Mild near-infrared laser-triggered photo-immunotherapy potentiates immune checkpoint blockade via an all-in-one theranostic nanoplatform.

One of the primary challenges for immune checkpoint blockade (ICB)-based therapy is the limited infiltration of T lymphocytes (T cells) into tumors, often referred to as immunologically "cold" tumors. A promising strategy to enhance the anti-tumor efficacy of ICB is to increase antigen exposure, thereby enhancing T cell activation and converting "cold" tumors into "hot" ones. Herein, we present an innovative all-in-one therapeutic nanoplatform to realize local mild photothermal- and photodynamic-triggered antigen exposure, thereby improving the anti-tumor efficacy of ICB. This nanoplatform involves conjugating programmed death-ligand 1 antibody (aPD-L1) with gadolinium-doped near-infrared (NIR)-emitting carbon dots (aPD-L1@GdCDs), which displays negligible cytotoxicity in the absence of light. But under controlled NIR laser irradiation, the GdCDs produce combined photothermal and photodynamic effects. This not only results in tumor ablation but also induces immunogenic cell death (ICD), facilitating enhanced infiltration of CD8+ T cells in the tumor area. Importantly, the combination of aPD-L1 with photothermal and photodynamic therapies via aPD-L1@GdCDs significantly boosts CD8+ T cell infiltration, reduces tumor size, and improves anti-metastasis effects compared to either GdCDs-based phototherapy or aPD-L1 alone. In addition, the whole treatment process can be monitored by multi-modal fluorescence/photoacoustic/magnetic resonance imaging (FLI/PAI/MRI). Our study highlights a promising nanoplatform for cancer diagnosis and therapy, as well as paves the way to promote the efficacy of ICB therapy through mild photothermal- and photodynamic-triggered immunotherapy.

Optical coherence tomography angiography of the retina and choroid in systemic diseases.

Optical coherence tomography angiography (OCTA) has transformed ocular vascular imaging, revealing microvascular changes linked to various systemic diseases. This review explores its applications in diabetes, hypertension, cardiovascular diseases, and neurodegenerative diseases. While OCTA provides a valuable window into the body's microvasculature, interpreting the findings can be complex. Additionally, challenges exist due to the relative non-specificity of its findings where changes observed in OCTA might not be unique to a specific disease, variations between OCTA machines, the lack of a standardized normative database for comparison, and potential image artifacts. Despite these limitations, OCTA holds immense potential for the future. The review highlights promising advancements like quantitative analysis of OCTA images, integration of artificial intelligence for faster and more accurate interpretation, and multi-modal imaging combining OCTA with other techniques for a more comprehensive characterization of the ocular vasculature. Furthermore, OCTA's potential future role in personalized medicine, enabling tailored treatment plans based on individual OCTA findings, community screening programs for early disease detection, and longitudinal studies tracking disease progression over time is also discussed. In conclusion, OCTA presents a significant opportunity to improve our understanding and management of systemic diseases. Addressing current limitations and pursuing these exciting future directions can solidify OCTA as an indispensable tool for diagnosis, monitoring disease progression, and potentially guiding treatment decisions across various systemic health conditions.

Multimodal in-vivo maps as a tool to characterize retinal structural biomarkers for progression in adult-onset Stargardt disease.

