Chiral assay methods for lifibrol and metabolites in plasma and the observation of unidirectional chiral inversion following administration of the enantiomers to dogs

Lifibrol, a new drug for the treatment of hypercholesterolemia, contains a stereogenic center bearing a secondary alcohol group. A normal-phase achiral–chiral HPLC separation of the enantiomers of lifibrol and two of its metabolites was developed and validated for quantitation in dog plasma. A silica and a Chiralcel OD-H column were operated in series and all six enantiomeric components and internal standard were directly separated. An initial solid-phase extraction (phenyl) clean-up step and a column-switching step to eliminate late-eluting compounds were also utilized. The solid-phase extraction step was automated using a robotic system. Assay development, validation, and application of the method to a bioavailability study of the racemate and enantiomers of lifibrol in dogs are described. The lower limit of quantitation was 0.0125 μg/ml for each enantiomer of lifibrol using 200 μl of dog plasma with UV detection (255 nm). In dog plasma following oral or intravenous administration of the racemate, the (R)/(S) ratio of the enantiomers of lifibrol was greater than one and increased with time. Following administration of the individual enantiomers, chiral inversion of the (S)-enantiomer but not the (R)-enantiomer was observed. © 1994 Wiley-Liss, Inc.     

An improved OpenSim gait model with multiple degrees of freedom knee joint and knee ligaments

Musculoskeletal models are widely used to investigate joint kinematics and predict muscle force during gait. However, the knee is usually simplified as a one degree of freedom joint and knee ligaments are neglected. The aim of this study was to develop an OpenSim gait model with enhanced knee structures. The knee joint in this study included three rotations and three translations. The three knee rotations and mediolateral translation were independent, with proximodistal and anteroposterior translations occurring as a function of knee flexion/extension. Ten elastic elements described the geometrical and mechanical properties of the anterior and posterior cruciate ligaments (ACL and PCL), and the medial and lateral collateral ligaments (MCL and LCL). The three independent knee rotations were evaluated using OpenSim to observe ligament function. The results showed that the anterior and posterior bundles of ACL and PCL (aACL, pACL and aPCL, pPCL) intersected during knee flexion. The aACL and pACL mainly provided force during knee flexion and adduction, respectively. The aPCL was slack throughout the range of three knee rotations; however, the pPCL was utilised for knee abduction and internal rotation. The LCL was employed for knee adduction and rotation, but was slack beyond 20° of knee flexion. The MCL bundles were mainly used during knee adduction and external rotation. All these results suggest that the functions of knee ligaments in this model approximated the behaviour of the physical knee and the enhanced knee structures can improve the ability to investigate knee joint biomechanics during various gait activities.     

A kinematic method for computing the motion of the body centre-of-mass (CoM) during walking: a Bayesian approach

The gait pattern of a particular patient can be altered in a large set of pathologies. Tracking the body centre-of-mass (CoM) during the gait allows a quantitative evaluation of these diseases at comparing the gait with normal patterns. A correct estimation of this variable is still an open question because of its non-linearity and inaccurate location. This paper presents a novel strategy for tracking the CoM, using a biomechanical gait model whose parameters are determined by a Bayesian strategy. A particle filter is herein implemented for predicting the model parameters from a set of markers located at the sacral zone. The present approach is compared with other conventional tracking methods and decreases the calculated root mean squared error in about a 56% in the x-axis and 59% in the y-axis.     

Inertial motion capture in conjunction with an artificial neural network can differentiate the gait patterns of hemiparetic stroke patients compared with able-bodied counterparts

Clinical gait analysis has proven to reduce uncertainties in selecting the appropriate quantity and type of treatment for patients with neuromuscular disorders. However, gait analysis as a clinical tool is under-utilised due to the limitations and cost of acquiring and managing data. To overcome these obstacles, inertial motion capture (IMC) recently emerged to counter the limitations attributed to other methods. This paper investigates the use of IMC for training and testing a back-propagation artificial neural network (ANN) for the purpose of distinguishing between hemiparetic stroke and able-bodied ambulation. Routine gait analysis was performed on 30 able-bodied control subjects and 28 hemiparetic stroke patients using an IMC system. An ANN was optimised to classify the two groups, achieving a repeatable network accuracy of 99.4%. It is concluded that an IMC system and appropriate computer methods may be useful for the planning and monitoring of gait rehabilitation therapy of stroke victims.     

Verification of accuracy and validity of gait phase detection system using motion sensors for applying walking assistive FES

In this study, we have analysed heel strike (HS) and toe off (TO) of normal individuals and hemiplegic patients, taking advantage of output curves acquired from various sensors, and verified the validity of sensor detection methods and their effectiveness when they were used for hemiplegic gaits. Gait phase detections using three different motion sensors were valid, since they all had reliabilities more than 95%, when compared with foot velocity algorithm. Results showed that the tilt sensor and the gyrosensor could detect gait phase more accurately in normal individuals. Vertical acceleration could detect HS most accurately in hemiplegic patient group A. The gyrosensor could detect HS and TO most accurately in hemiplegic patient groups A and B. The detection of TO from all sensor signals was valid in both the patient groups A and B. However, the vertical acceleration detected HS validly in patient group A and the gyrosensor detected HS validly in patient group B.     

Dynamic Modeling and Experiment of a Fish Robot with a Flexible Tail Fin

This paper presents the dynamic modeling of a flexible tail for a robotic fish. For this purpose firstly, the flexible tail was simplified as a slewing beam actuated by a driving moment. The governing equation of the flexible tail was derived by using the Euler-Bernoulli theory. In this equation, the resistive forces were estimated as a term analogous to viscous damping. Then, the modal analysis method was applied in order to derive an analytical solution of the governing equation, by which the relationship between the driving moment and the lateral movement of the flexible tail was described. Finally, simulations and experiments were carried out and the results were compared to verify the accuracy of the dynamic model. It was proved that the dynamic model of a fish robot with a flexible tail fin well explains the real behavior of robotic fish in underwater environment.