We investigated whether blockade of the CD47 signaling pathway could reduce ischemia‐reperfusion injury (IRI) of renal allografts donated after cardiac death (DCD) in a porcine animal model of transplantation. Renal allografts were subjected to 30 minutes of warm ischemia, 3.5 hours of cold ischemia, and then perfused with a humanized anti‐CD47 monoclonal antibody (CD47mAb) in the treatment group or HTK solution in the control group (n = 4/group). The animals were euthanized five days after transplantation. At the time of reperfusion, indocyanine green‐based in vivo imaging showed that CD47mAb‐treated organs had greater and more uniform reperfusion. On post‐transplant days 3‐5, the treatment group had lower values compared to the control for creatinine and blood urea nitrogen. Histological examination of allograft tissues showed a significant decrease of acute tubular injury in the CD47mAb‐treated group compared to control. Compared to the control group, CD47mAb treatment significantly decreased genes expression related to oxidative stress (sod‐1, gpx‐1, and txn), the inflammatory response (il‐2, il‐6, inf‐g, and tgf‐b), as well as reduced protein levels of BAX, Caspase‐3, MMP2, and MMP9. These data demonstrate that CD47mAb blockade decreases IRI and subsequent tissue injury in DCD renal allografts in a large animal transplant model.

Registration is an important problem in computerassisted orthopedic surgery (CAOS). As one typical example, the pre-operative space where the patient-specific surgical plan is usually made needs to be accurately aligned with the patient space where the surgical procedures are conducted. The registration task still attracts a lot of research efforts, partially because establishing the point correspondences between two point sets (PSs) under noise, outliers and partial overlapping is a non-trivial problem. More specifically, in CAOS, (a) the intraoperative points only cover a small partial region of the preoperative whole model; (b) the acquired intra-operative points are usually noisy and contains outliers. To facilitate the related researchers to quickly know both the classical and state-of-theart rigid point set registration (RPSR) methods, we present a concise review about related rigid registration methods in CAOS in this paper. The contributions of this paper include: (1) we review the RPSR methods that are suitable for or very related to the CAOS application; (2) the surveyed registration algorithms’ advantages and disadvantages are discussed and compared; (3) potential future research directions are also discussed.

Point set registration (PSR) is an essential problem in the field of surgical navigation and augmented reality (AR). In surgical navigation, the aim of registration is mapping the pre-operative space to the intra-operative space. This article introduces a reliable hybrid mixture model, in which the reliability of the normal vectors in the generalized point set (GPS) is examined and exploited. The motivation of considering the reliability of orientation information is that normal vectors cannot be estimated or measured accurately in the clinic. The point set (PS) is divided into two subsets according to the reliability of normal vectors. PSR is cast into the maximum likelihood estimation (MLE) problem. The expectation maximization (EM) framework is used to solve the MLE problem. In the E-step, the posterior probabilities between points in two PSs are computed. In the M-step, the transformation matrix and model components are updated by optimizing the objective function. We have demonstrated through extensive experiments on the human femur bone PS that the proposed algorithm outperforms the state-of-the-art ones in terms of accuracy, robustness, and convergence speed.

An accurate indoor positioning system (IPS) for the public has become an essential function with the fast development of smart city-related applications. The performance of the current IPS is limited by the complex indoor environments, the poor performance of smartphone built-in sensors, and time-varying measurement errors of different location sources. This paper introduces a hybrid indoor positioning system (H-IPS) that combines acoustic ranging, Wi-Fi fingerprinting, and low-cost sensors. This system is designed specifically for large-scale indoor environments with non-line-of-sight (NLOS) conditions. To improve the accuracy in estimating pedestrian motion trajectory, a data and model dual-driven (DMDD) model is proposed to integrate the inertial navigation system (INS) mechanization and the deep learning-based speed estimator. Additionally, a double-weighted K-nearest neighbor matching algorithm enhanced the accuracy of Wi-Fi fingerprinting and scene recognition. The detected scene results were then utilized for NLOS detection and estimation of acoustic ranging results. Finally, an adaptive unscented Kalman filter (AUKF) was developed to provide universal positioning performance, which further improved by the Wi-Fi accuracy indicator and acoustic drift estimator. The experimental results demonstrate that the presented H-IPS achieves precise positioning under NLOS scenes, with meter-level accuracy attainable within the coverage range of acoustic signals.