Purpose: To characterize retinal structural biomarkers for progression in adult-onset Stargardt disease from multimodal retinal imaging in-vivo maps. Methods: Seven adult patients (29-69 years; 3 males) with genetically-confirmed and clinically diagnosed adult-onset Stargardt disease and age-matched healthy controls were imaged with confocal and non-confocal Adaptive Optics Scanning Light Ophthalmoscopy (AOSLO), optical coherence tomography (OCT), fundus infrared (FIR), short wavelength-autofluorescence (FAF) and color fundus photography (CFP). Images from each modality were scaled for differences in lateral magnification before montages of AOSLO images were aligned with en-face FIR, FAF and OCT scans to explore changes in retinal structure across imaging modalities. Photoreceptors, retinal pigment epithelium (RPE) cells, flecks, and other retinal alterations in macular regions were identified, delineated, and correlated across imaging modalities. Retinal layer-thicknesses were extracted from segmented OCT images in areas of normal appearance on clinical imaging and intact outer retinal structure on OCT. Eccentricity dependency in cell density was compared with retinal thickness and outer retinal layer thickness, evaluated across patients, and compared with data from healthy controls. Results: In patients with Stargardt disease, alterations in retinal structure were visible in different image modalities depending on layer location and structural properties. The patients had highly variable foveal structure, associated with equally variable visual acuity (-0.02 to 0.98 logMAR). Cone and rod photoreceptors, as well as RPE-like structures in some areas, could be quantified on non-confocal split-detection AOSLO images. RPE cells were also visible on dark field AOSLO images close to the foveal center. Hypo-reflective gaps of non-waveguiding cones (dark cones) were seen on confocal AOSLO in regions with clinically normal CFP, FIR, FAF and OCT appearance and an intact cone inner segment mosaic in three patients. Conclusion: Dark cones were identified as a possible first sign of retinal disease progression in adult-onset Stargardt disease as these are observed in retinal locations with otherwise normal appearance and outer retinal thickness. This corroborates a previous report where dark cones were proposed as a first sign of progression in childhood-onset Stargardt disease. This also supports the hypothesis that, in Stargardt disease, photoreceptor degeneration occurs before RPE cell death.

Segmentation model of soft tissue sarcoma based on self-supervised learning.

Introduction: Soft tissue sarcomas, similar in incidence to cervical and esophageal cancers, arise from various soft tissues like smooth muscle, fat, and fibrous tissue. Effective segmentation of sarcomas in imaging is crucial for accurate diagnosis. Methods: This study collected multi-modal MRI images from 45 patients with thigh soft tissue sarcoma, totaling 8,640 images. These images were annotated by clinicians to delineate the sarcoma regions, creating a comprehensive dataset. We developed a novel segmentation model based on the UNet framework, enhanced with residual networks and attention mechanisms for improved modality-specific information extraction. Additionally, self-supervised learning strategies were employed to optimize feature extraction capabilities of the encoders. Results: The new model demonstrated superior segmentation performance when using multi-modal MRI images compared to single-modal inputs. The effectiveness of the model in utilizing the created dataset was validated through various experimental setups, confirming the enhanced ability to characterize tumor regions across different modalities. Discussion: The integration of multi-modal MRI images and advanced machine learning techniques in our model significantly improves the segmentation of soft tissue sarcomas in thigh imaging. This advancement aids clinicians in better diagnosing and understanding the patient's condition, leveraging the strengths of different imaging modalities. Further studies could explore the application of these techniques to other types of soft tissue sarcomas and additional anatomical sites.

Advanced Image Stitching Method for Dual-Sensor Inspection.

Efficient image stitching plays a vital role in the Non-Destructive Evaluation (NDE) of infrastructures. An essential challenge in the NDE of infrastructures is precisely visualizing defects within large structures. The existing literature predominantly relies on high-resolution close-distance images to detect surface or subsurface defects. While the automatic detection of all defect types represents a significant advancement, understanding the location and continuity of defects is imperative. It is worth noting that some defects may be too small to capture from a considerable distance. Consequently, multiple image sequences are captured and processed using image stitching techniques. Additionally, visible and infrared data fusion strategies prove essential for acquiring comprehensive information to detect defects across vast structures. Hence, there is a need for an effective image stitching method appropriate for infrared and visible images of structures and industrial assets, facilitating enhanced visualization and automated inspection for structural maintenance. This paper proposes an advanced image stitching method appropriate for dual-sensor inspections. The proposed image stitching technique employs self-supervised feature detection to enhance the quality and quantity of feature detection. Subsequently, a graph neural network is employed for robust feature matching. Ultimately, the proposed method results in image stitching that effectively eliminates perspective distortion in both infrared and visible images, a prerequisite for subsequent multi-modal fusion strategies. Our results substantially enhance the visualization capabilities for infrastructure inspection. Comparative analysis with popular state-of-the-art methods confirms the effectiveness of the proposed approach.

Initial investigation on ultrasound-guided percutaneous biopsy of lesions in the first hepatic hilum with fusion of ultrasound and multimodal imaging cognitive guidance.

