Xin Liu

We report a new type of heterogeneous nanoparticles (NPs) composed of a heavily doped semiconductor domain (Cu2-xSe) and a metal domain (Au), which exhibit a broad localized surface plasmon resonance (LSPR) across visible and near-infrared (NIR) wavelengths, arising from interactions between the two nanocrystal domains. We demonstrate both in vivo photoacoustic imaging and in vitro dark field imaging, using the broad LSPR in Cu2-xSe-Au hybrid NPs to achieve contrast at different wavelengths. The high photoacoustic imaging depth achieved, up to 17 mm, shows that these novel contrast agents could be clinically relevant. More broadly, this work demonstrates a new strategy for tuning LSPR absorbance by engineering the density of free charge carriers in two interacting domains.

Photoacoustic (PA) imaging, as a fast growing technology that combines the high contrast of light and large penetration depth of ultrasound, has demonstrated great potential for molecular imaging of cancer. However, PA molecular imaging of orthotopic brain tumors is still challenging, partially due to the limited options and insufficient sensitivity of available PA molecular probes. Here, the direct formation of single‐layer (S‐MoS 2 ), few‐layer (F‐MoS 2 ), and multi‐layer (M‐MoS 2 ) nanosheets by the albumin‐assisted exfoliation without further surface modifications is reported. It is demonstrated that the PA effect of the MoS 2 nanosheets is highly dependent on their layered nanostructures. Decreasing the number of nanosheet layers from M‐MoS 2 to S‐MoS 2 can both significantly enhance the near‐infrared light absorption and improve the elastic properties of the nanomaterial, resulting in greatly amplified PA effect. The in vitro experiments demonstrate that the prepared S‐MoS 2 with excellent biocompatibility can be efficiently internalized into U87 glioma cells, producing strong PA signals for highly sensitive detection of brain tumor cells, with a detection limit of ≈100 cells. Intravenous administration of S‐MoS 2 to both U87 subcutaneous and orthotopic tumor‐bearing mice shows highly efficient tumor retention and significantly enhanced PA contrast. Tumor tissue ≈1.5 mm below the skull can still be clearly visualized in vivo. Previous studies suggest that the fabricated S‐MoS 2 with amplified PA effect have high potential to serve as an efficient nanoplatform for sensitive PA molecular imaging and hold promising prospect for translational medicine.

By localizing microbubbles (MBs) in the vasculature, ultrasound localization microscopy (ULM) has recently been proposed, which greatly improves the spatial resolution of ultrasound (US) imaging and will be helpful for clinical diagnosis. Nevertheless, several challenges remain in fast ULM imaging. The main problems are that current localization methods used to implement fast ULM imaging, e.g., a previously reported localization method based on sparse recovery (CS-ULM), suffer from long data-processing time and exhaustive parameter tuning (optimization). To address these problems, in this paper, we propose a ULM method based on deep learning, which is achieved by using a modified sub-pixel convolutional neural network (CNN), termed as mSPCN-ULM. Simulations and in vivo experiments are performed to evaluate the performance of mSPCN-ULM. Simulation results show that even if under high-density condition (6.4 MBs/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), a high localization precision (~28 μm in the lateral direction and ~24 μm in the axial direction) and a high localization reliability (Jaccard index of 0.66) can be obtained by mSPCN-ULM, compared to CS-ULM. The in vivo experimental results indicate that with plane wave scan at a transmit center frequency of 15.625 MHz, microvessels with diameters of ~17 μm can be detected and adjacent microvessels with a distance of ~42 μm can be separated. Furthermore, when using GPU acceleration, the data-processing time of mSPCN-ULM can be shortened to ~6 sec/frame in the simulations and ~23 sec/frame in the in vivo experiments, which is 3-4 orders of magnitude faster than CS-ULM. Finally, once the network is trained, mSPCN-ULM does not need parameter tuning to implement ULM. As a result, mSPCN-ULM opens the door to implement ULM with fast data-processing speed, high imaging accuracy, short data-acquisition time, and high flexibility (robustness to parameters) characteristics.

Abstract Modulating the emission wavelengths of materials has always been a primary focus of fluorescence technology. Nanocrystals (NCs) doped with lanthanide ions with rich energy levels can produce a variety of emissions at different excitation wavelengths. However, the control of multimodal emissions of these ions has remained a challenge. Herein, we present a new composition of Er 3+ ‐based lanthanide NCs with color‐switchable output under irradiation with 980, 808, or 1535 nm light for information security. The variation of excitation wavelengths changes the intensity ratio of visible (Vis)/near‐infrared (NIR‐II) emissions. Taking advantage of the Vis/NIR‐II multimodal emissions of NCs and deep learning, we successfully demonstrated the storage and decoding of visible light information in pork tissue.