Azulene-Containing Bis(squaraine) Dyes: Design, Synthesis and Aggregation Behaviors.

The relationship among chemical structure, physicochemical property and aggregation behavior of organic functional material is an important research topic. Here, we designed and synthesized three bis(squaraine) dyes BSQ1, BSQ2 and BSQ3 through the combination of two kinds of unsymmetrical azulenyl squaraine monomers. Their physicochemical properties were investigated in both molecular and aggregate states. Generally, BSQ1 displayed different assembly behaviors from BSQ2 and BSQ3. Upon fabrication into nanoparticles, BSQ1 tend to form J-aggregates while BSQ2 and BSQ3 tend to form H-aggregates in aqueous medium. When in the form of thin films, three bis(squaraine) dyes all adopted J-aggregation packing modes while only BSQ1 presented the most significant rearrangement of aggregate structures as well as the improvement in the carrier mobilities upon thermal annealing. Our research highlights the discrepancy of aggregation behaviors originating from the molecular structure and surrounding circumstances, providing guidance for the molecular design and functional applications of squaraines.

Islet autoantibody seroconversion in type-1 diabetes is associated with metagenome-assembled genomes in infant gut microbiomes.

The immune system of some genetically susceptible children can be triggered by certain environmental factors to produce islet autoantibodies (IA) against pancreatic ß cells, which greatly increases their risk for Type-1 diabetes. An environmental factor under active investigation is the gut microbiome due to its important role in immune system education. Here, we study gut metagenomes that are de-novo-assembled in 887 at-risk children in the Environmental Determinants of Diabetes in the Young (TEDDY) project. Our results reveal a small set of core protein families, present in >50% of the subjects, which account for 64% of the sequencing reads. Time-series binning generates 21,536 high-quality metagenome-assembled genomes (MAGs) from 883 species, including 176 species that hitherto have no MAG representation in previous comprehensive human microbiome surveys. IA seroconversion is positively associated with 2373 MAGs and negatively with 1549 MAGs. Comparative genomics analysis identifies lipopolysaccharides biosynthesis in Bacteroides MAGs and sulfate reduction in Anaerostipes MAGs as functional signatures of MAGs with positive IA-association. The functional signatures in the MAGs with negative IA-association include carbohydrate degradation in lactic acid bacteria MAGs and nitrate reduction in Escherichia MAGs. Overall, our results show a distinct set of gut microorganisms associated with IA seroconversion and uncovered the functional genomics signatures of these IA-associated microorganisms.

Deep-learning-aided forward optical coherence tomography endoscope for percutaneous nephrostomy guidance.

Percutaneous renal access is the critical initial step in many medical settings. In order to obtain the best surgical outcome with minimum patient morbidity, an improved method for access to the renal calyx is needed. In our study, we built a forward-view optical coherence tomography (OCT) endoscopic system for percutaneous nephrostomy (PCN) guidance. Porcine kidneys were imaged in our experiment to demonstrate the feasibility of the imaging system. Three tissue types of porcine kidneys (renal cortex, medulla, and calyx) can be clearly distinguished due to the morphological and tissue differences from the OCT endoscopic images. To further improve the guidance efficacy and reduce the learning burden of the clinical doctors, a deep-learning-based computer aided diagnosis platform was developed to automatically classify the OCT images by the renal tissue types. Convolutional neural networks (CNN) were developed with labeled OCT images based on the ResNet34, MobileNetv2 and ResNet50 architectures. Nested cross-validation and testing was used to benchmark the classification performance with uncertainty quantification over 10 kidneys, which demonstrated robust performance over substantial biological variability among kidneys. ResNet50-based CNN models achieved an average classification accuracy of 82.6%±3.0%. The classification precisions were 79%±4% for cortex, 85%±6% for medulla, and 91%±5% for calyx and the classification recalls were 68%±11% for cortex, 91%±4% for medulla, and 89%±3% for calyx. Interpretation of the CNN predictions showed the discriminative characteristics in the OCT images of the three renal tissue types. The results validated the technical feasibility of using this novel imaging platform to automatically recognize the images of renal tissue structures ahead of the PCN needle in PCN surgery.

Effect of Cyclocarya paliurus polysaccharides on streptozotocin-induced diabetic nephropathy in rats.

OBJEVTIVE: To investigate the efficacy of Cyclocarya paliurus (C. paliurus) polysaccharides on stre- ptozotocin-induced diabetic nephropathy in rats. METHODS: Rats were divided into 6 groups, including group of normal control, group of diabetic control, group of metformin treatment, low-dose group of C. paliurus polysaccharides treatment, middle-dose group of C. paliurus polysaccharides treatment and high-dose group of C. paliurus polysaccharides treatment. Histological analysis of kidney was analyzed using hematoxilin and eosin. Levels of blood glucose, creatinine, urea, uric acid were determined by spectrophotometry. Anti-oxidative enzymes were measured by real-time polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA). Advanced glycation end products (AGEs) and transforming growth factor-ß1 (TGF-ß1) level was measured by ELISA. RESULTS: Abnormal changes were observed in the group of diabetic control characterized by atrophy of the renal glomeruli with hypercellularity, congestion of glomerular tufts, dilation of the renal spaces, and degeneration of renal tubule. Compared with that of normal group, blood glucose, creatinine, urea, uric acid level was significantly increased in the group of diabetic control. Superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase level was significantly decreased, but AGEs and TGF-ß1 level was significantly increased. By contrast, administration of C. paliurus polysaccharides and metformin could reverse the above-mentioned results of the group of diabetic control, especially in the high-dose group of C. paliurus polysaccharides. CONCLUSION: Our findings suggest that C. paliurus polysaccharides may play a protecting role for nephropathy of diabetic rats by lowering glucose, creatinine, urea, uric acid level, enhancing the antioxidative ability, and reducing AGEs and TGF-ß1 expression.

Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks.

Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.