Purpose: This study aims to evaluate the efficacy and safety of ultrasound-guided percutaneous biopsy of the first hepatic hilum lesion, and examine its clinical value of diagnosis and treatment. Methods: We conducted a retrospective study on patients diagnosed with the first hepatic hilum lesions at Fujian Provincial Hospital between February 2015 and October 2022. We selected patients who had lesions in the first hepatic hilum(including a 2cm surrounding area of the left/right hepatic ducts and upper-middle segment of the common bile duct) and the liver periphery(in the peripheral area of the liver, outside of the above-mentioned first hepatic porta region). These patients underwent percutaneous ultrasound-guided core needle biopsy (PUS-CNB) with cognitive fusion guidance using CT, MRI, or PET-CT. We compared the safety and efficacy of PUS-CNB in the first hepatic hilum and the liver periphery to explore the value of PUS-CNB in optimizing the clinical treatment of the first hepatic hilum lesions. Results: The studied includes 38 cases of the first hepatic hilum cases (18 females; 20 males), 23 presented with mass-forming tumors while the remaining 15 exhibited diffuse infiltrative tumors, with an average diameter of 4.65± 2.51 cm. The percutaneous biopsy procedure, conducted under ultrasound guidance, had an average operation time of 14.55 ± 2.73 minutes, and resulted in a postoperative bleeding volume of approximately 10.79 ± 2.79 ml. The diagnostic success rate was noted to be as high as 92.11% among the participants who underwent percutaneous biopsy of the first hepatic hilum. Procedural complications, such as bleeding, bile leakage, intestinal perforation, infection or needle tract seeding, did not occur during or after the biopsy procedure. Affected by biopsy results, 5 altered their clinical treatment plans accordingly, 24patients received non-surgical treatment, 9 underwent surgical treatment, 5 underwent radiofrequency ablation for the lesions. The study comprised a total of 112 cases for percutaneous biopsy of the liver periphery. The safety and effectiveness of the two biopsy techniques were comparable, with diagnostic success rates of 92.11% VS. 94.34%, respectively (p = 0.61). Conclusion: Cognitive fusion of ultrasound and multi-modal imaging for the first hepatic hilum lesion puncture biopsy is a safe and effective diagnostic procedure, with better diagnostic rate, may improve clinical value of diagnosis and treatment of various diseases.

A multi-modal image fusion workflow incorporating MALDI imaging mass spectrometry and microscopy for the study of small pharmaceutical compounds.

Multi-modal imaging analyses of dosed tissue samples can provide more comprehensive insight into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multi-modal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry, but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity co-registration with higher resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high spatial resolution microscopy image. As a proof of concept, our multi-modal workflow was applied to brain tissue extracted from a Sprague Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models including linear regression, partial least squares regression (PLS), random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 µm spatial resolutions, a significant improvement compared to the original images acquired at 25 µm spatial resolution. The predicted mass spectrometry images were then co-registered with an H&E image and IHC fluorescence image of the µ-opioid receptor to assess co-localization of corynantheidine with brain cells. Our study also provides insight into the different evaluation parameters to consider when utilizing image fusion for biological applications.

Novel features of degenerative retinoschisis identified using ultra-widefield multicolor channels: A review of 139 eyes.

BACKGROUND/OBJECTIVE: To utilize ultra-widefield multimodal imaging (Optos PLC) to describe novel findings in degenerative retinoschisis. METHODS: This retrospective, non-comparative case series of degenerative retinoschisis received a waiver of consent from Advarra IRB, Protocol 00066379. Initial ultra-widefield pseudocolour, colour-separated, autofluorescence, and peripheral OCT imaging were analysed for characterizing features. RESULTS: In total, 139 eyes were included. A hyporeflective reticular pattern associated with retinoschisis was seen on pseudocolour images in 39% of cases, but visible in 53% on green-separated images. Fine hyper-reflective foci were observed in 49%. In 27%, retinoschisis was confirmed with OCT. CONCLUSIONS: Ultra-widefield pseudocolour and green-separated images are valuable for the diagnosis and characterization of degenerative retinoschisis. The findings described may prompt the evaluation of subtle retinoschisis with peripheral OCT.

Dual/Multi-Modal Image-Guided Diagnosis and Therapy Based on Luminogens with Aggregation-Induced Emission.

