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.
The immune system of some genetically susceptible children can be triggered by certain environmental factors to produce islet autoantibodies (IA) against pancreatic β 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
It is important for underwater wireless sensor networks (UWSNs) to satisfy the diverse monitoring demands in harsh and perilous three-dimensional underwater environments. After the monitoring missions and demands transform, a large number of underwater event coverage holes will appear. Traditional network repair strategies cannot be applied to the ever-changing underwater monitoring missions and the harsh multi-constrained three-dimensional underwater environments. Multiple autonomous underwater vehicles (multi-AUVs) have strong adaptability and flexibility in perilous and harsh three-dimensional underwater environments. First, an underwater event coverage hole (UECH) repair model under various constraints is proposed. Next, a multi-agent event coverage hole repair algorithm (MECHR), which combines multi-agent strategy with diversity archive strategy, is proposed to repair UECHs in UWSNs. The presented algorithm symmetrically completes subtasks through information exchange and interactive operations with other agents. Unlike existing repair strategies, the MECHR algorithm can effectively repair a large number of UECHs resulted by the transformations in underwater monitoring scenes and demands. The MECHR algorithm can adapt to a wide range of harsh scenes and multi-constrained three-dimensional underwater environments. Eventually, the effect of the MECHR algorithm is verified through underwater repair simulation experiments, which can adapt to the constantly changing three-dimensional underwater monitoring environments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.