Researchers at the University of California, Davis have developed a unique approach to target solid tumors using novel Fas-targeting antibodies designed for improved selectivity and efficacy in immunotherapy.

      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. 

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Time-lapse microscopy allows for direct observation of cell biological processes at the single-cell level with high temporal resolution. Quantitative analysis of single-cell time-lapse microscopy requires automated segmentation and tracking of individual cells over several days. Precise segmentation and tracking remain challenging because cells change their shape, divide, and show unpredictable movements.Researchers at UC Santa Cruz applied recent advances in the application of deep-learning models to the analysis of cellular images. The result was a deep-learning-based model and a user-friendly software, termed DeepSea, that automates both the segmentation and tracking of individual cells in time-lapse microscopy images.

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This novel technology enables refined temporal control of protein-protein interactions that can be used to regulate cell therapies, including CAR T-cells and “cell factories”.

Researchers at the University of California, Davis, have developed new synthesis methods for the rapid and highly pure production of glycosphingolipids. The prototyped process can produce pure glycosphingolipids that can be used within basic disease research and drug and diagnostic development.