Anti-MME monoclonal antibody, clone NFN-89 [PerCP/Cy5.5®]

Background: Medial meniscal extrusion (MME) is defined as displacement of the meniscus that extends beyond the tibial margin. Knee varus malalignment increases MME. Purpose/Hypothesis: The purpose of this study was to quantify MME before and after medial opening wedge high tibial osteotomy (HTO) and to correlate the reduction of MME with clinical outcomes and return to activity. It was hypothesized that MME would decrease after HTO and that patients with lower MME after surgery would have improved clinical outcomes and return to activity at short-term follow-up. Study Design: Case series; Level of evidence, 4. Methods: This study included 66 patients who underwent HTO to correct the anatomic axis with a minimum follow-up of 2 years. MME was measured using magnetic resonance imaging preoperatively and 6 weeks after surgery (study protocol). Patients were assessed preoperatively and postoperatively with the Knee injury and Osteoarthritis Outcome Score (KOOS), visual analog scale (VAS) score for pain, and Tegner score. Results: The mean +/- SD preoperative and postoperative MME values were 3.9 +/- 0.6 mm and 0.9 +/- 0.5 mm, respectively. At 2 years after surgery, KOOS, pain VAS, and Tegner scores were higher than those found preoperatively (P < .001). Patients with less than 1.5 mm of MME after surgery had better clinical outcomes and return to activity compared with patients who had MME of 1.5 mm or more (P < .05). Conclusion: Medial opening wedge HTO decreased MME after 6 weeks and improved clinical outcomes and return to activity at a minimum 2-year follow-up. Additionally, patients with postoperative MME of less than 1.5 mm had better clinical outcomes and return to activity compared with patients who had postoperative MME of 1.5 mm or more.

In order to improve the detection probability in the environment of low signal-to-noise ratio (SNR), and solving the problem of complex threshold derivation in traditional spectrum sensing technology, the improved spectrum sensing method is proposed in this paper. Firstly, the signal received by each secondary user is decomposed and recombined (DAR). Then the correlation coefficient (CC) based on the sampling signal matrix is extracted as the decision statistic, which reduces the influence of the noise uncertainty. Finally, the K-means clustering algorithm is used to class these decision statistics, accuracy greatly. In order to facilitate expression, the proposed algorithm is abbreviated as DARCCK. Through experimental simulation, the DARCCK algorithm exhibits better detection performance than the energy detection (ED), the maximum and minimum eigenvalue (MME) algorithm and the difference between the maximum and minimum eigenvalues (DMM) in the communication environment with low SNR.