The combination of multiple imaging methods has made an indelible contribution to the diagnosis, surgical navigation, treatment, and prognostic evaluation of various diseases. Due to the unique advantages of luminogens with aggregation-induced emission (AIE), their progress has been significant in the field of organic fluorescent contrast agents. Herein, this manuscript summarizes the recent advancements in AIE molecules as contrast agents for optical image-based dual/multi-modal imaging. We particularly focus on the exceptional properties of each material and the corresponding application in the diagnosis and treatment of diseases.

Multimodal real-time imaging with laser speckle contrast and fluorescent contrast.

INTRODUCTION: Laser speckle contrast imaging (LSCI) can achieve real-time 2D perfusion maps non-invasively. However, LSCI is still difficult to use in general clinical applications because of movement sensitivity and limitations in blood flow analysis. To overcome this, fluorescence imaging (FI) is combined with LSCI using a light source with a wavelength of 785 nm in near-infrared (NIR) region and validates to visualize real-time blood perfusion. MATERIALS AND METHODS: The system was performed using Intralipid and indocyanine green (ICG) in a flow phantom that has three tubes and controlled the flow rate in 0-150 µl/min range. First, real-time LSCI was monitored and measured the change in speckle contrast by reperfusion. Then, we visualized blood perfusion of a rabbit ear under the non-invasive condition by intravenous injection using a total of five different ICG concentration solutions from 128 µM to 3.22 mM. RESULTS: The combined system achieved the performance of processing laser speckle images at about 37-38 fps, and we simultaneously confirmed the fluorescence of ICG and changes in speckle contrast due to intralipid as a light scatterer. In addition, we obtained real-time contrast variation and fluorescent images occurring in rabbit's blood perfusion. CONCLUSIONS: The aim of this study is to provide a real-time diagnostic imaging system that can be used in general clinical applications. LSCI and FI are combined complementary for observing tissue perfusion using a single NIR light source. The combined system could achieve real-time visualization of blood perfusion non-invasively.

In vivocharacterization of the optical and hemodynamic properties of the human sternocleidomastoid muscle through ultrasound-guided hybrid near-infrared spectroscopies.

Objective.In this paper, we present a detailedin vivocharacterization of the optical and hemodynamic properties of the human sternocleidomastoid muscle (SCM), obtained through ultrasound-guided near-infrared time-domain and diffuse correlation spectroscopies.Approach.A total of sixty-five subjects (forty-nine females, sixteen males) among healthy volunteers and thyroid nodule patients have been recruited for the study. Their SCM hemodynamic (oxy-, deoxy- and total hemoglobin concentrations, blood flow, blood oxygen saturation and metabolic rate of oxygen extraction) and optical properties (wavelength dependent absorption and reduced scattering coefficients) have been measured by the use of a novel hybrid device combining in a single unit time-domain near-infrared spectroscopy, diffuse correlation spectroscopy and simultaneous ultrasound imaging.Main results.We provide detailed tables of the results related to SCM baseline (i.e. muscle at rest) properties, and reveal significant differences on the measured parameters due to variables such as side of the neck, sex, age, body mass index, depth and thickness of the muscle, allowing future clinical studies to take into account such dependencies.Significance.The non-invasive monitoring of the hemodynamics and metabolism of the sternocleidomastoid muscle during respiration became a topic of increased interest partially due to the increased use of mechanical ventilation during the COVID-19 pandemic. Near-infrared diffuse optical spectroscopies were proposed as potential practical monitors of increased recruitment of SCM during respiratory distress. They can provide clinically relevant information on the degree of the patient's respiratory effort that is needed to maintain an optimal minute ventilation, with potential clinical application ranging from evaluating chronic pulmonary diseases to more acute settings, such as acute respiratory failure, or to determine the readiness to wean from invasive mechanical ventilation.

Characterizing close-focus lenses for microendoscopy.

