Shape Anisotropy-Governed High-Performance Nanomagnetosol for In Vivo Magnetic Particle Imaging of Lungs.

Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has shown extensive lung manifestations in vulnerable individuals, putting lung imaging and monitoring at the forefront of early detection and treatment. Magnetic particle imaging (MPI) is an imaging modality, which can bring excellent contrast, sensitivity, and signal-to-noise ratios to lung imaging for the development of new theranostic approaches for respiratory diseases. Advances in MPI tracers would offer additional improvements and increase the potential for clinical translation of MPI. Here, a high-performance nanotracer based on shape anisotropy of magnetic nanoparticles is developed and its use in MPI imaging of the lung is demonstrated. Shape anisotropy proves to be a critical parameter for increasing signal intensity and resolution and exceeding those properties of conventional spherical nanoparticles. The 0D nanoparticles exhibit a 2-fold increase, while the 1D nanorods have a > 5-fold increase in signal intensity when compared to VivoTrax. Newly designed 1D nanorods displayed high signal intensities and excellent resolution in lung images. A spatiotemporal lung imaging study in mice revealed that this tracer offers new opportunities for monitoring disease and guiding intervention.

Deep learning-enabled quantification of simultaneous PET/MRI for cell transplantation monitoring.

Current methods of in vivo imaging islet cell transplants for diabetes using magnetic resonance imaging (MRI) are limited by their low sensitivity. Simultaneous positron emission tomography (PET)/MRI has greater sensitivity and ability to visualize cell metabolism. However, this dual-modality tool currently faces two major challenges for monitoring cells. Primarily, the dynamic conditions of PET such as signal decay and spatiotemporal change in radioactivity prevent accurate quantification of the transplanted cell number. In addition, selection bias from different radiologists renders human error in segmentation. This calls for the development of artificial intelligence algorithms for the automated analysis of PET/MRI of cell transplantations. Here, we combined K-means++ for segmentation with a convolutional neural network to predict radioactivity in cell-transplanted mouse models. This study provides a tool combining machine learning with a deep learning algorithm for monitoring islet cell transplantation through PET/MRI. It also unlocks a dynamic approach to automated segmentation and quantification of radioactivity in PET/MRI.

Synthesis and antibacterial activity study of a novel class of cationic anthraquinone analogs.

Reported previously by our group, one-pot cycloaddition using naphthoquinone, sodium azide and alkyl halides can lead to the formation of both 1-alkyl-1H- and 2-alkyl-2H-naphtho[2,3-d]triazole-4,9-diones. Herein, the effect of leaving group and additive in dictating the selectivity between the formation of 1-alkyl-1H- and 2-alkyl-2H-naphtho[2,3-d]triazole-4,9-diones has been further investigated. In the process of investigating the factors that control the selectivity and the biological activity associated with these two compounds, a novel class of antibacterial cationic anthraquinone analogs has been developed. Although these compounds are structurally similar, different antibacterial profiles are noted. One lead compound, 4e manifests high potency (MIC<1µg/mL) and selectivity against Gram positive (G+) pathogens including methicillin-resistant Staphylococcus aureus (MRSA) while exerting only modest activity against Gram negative (G-) bacteria. Other lead compounds (4f and 4g) exhibit broad antibacterial activity including MRSA and vancomycin-resistant Enterococcus faecalis (VRE) that is comparable to other commercially available cationic antiseptic chemicals. This unique difference in antibacterial profile may pave the way for the development of new therapeutic agents.