Human schistosomiasis, caused by species, is a major public health problem affecting more than 700 million people in 78 countries, with over 40 mammalian host reservoir species complicating the transmission ecosystem. The primary cause of morbidity is considered to be granulomas induced by fertilized eggs of schistosomes in the liver and intestines. Some host species, like rats (), are naturally intolerant to infection, and do not produce granulomas or pose a threat to transmission, while others, like mice and hamsters, are highly susceptible. The reasons behind these differences are still a mystery. Using inducible nitric oxide synthase knockout (iNOS) Sprague-Dawley rats, we found that inherent high expression levels of iNOS in wild-type (WT) rats play an important role in blocking growth, reproductive organ formation, and egg development in , resulting in production of nonfertilized eggs. Granuloma formation, induced by fertilized eggs in the liver, was considerably exacerbated in the iNOS rats compared with the WT rats. This inhibition by nitric oxide acts by affecting mitochondrial respiration and energy production in the parasite. Our work not only elucidates the innate mechanism that blocks the development and production of fertilized eggs in but also offers insights into a better understanding of host-parasite interactions and drug development strategies against schistosomiasis.
A three-dimensional thermo-elasto-hydro-dynamic model for bidirectional thrust bearings used in pump-turbines taking into account the temperature-viscosity effect as well as thermal-elastic deformations in the pad and runner surface was set up. The finite difference method was employed to solve the thermo-hydro-dynamic model, and the thermal-elastic deformations in the pad and runner were obtained by the finite element software ANSYS11.0. The data transfer between the thermo-hydro-dynamic model and ANSYS11.0 was carried out automatically by an interface program. Laboratory test was performed on the Harbin Electric Machinery 3000 ton thrust bearing test rig. A detailed comparison between the experimental results and numerical predictions showed quite good overall agreement on the oil film thickness, pressure and temperature, which provided an evidence of validation of the three-dimensional thermo-elasto-hydrodynamic model coupled with finite difference method and finite element method developed in this article.
IntroductionThree-dimensional (3D) reconstruction of fracture fragments on hip Computed tomography (CT) may benefit the injury detail evaluation and preoperative planning of the intertrochanteric femoral fracture (IFF). Manually segmentation of bony structures was tedious and time-consuming. The purpose of this study was to propose an artificial intelligence (AI) segmentation tool to achieve semantic segmentation and precise reconstruction of fracture fragments of IFF on hip CTs.Materials and MethodsA total of 50 labeled CT cases were manually segmented with Slicer 4.11.0. The ratio of training, validation and testing of the 50 labeled dataset was 33:10:7. A simplified V-Net architecture was adopted to build the AI tool named as IFFCT for automatic segmentation of fracture fragments. The Dice score, precision and sensitivity were computed to assess the segmentation performance of IFFCT. The 2D masks of 80 unlabeled CTs segmented by AI tool and human was further assessed to validate the segmentation accuracy. The femoral head diameter (FHD) was measured on 3D models to validate the reliability of 3D reconstruction.ResultsThe average Dice score of IFFCT in the local test dataset for “proximal femur”, “fragment” and “distal femur” were 91.62%, 80.42% and 87.05%, respectively. IFFCT showed similar segmentation performance in cross-dataset, and was comparable to that of human expert in human-computer competition with significantly reduced segmentation time (p < 0.01). Significant differences were observed between 2D masks generated from semantic segmentation and conventional threshold-based segmentation (p < 0.01). The average FHD in the automatic segmentation group was 47.5 ± 4.1 mm (41.29∼56.59 mm), and the average FHD in the manual segmentation group was 45.9 ± 6.1 mm (40.34∼64.93 mm). The mean absolute error of FHDs in the two groups were 3.38 mm and 3.52 mm, respectively. No significant differences of FHD measurements were observed between the two groups (p > 0.05). All ICCs were greater than 0.8.ConclusionThe proposed AI segmentation tool could effectively segment the bony structures from IFF CTs with comparable performance of human experts. The 2D masks and 3D models generated from automatic segmentation were effective and reliable, which could benefit the injury detail evaluation and preoperative planning of IFFs.
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