify existing optics or electronics.

A cutting-edge vaccine delivery platform that enhances tumor treatment by co-delivering MHC class I and II restricted antigens.

Researchers at the University of California, Davis have developed an approach for the rapid inactivation of bacteria and virus using photo-active matrices enhanced with bio-affinity ligands under daylight irradiation conditions.

Researchers at the University of California, Davis have developed an algorithm for designing and identifying complex structures having custom release profiles for controlled drug delivery.

      Biologics are antibodies produced by genetically engineered cells and are widely used in therapeutic applications. Examples include pembrolizumab (Keytruda) and atezolizumab (Tecentriq), both employed in cancer immunotherapy as checkpoint inhibitors to restore T- cell immune responses against tumor cells. These biologics are produced by engineered cells in bioreactors in a process that is highly sensitive to the bioreactor environment, making it essential to integrate process analytical technologies (PAT) for closed-loop, real-time adjustments. Recent trends have focused on leveraging integrated circuit (IC) solutions for system miniaturization and enhanced functionality, for example enabling a single IC that monitors O2, pH, oxidation-reduction potential (ORP), temperature, and glucose levels. However, no current technology can directly and continuously quantify the concentration and quality of the produced biologics in real-time within the bioreactor. Such critical measurements still rely on off-line methods such as immunoassays and mass spectrometry, which are time-consuming and not suitable for real- time process control.       UC Berkeley researchers have developed a microsystem for real-time, in-vivo monitoring of antibody therapeutics using structure-switching aptamers by employing an integrator-based readout front-end. This approach effectively addresses the challenge of a 100× reduction in signal levels compared to the measurement of small-molecule drugs in prior works. The microsystem is also uniquely suited to the emerging paradigm of “living pharmacies.” In living pharmacies, drug-producing cells will be hosted on implantable devices, and real-time monitoring of drug production/diffusion rates based on an individual’s pharmokinetics will be crucial.

      Integrating microelectronics with microfluidics, especially those implemented in silicon-based CMOS technology, has driven the next generation of in vitro diagnostics. CMOS/microfluidics platforms offer (1) close interfaces between electronics and biological samples, and (2) tight integration of readout circuits with multi-channel microfluidics, both of which are crucial factors in achieving enhanced sensitivity and detection throughput. Conventionally bulky benchtop instruments are now being transformed into millimeter-sized form factors at low cost, making the deployment for Point-of-Care (PoC) applications feasible. However, conventional CMOS/microfluidics integration suffers from significant misalignment between the microfluidics and the sensing transducers on the chip, especially when the transducer sizes are reduced or the microfluidic channel width shrinks, due to limitations of current fabrication methods.       UC Berkeley researchers have developed a novel methodology for fabricating microfluidics platforms closely embedded within a silicon chip implemented in CMOS technology. The process utilizes a one-step approach to create fluidic channels directly within the CMOS technology and avoids the previously cited misalignment. Three types of structures are presented in a TSMC 180-nm CMOS chip: (1) passive microfluidics in the form of a micro-mixer and a 1:64 splitter, (2) fluidic channels with embedded ion-sensitive field-effect transistors (ISFETs) and Hall sensors, and (3) integrated on-chip impedance-sensing readout circuits including voltage drivers and a fully differential transimpedance amplifier (TIA). Sensors and transistors are functional pre- and post-etching with minimal changes in performance. Tight integration of fluidics and electronics is achieved, paving the way for future small-size, high-throughput lab-on-chip (LOC) devices.

This invention addresses the challenge of delivering macromolecules and other therapeutic cargo into the cell's cytoplasm by overcoming the endosomal membrane barrier. The innovation, developed by UC Berkeley researchers, involves improved versions of the ZF5.3 peptide. These improved peptide variants significantly enhance the efficiency of endosomal escape. This advancement provides a more effective and reliable method for intracellular delivery compared to existing alternatives, which often suffer from low efficiency or significant toxicity.

      Molecular dynamics (MD) is a computational materials science modality widely used in academic and industrial settings for materials discovery and more. A critical aspect of modern MD calculations are machine learning interatomic potentials (MLIPs), which learn from reference quantum mechanical calculations and predict the energy and forces of atomic configurations quickly. MLIPs allow for more accurate and comprehensive exploration of material/molecular properties at-scale. However, state-of-the-art MLIP methods mostly use a short-range approximation, which may be sufficient for describing properties of homogeneous bulk systems but fail for liquid-vapor interfaces, dielectric response, dilute ionic solutions with Debye-Huckel screening, and interactions between gas phase molecules. Short-range MLIPs neglect all long-range interactions, such as Coulomb and dispersion interactions.      To address the current shortcoming, UC Berkeley researchers have developed a straightforward and efficient algorithm to account for long-range interactions in MLIPs. The algorithm can predict system properties including those with charged, polar or apolar molecular dimers, bulk water, and water-vapor interfaces. In these cases standard short-range MLIPs lead to unphysical predictions, even when utilizing message passing algorithms. The present method eliminates artifacts while only about doubling the computational cost. Furthermore, it can be incorporated into most existing MLIP architectures, including potentials based on local atomic environments such as HDNPP, Gaussian Approximation Potentials (GAP), Moment Tensor Potentials (MTPs), atomic cluster expansion (ACE), and MPNN (e.g., NequIP, MACE).

