Active Targeting Hyaluronan Conjugated Nanoprobe for Magnetic Particle Imaging and Near-Infrared Fluorescence Imaging of Breast Cancer and Lung Metastasis.

A major contributing cause to breast cancer related death is metastasis. Moreover, breast cancer metastasis often shows little symptoms until a large area of the organs is occupied by metastatic cancer cells. Breast cancer multimodal imaging is attractive since it integrates advantages from several modalities, enabling more accurate cancer detection. Glycoprotein CD44 is overexpressed on most breast cancer cells and is the primary cell surface receptor for hyaluronan (HA). To facilitate breast cancer diagnosis, we report an indocyanine green (ICG) and HA conjugated iron oxide nanoparticle (NP-ICG-HA), which enabled active targeting to breast cancer by HA-CD44 interaction and detected metastasis with magnetic particle imaging (MPI) and near-infrared fluorescence imaging (NIR-FI). When evaluated in a transgenic breast cancer mouse model, NP-ICG-HA enabled the detection of multiple breast tumors in MPI and NIR-FI, providing more comprehensive images and a diagnosis of breast cancer. Furthermore, NP-ICG-HAs were evaluated in a lung metastasis model. Upon NP-ICG-HA administration, MPI showed clear signals in the lungs, indicating the tumor sites. This is the first time that HA-based NPs have enabled MPI of cancer. NP-ICG-HAs are an attractive platform for noninvasive detection of primary breast cancer and lung metastasis.

Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography (MSOT) imaging.

Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through-plane resolution volumetric MSOT is time-consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross-sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG-conjugated nanoworms particles (NWs-ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.

Super-resolution and segmentation deep learning for breast cancer histopathology image analysis.

Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images' size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model's results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.

Tunable, Low-Cost, Multi-Channel, Broadband Liquid Crystal Shutter for Fluorescence Imaging in Widefield Microscopy.

Bistable liquid crystal (LC) shutters have attracted much interest due to their low energy consumption and fast response time. In this paper, we demonstrate an electrically tunable/switchable biostable LC light shutter in biological optics through a three-step easy-assembly, inexpensive, multi-channel shutter. The liquid crystal exhibits tunable transparency (100% to 10% compared to the initial light intensity) under different voltages (0 V to 90 V), indicating its tunable potential. By using biomedical images, the response time, resolution, and light intensity changes of the LC under different voltages in three common fluorescence wavelengths are displayed intuitively. Particularly, the shutter's performance in tumor images under the near-infrared band shows its application potential in biomedical imaging fields.

Indocyanine Green-Conjugated Superparamagnetic Iron Oxide Nanoworm for Multimodality Breast Cancer Imaging.

Breast cancer is the leading cause of cancer-associated deaths among women. Techniques for non-invasive breast cancer detection and imaging are urgently needed. Multimodality breast cancer imaging is attractive since it can integrate advantages from several modalities, enabling more accurate cancer detection. In order to accomplish this, indocyanine green (ICG)-conjugated superparamagnetic iron oxide nanoworm (NW-ICG) has been synthesized as a contrast agent. When evaluated in a spontaneous mouse breast cancer model, NW-ICG gave a large tumor to normal tissue contrasts in multiple imaging modalities including magnetic particle imaging, near-infrared fluorescence imaging, and photoacoustic imaging, providing more comprehensive detection and imaging of breast cancer. Thus, NW-ICGs are an attractive platform for non-invasive breast cancer diagnosis.

A fully transparent, flexible PEDOT:PSS-ITO-Ag-ITO based microelectrode array for ECoG recording.

Integrative neural interfaces combining neurophysiology and optogenetics with neural imaging provide numerous opportunities for neuroscientists to study the structure and function of neural circuits in the brain. Such a comprehensive interface demands miniature electrode arrays with high transparency, mechanical flexibility, electrical conductivity, and biocompatibility. Conventional transparent microelectrodes made of a single material, such as indium tin oxide (ITO), ultrathin metals, graphene and poly-(3,4-ethylenedioxythiophene)/poly(styrenesulfonate) (PEDOT:PSS), hardly possess the desired combination of those properties. Herein, ultra-flexible, highly conductive and fully transparent microscale electrocorticogram (µECoG) electrode arrays made of a PEDOT:PSS-ITO-Ag-ITO assembly are constructed on thin parylene C films. The PEDOT:PSS-ITO-Ag-ITO assembly achieves a maximum ∼14% enhancement in light transmission over a broad spectrum (350-650 nm), a significant reduction in electrochemical impedance by 91.25%, and an increase in charge storage capacitance by 1229.78 µC cm-2. Peeling, bending, and Young's modulus tests verify the enhanced mechanical flexibility and robustness of the multilayer assembly. The µECoG electrodes enable electrical recordings with high signal-to-noise ratios (SNRs) (∼35-36 dB) under different color photostimulations, suggesting that the electrodes are resilient to photon-induced artifacts. In vivo animal experiments confirm that our array can successfully record light-evoked ECoG oscillations from the primary visual cortex (V1) of an anesthetized rat.

2D Au-Coated Resonant MEMS Scanner for NIR Fluorescence Intraoperative Confocal Microscope.

The electrostatic MEMS scanner plays an important role in the miniaturization of the microscopic imaging system. We have developed a new two-dimensional (2D) parametrically-resonant MEMS scanner with patterned Au coating (>90% reflectivity at an NIR 785-nm wavelength), for a near-infrared (NIR) fluorescence intraoperative confocal microscopic imaging system with a compact form factor. A silicon-on-insulator (SOI)-wafer based dicing-free microfabrication process has been developed for mass-production with high yield. Based on an in-plane comb-drive configuration, the resonant MEMS scanner performs 2D Lissajous pattern scanning with a large mechanical scanning angle (MSA, ±4°) on each axis at low driving voltage (36 V). A large field-of-view (FOV) has been achieved by using a post-objective scanning architecture of the confocal microscope. We have integrated the new MEMS scanner into a custom-made NIR fluorescence intraoperative confocal microscope with an outer diameter of 5.5 mm at its distal-end. Axial scanning has been achieved by using a piezoelectric actuator-based driving mechanism. We have successfully demonstrated ex vivo 2D imaging on human tissue specimens with up to five frames/s. The 2D resonant MEMS scanner can potentially be utilized for many applications, including multiphoton microendoscopy and wide-field endoscopy.