Microendoscopes are commonly used in small lumens in the body, for which a focus near to the distal tip and ability to operate in an aqueous environment are paramount for navigation and disease detection. Commercially available distal optic systems below 1mm in diameter are severely limited, and custom micro lenses are generally very expensive. Gradient index of refraction (GRIN) singlets are available in small diameters but have limited optical performance adjustability. Three-dimensional (3D) printed monolithic optical systems are an emerging option that may be suitable for enabling high performance, close-focus imaging. In this manuscript, we compared the optical performance of three custom distal optic systems; a custom-pitch GRIN singlet, 3D-printed monolithic doublet, and 3D-printed monolithic triplet, with a nominal working distance (WD) of 1.5mm, 0.5mm and 0.4mm in 0.9% saline. These short WDs are ideal for microendoscopy in collapsed or flushed lumens such as pancreatic duct or fallopian tube. The GRIN singlet had performance limited only by the fiber bundle relay over 0.9mm to 1.6 mm depth of field (DOF). The 3D printed doublet was able to achieve a comparable DOF of 0.71mm, while the 3D printed triplet suffered the most limited DOF of 0.55mm. 3D printing enables flexible design of monolithic multi-element systems with aspheric surfaces of very short WDs and relative ease of integration.

Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review.

Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.

A novel process for H&E, immunofluorescence, and imaging mass cytometry on a single slide with a concise analytics pipeline.

Imaging mass cytometry (IMC) is a powerful spatial technology that utilizes cytometry time of flight to acquire multiplexed image datasets with up to 40 markers, via metal-tagged antibodies. Recent advances in IMC have led to the inclusion of RNAScope probes and multiple new analysis pipelines have led to faster analyses and better results. However, IMC still suffers from lower resolution (1 µm2 pixels) and relatively small regions of interest (ROIs) (<2 mm2 ) compared to other, light-based microscope technologies. Capturing higher-resolution images on serial sections causes great difficulty when attempting to align cells and structures across serial sections, especially when observing smaller cell types and structures. Therefore, we demonstrate the combination of H&E and multiplex immunofluorescence imaging, for much higher resolution of the structural and cellular compartments found throughout the entire tissue section, with the high-dimensionality of IMC for specific ROIs on a single slide. Additionally, we demonstrate a simple and effective open-source cell segmentation and IMC analysis pipeline with previously published and freely available software.

Identifying Alzheimer's disease and mild cognitive impairment with atlas-based multi-modal metrics.

Introduction: Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently. Methods: In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance. Results: The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus. Conclusion: Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.

Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network.

Purpose: The interpretation of image data plays a critical role during acute brain stroke diagnosis, and promptly defining the requirement of a surgical intervention will drastically impact the patient's outcome. However, determining stroke lesions purely from images can be a daunting task. Many studies proposed automatic segmentation methods for brain stroke lesions from medical images in different modalities, though heretofore results do not satisfy the requirements to be clinically reliable. We investigate the segmentation of brain stroke lesions using a geometric deep learning model that takes advantage of the intrinsic interconnected diffusion features in a set of multi-modal inputs consisting of computer tomography (CT) perfusion parameters. Approach: We propose a geometric deep learning model for the segmentation of ischemic stroke brain lesions that employs spline convolutions and unpooling/pooling operators on graphs to excerpt graph-structured features in a fully convolutional network architecture. In addition, we seek to understand the underlying principles governing the different components of our model. Accordingly, we structure the experiments in two parts: an evaluation of different architecture hyperparameters and a comparison with state-of-the-art methods. Results: The ablation study shows that deeper layers obtain a higher Dice coefficient score (DCS) of up to 0.3654. Comparing different pooling and unpooling methods shows that the best performing unpooling method is the proportional approach, yet it often smooths the segmentation border. Unpooling achieves segmentation results more adapted to the lesion boundary corroborated with systematic lower values of Hausdorff distance. The model performs at the level of state-of-the-art models without optimized training methods, such as augmentation or patches, with a DCS of 0.4553±0.0031. Conclusions: We proposed and evaluated an end-to-end trainable fully convolutional graph network architecture using spline convolutional layers for the ischemic stroke lesion prediction. We propose a model that employs graph-based operations to predict acute stroke brain lesions from CT perfusion parameters. Our results prove the feasibility of using geometric deep learning to solve segmentation problems, and our model shows a better performance than other models evaluated. The proposed model achieves improved metric values for the DCS metric, ranging from 8.61% to 69.05%, compared with other models trained under the same conditions. Next, we compare different pooling and unpooling operations in relation to their segmentation results, and we show that the model can produce segmentation outputs that adapt to irregular segmentation boundaries when using simple heuristic unpooling operations.

Triglyceride-Rich Lipoprotein-Mediated Polymer Dots for Multimodal Imaging Interscapular Brown Adipose Tissue Capillaries.