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Electrodermal activity (EDA) has traditionally been used for monitoring mental activity by measuring skin conductance (SkinG) at locations with high sweat gland density. However, EDA has not been considered useful for physical activity due to baseline shifts caused by sweat accumulation at the skin/electrode interface.

      Quantum sensing is rapidly reshaping our ability to discern chemical processes with high sensitivity and spatial resolution. Many quantum sensors are based on nitrogen-vacancy (NV) centers in diamond, with nanodiamonds (NDs) providing a promising approach to chemical quantum sensing compared to single crystals for benefits in cost, deployability, and facile integration with the analyte. However, high-precision chemical quantum sensing suffers from large statistical errors from particle heterogeneity, fluorescence fluctuations related to particle orientation, and other unresolved challenges.      To overcome these obstacles, UC Berkeley researchers have developed a novel microfluidic chemical quantum sensing device capable of high-precision, background-free quantum sensing at high-throughput. The microfluidic device solves problems with heterogeneity while simultaneously ensuring close interaction with the analyte. The device further yields exceptional measurement stability, which has been demonstrated over >103s measurement and across ~105 droplets.  Greatly surpassing the stability seen in conventional quantum sensing experiments, these properties are also resistant to experimental variations and temperature shifts. Finally, the required ND sensor volumes are minuscule, costing only about $0.63 for an hour of analysis. 

Researchers at the University of California, Davis have developed a technology to expedite COVID-19 diagnosis and treatment using viral spike protein (S-protein) targeted peptides Zika virus envelop protein.

The rippled sheet was proposed by Pauling and Corey as a structural class in 1953. Following approximately a half century of only minimal activity in the field, the experimental foundation began to emerge, with some of the key papers published over the course of the last decade. Researchers at UC Santa Cruz have explored the structure of and have discovered ways to form new beta rippled sheets. 

The reliable detection of trace amounts of analytes, such as disease biomarkers or environmental toxins, requires complex and often time-consuming laboratory techniques, limiting rapid point-of-care diagnostics. This innovation provides a simple, rapid, and highly sensitive diagnostic method, termed Plasmonic Coffee-Ring Pattern Diagnostic Devices, for detecting analytes of interest. The core technology, developed by researchers, involves a specialized nanoporous or mesoporous hydrophilic membrane that has been chemically treated to achieve an intermediate wettability (intermediate between hydrophilic and hydrophobic). A drop of sample is allowed to dry on this treated membrane, capturing any target analyte. A subsequent, overlapping drop of functionalized gold nanoshells is applied, which interacts with the immobilized analyte to form a distinct, visible plasmonic pattern (a "coffee-ring" effect) that signals the analyte's presence. This pattern-based method enables the detection of analytes at concentrations as low as 5 pg/mL, offering an order-of-magnitude increase in sensitivity compared to many alternative rapid diagnostic platforms. Furthermore, the reproducible pattern can be read and interpreted using machine learning-assisted embodiments to precisely quantify the analyte present in the sample.

Researchers at the University of California, Davis have developed a self-assembling, fibrous photosensitizer that targets mitochondria in tumor cells for destruction via photodynamic therapy with enhanced localization and potency.

Measurement of electrical activity in nervous tissue has many applications in medicine, but the implantation of a large number of sensors is traditionally very risky and costly. Devices must be large due to their necessary complexity and power requirements, driving up the risk further and discouraging adoption. To address these problems, researchers at UC Berkeley have developed devices and methods to allow small, very simple and power-efficient sensors to transmit information by backscatter feedback. That is, a much more complex and powerful external interrogator sends an electromagnetic or ultrasound signal, which is modulated by the sensor nodes and reflected back to the interrogator. Machine learning algorithms are then able to map the reflected signals to nervous activity. The asymmetric nature of this process allows most of the complexity to be offloaded to the external interrogator, which is not subject to the same constraints as implanted devices. This allows for larger networks of nodes which can generate higher resolution data at lower risks and costs than existing devices.

Researchers at the University of California, Davis have developed a method to stop bacterial growth while maintaining desirable metabolic functions for therapeutic and biotechnological applications.