Safety and performance of Hydros and Hydros-TA for knee osteoarthritis: a prospective, multicenter, randomized, double-blind feasibility trial.

BACKGROUND: Studies have evaluated the concomitant use of hyaluronan (HA) with steroids, anti-inflammatory drugs and analgesic agents in an attempt to magnify the extent and duration of pain relief due to knee osteoarthritis. To date there has not been an intra-articular combination therapy available for relief of knee osteoarthritis symptoms--one that combines the fast acting onset of symptom relief provided by a corticosteroid with the long-lasting symptom relief provided by HA in a single injection. The objective of this study was to evaluate the safety and preliminary performance of two new HA formulations, Hydros (hyaluronan-based hydrogel suspended in hyaluronan solution) and Hydros-TA (HA plus 10 mg of triamcinolone acetonide [TA]) in subjects with knee osteoarthritis. METHODS: We conducted a Phase 2 prospective, multicenter, randomized, double-blind feasibility trial. Participants (n = 98; mean age 60 years) with knee osteoarthritis (Kellgren-Lawrence grade 2 or 3) were randomized to three treatment groups: Hydros, Hydros-TA, and Synvisc-One® (hylan G-F 20). Participants received one 6 ml intra-articular injection in the treatment knee and were evaluated at 2, 6, 13, and 26 weeks post-treatment. Safety was assessed from adverse events at all study visits. The primary efficacy outcome was the change from baseline on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) A (Pain) score for the treatment knee. RESULTS: Adverse events were similar across treatment groups with the most common being arthralgia, joint swelling, back pain, and joint stiffness. Arthralgia was reported 5 times with Synvisc-One, 4 with Hydros, and 1 with Hydros-TA. Each group demonstrated a reduction in the WOMAC A (Pain) score over 26 weeks: [least-square mean (standard error)] 30.5 (5.1) mm for Hydros; 34.4 (4.7) mm for Hydros-TA; 28.0 (5.4) mm for Synvisc-One and an observed improvement of 2.5 mm (p = 0.64) and 6.4 mm (p = 0.24) over Synvisc-One for Hydros and Hydros-TA, respectively. CONCLUSIONS: A single injection of Hydros or Hydros-TA was well-tolerated and relieved pain associated with knee osteoarthritis over 26 weeks. Data indicate that Hydros-TA had a more rapid pain relief compared to Hydros alone. A Phase 3 trial is underway to confirm these preliminary results. TRIAL REGISTRATION NUMBER: NCT01134406.

MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements.

In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC.

MCMC-based particle filtering for tracking a variable number of interacting targets.

We describe a particle filter that effectively deals with interacting targets--targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.

Binding balls: fast detection of binding sites using a property of spherical Fourier transform.

The functional prediction of proteins is one of the most challenging problems in modern biology. An established computational technique involves the identification of three-dimensional local similarities in proteins. In this article, we present a novel method to quickly identify promising binding sites. Our aim is to efficiently detect putative binding sites without explicitly aligning them. Using the theory of Spherical Harmonics, a candidate binding site is modeled as a Binding Ball. The Binding Ball signature, offered by the Spherical Fourier coefficients, can be efficiently used for a fast detection of putative regions. Our contribution includes the Binding Ball modeling and the definition of a scoring function that does not require aligning candidate regions. Our scoring function can be computed efficiently using a property of Spherical Fourier transform (SFT) that avoids the evaluation of all alignments. Experiments on different ligands show good discrimination power when searching for known binding sites. Moreover, we prove that this method can save up to 40% in time compared with traditional approaches.