No abstract

IntroductionAntineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is relatively rare in children. This article aimed to analyze clinical and renal histology findings and different responses to induction treatment associated with the long-term renal outcomes in children with AAV in a single center.MethodsAll pediatric patients with AAV admitted to Tongji Hospital from January 2002 to January 2021 were included in the study. The demographic, clinical, pathological, laboratory, and treatment data and outcomes were collected and analyzed to identify predictors associated with response to induction treatment and progression to end-stage renal disease (ESRD).ResultsIn total, 48 children with AAV were included in this cohort; 81.25% of them were women, and 91.7% were microscopic polyangiitis (MPA). Kidney involvement was found in 45 patients (93.75%). The most common histopathological subtype was crescentic form in this cohort according to Berden’s classification. In total, 34 patients (70.8%) showed eGFR <60 ml/min/1.73 m2 at the time of diagnosis. Complete and partial remission was achieved in 8 patients (16.7%) and 19 patients (39.6%), respectively, following 6-month induction treatment. Half of the patients eventually progressed to ESRD at a mean time of (13.04 ± 15.83) months after diagnosis. The independent predictors of nonremission following induction treatment and progression to ESRD were baseline eGFR <60 ml/min/1.73 m2 and hypertension at diagnosis. Renal survival significantly decreased over time in patients with renal sclerotic subtypes or those with nonremission following induction treatment by Kaplan–Meier curve estimation.ConclusionsOur study demonstrates that women, MPA, and crescentic subtypes are predominant in pediatric AAV in China. Initial renal failure (eGFR <60 ml/min/1.73 m2), hypertension, sclerotic pathological subtype, and nonremission following induction treatment are predictive of long-term renal outcomes.

The survivability of autonomous underwater vehicles (AUV) in complex missions and dangerous situations is of great significance to ocean resource exploration, hydrological research, maritime rescue, and undersea military. Existing researches on motion control for the AUV mainly focus on its normal operating, but the active self-rescue method in emergency situations is hardly found. As classical control methods are not sufficient enough for complicated self-rescue missions of the AUV, this paper uses the deep reinforcement learning (DRL) algorithm to solve this problem because the DRL algorithm has the advantages in learning and decision making for complex robot control missions. In this paper, the normal motion control of the AUV based on the deep deterministic policy gradient algorithm is explored, including the yaw angle adjustment, yaw angle adjustment extension, trajectory tracking, and normal floating-up control of the AUV. Then, active self-rescue methods are successfully achieved to recover the AUV from emergencies, such as ocean water density decreasing sharply or one fin getting jammed at a random angle. What is more, real environment experiments are successfully conducted on a self-developed platform of the AUV to validate the feasibility of the proposed control methods. The results can effectively improve the survivability of the AUV and can be a reference to submarine survivability technologies.

No abstract