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Background Graft preparation techniques for the Adams-Berger distal radioulnar joint (DRUJ) reconstruction vary among surgeons with insufficient evidence to support any specific technique. Questions/Purposes We compared survival with cyclic loading, absolute elongation, elongation rate, and modes of failure of four graft preparation techniques. Methods Fifteen porcine extensor tendons were divided into three equal groups: tendon only; tendon augmented along its full length with nonlocking 2-0 FiberLoop suture spaced at 6 mm intervals; and tendon with suture at 12 mm intervals. Suture only was also tested. Samples were woven through custom radius- and ulna-simulating jigs mounted on a mechanical testing machine. Samples underwent a staircase cyclic loading protocol and were then inspected visually for the mode of failure. Survival with cyclic loading, absolute elongation, and elongation rate was compared. Results Average survival with cyclic loading of suture-augmented tendon was significantly higher than tendon only. All tendon groups had significantly higher survival compared with suture only. Absolute elongation was subject to variability due to initial nonlinear elongation behavior of samples. The elongation rate was significantly lower with suture compared with all tendon groups. Modes of failure included rupture of the tendon and/or suture at the simulated graft-bone interface and elongation of the entire construct without rupture. Conclusions In this biomechanical study, augmentation of porcine tendons with suture spaced at either 6 or 12 mm for DRUJ reconstruction significantly increased survival to a staircase cyclic loading protocol Clinical Relevance For the Adams-Berger reconstruction, tendon grafts augmented along their entire length by nonabsorbable braided suture are biomechanically superior to tendon alone.
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This paper introduces an optimal algorithm for solving the discrete grid-based coverage path planning (CPP) problem. This problem consists in finding a path that covers a given region completely. First, we propose a CPP-solving baseline algorithm based on the iterative deepening depth-first search (ID-DFS) approach. Then, we introduce two branch-and-bound strategies (Loop detection and an Admissible heuristic function) to improve the results of our baseline algorithm. We evaluate the performance of our planner using six types of benchmark grids considered in this study: Coast-like, Random links, Random walk, Simple-shapes, Labyrinth and Wide-Labyrinth grids. We are first to consider these types of grids in the context of CPP. All of them find their practical applications in real-world CPP problems from a variety of fields. The obtained results suggest that the proposed branch-and-bound algorithm solves the problem optimally (i.e., the exact solution is found in each case) orders of magnitude faster than an exhaustive search CPP planner. To the best of our knowledge, no general CPP-solving exact algorithms, apart from an exhaustive search planner, have been proposed in the literature.
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Rehabilitative motor training is currently one of the most widely used approaches to promote moderate recovery following injuries of the central nervous system. Such training is generally applied in the clinical setting, whereas it is not standard in preclinical research. This is a concern as it is becoming increasingly apparent that neuroplasticity enhancing treatments require training or some form of activity as a co-therapy to promote functional recovery. Despite the importance of training and the many open questions regarding its mechanistic consequences, its use in preclinical animal models is rather limited. Here we review approaches, findings and challenges when training is applied in animal models of spinal cord injury, and we suggest recommendations to facilitate the integration of training using an appropriate study design, into pre-clinical studies.