Brown adipose tissues (BATs) have been identified as a promising target of metabolism disorders. [18F]FDG-PET (FDG = fluorodeoxyglucose; PET = positron emission tomography) has been predominantly employed for BAT imaging, but its limitations drive the urgent need for novel functional probes combined with multimodal imaging approaches. It has been reported that polymer dots (Pdots) display rapid BAT imaging without additional cold stimulation. However, the mechanism by which Pdots image BAT remains unclear. Here, we made an intensive study of the imaging mechanism and found that Pdots can bind to triglyceride-rich lipoproteins (TRLs). By virtue of their high affinity to TRLs, Pdots selectively accumulate in capillary endothelial cells (ECs) in interscapular brown adipose tissues (iBATs). Compared to poly(styrene-co-maleic anhydride)cumene terminated (PSMAC)-Pdots with a short half-life and polyethylene glycol (PEG)-Pdots with low lipophilicity, naked-Pdots have good lipophilicity, with a half-life of about 30 min and up to 94% uptake in capillary ECs within 5 min, increasing rapidly after acute cold stimulation. These results suggested that the accumulation changes of Pdots in iBAT can reflect iBAT activity sensitively. Based on this mechanism, we further developed a strategy to detect iBAT activity and quantify the TRL uptake in vivo using multimodal Pdots.

Spatiotemporal tracking of gold nanorods after intranasal administration for brain targeting.

Intranasal administration is becoming increasingly more attractive as a fast delivery route to the brain for therapeutics circumventing the blood-brain barrier (BBB). Gold nanorods (AuNRs) demonstrate unique optical and biological properties compared to other gold nanostructures due to their high aspect ratio. In this study, we investigated for the first time the brain region-specific distribution of AuNRs and their potential as a drug delivery platform for central nervous system (CNS) therapy following intranasal administration to mice using a battery of analytical and imaging techniques. AuNRs were functionalized with a fluorescent dye (Cyanine5, Cy5) or a metal chelator (diethylenetriaminepentaacetic dianhydride, DTPA anhydride) to complex with Indium-111 via a PEG spacer for optical and nuclear imaging, respectively. Direct quantification of gold was achieved by inductively coupled plasma mass spectrometry. Rapid AuNRs uptake in mice brains was observed within 10 min following intranasal administration which gradually reduced over time. This was confirmed by the 3 imaging/analytical techniques. Autoradiography of sagittal brain sections suggested entry to the brain via the olfactory bulb followed by diffusion to other brain regions within 1 h of administration. The presence of AuNR in glioblastoma (GBM) tumors following intranasal administration was also proven which opens doors for AuNRs applications, as nose-to-brain drug delivery carriers, for treatment of a range of CNS diseases.

MLP-Res-Unet：MLPs and residual blocks-based U-shaped network intervertebral disc segmentation of multi-modal MR spine images.

BACKGROUND: Intervertebral disc degeneration (IVD) is now the most prevalent disease in the world; thus, precise intervertebral disc segmentation is essential for the assessment and diagnosis of spinal diseases. Multi-modal magnetic resonance (MR) imaging is more multi-dimensional and thorough than unimodal imaging. However, manual segmentation of multi-modal MRI not only imposes a huge burden on physicians but also has a high error rate. OBJECTIVE: In this study, we propose a new method that can efficiently and accurately segment intervertebral discs from multi-modal MR spine images, providing a reproducible usage scheme for the diagnosis of spinal disorders. METHODS: We suggest a network structure called MLP-Res-Unet that reduces the amount of computational load and the number of parameters while maintaining performance. Our contribution is two-fold. First, a medical image segmentation network that fuses residual blocks and a multilayer perceptron (MLP) is proposed. Secondly, we design a new deep supervised method and pass the features extracted from the encoder to the decoder through the residual path to achieve a new full-scale residual connection. RESULTS: We evaluate the network on the MICCAI-2018 IVD dataset and obtain Dice similarity coefficient equal to 94.77 (%) and Jaccard coefficient equal to 84.74 (%), while we reduce the amount of parameters by a factor of 3.9 and computation by a factor of 2.4 compared to the IVD-Net. CONCLUSION: Experiments show that MLP-Res-Unet improves segmentation performance and creates a simpler model structure while reducing the number of parameters and computation.