Standardization of serum cholesterol assays by use of serum calibrators and direct addition of Liebermann-Burchard reagent

Serum cholesterol concentrations of subjects in epidemiological studies were measured after direct addition of Liebermann-Burchard reagent; results were calibrated with human serum pools assayed according to Abell et al. (J. Biol. Chem. 195:357-366, 1952). Accuracy and precision were monitored for six years by analysis of internal-control pools and blind external-control pools. For various internal-control pools, the imprecision (CV) of the long-term averages of run means ranged from 0.5 to 0.9%. The within-run CV for internal control and patients' sera was about 1%. For blind control sera with different concentrations (provided by the Centers for Disease Control, Atlanta, GA, over the same period), the average difference per three-month period between the values found and the target values was usually between -0.5% and 0.7% for medium-concentration pools and between -2% and 2% for low- and high-concentration pools (extreme values: -2.4% and 2.5%). The CV per three-month period ranged from 0.6 to 2.7%. Sera from subjects on diets of high or low linoleic acid content were analyzed to study the effect of the fatty acid portion of serum cholesterol esters; the differences between values obtained with the comparison method and the direct method was insignificant on both diets. We conclude that the use of serum calibrators eliminates the bias inherent in the direct method

Evaluation of EMG, force and joystick as control interfaces for active arm supports

Background:\ud The performance capabilities and limitations of control interfaces for the operation of active movement-assistive devices remain unclear. Selecting an optimal interface for an application requires a thorough understanding of the performance of multiple control interfaces. \ud \ud Methods:\ud In this study the performance of EMG-, force- and joystick-based control interfaces were assessed in healthy volunteers with a screen-based one-dimensional position-tracking task. The participants had to track a target that was moving according to a multisine signal with a bandwidth of 3 Hz. The velocity of the cursor was proportional to the interface signal. The performance of the control interfaces were evaluated in terms of tracking error, gain margin crossover frequency, information transmission rate and effort. \ud \ud Results:\ud None of the evaluated interfaces was superior in all four performance descriptors. The EMG-based interface was superior in tracking error and gain margin crossover frequency compared to the force- and the joystick-based interfaces. The force-based interface provided higher information transmission rate and lower effort than the EMG-based interface. The joystick-based interface did not present any significant difference with the force-based interface for any of the four performance descriptors. We found that significant differences in terms of tracking error and information transmission rate were present beyond 0.9 and 1.4 Hz respectively. \ud \ud Conclusions:\ud Despite the fact that the EMG-based interface is far from the natural way of interacting with the environment, while the force-based interface is closer, the EMG-based interface presented very similar and for some descriptors even a better performance than the force-based interface for frequencies below 1.4 Hz. The classical joystick presented a similar performance to the force-based interface and holds the advantage of being a well established interface for the control of many assistive devices. From these findings we concluded that all the control interfaces considered in this study can be regarded as a candidate interface for the control of an active arm support

Domweg gelukkig in de Dapperstraat'. Juryrapport van de Literaire Prijs van de provincie Gelderland 2005

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Validity of total and segmental impedance measurements for prediction of body composition across ethnic population groups

Methods: Anthropometric parameters were measured in addition to impedance (100 kHz) of the total body, arms and legs. Impedance indexes were calculated as height2/impedance. Arm length (span) and leg length (sitting height), wrist and knee width were measured from which body build indices were calculated. Total body water (TBW) was measured using deuterium oxide dilution. Extra cellular water (ECW) was measured using bromide dilution. Body fat percentage was determined using a chemical four-compartment model. Results: The bias of TBW predicted from total body impedance index (bias: measured minus predicted TBW) was different among the three ethnic groups, TBW being significantly underestimated in Indians compared to Chinese and Malays. This bias was found to be dependent on body water distribution (ECW/TBW) and parameters of body build, mainly relative (to height) arm length. After correcting for differences in body water distribution and body build parameters the differences in bias across the ethnic groups disappeared. The impedance index using total body impedance was better correlated with TBW than the impedance index of arm or leg impedance, even after corrections for body build parameters. Conclusions: The study shows that ethnic-specific bias of impedance-based prediction formulas for body composition is due mainly to differences in body build among the ethnic groups. This means that the use of 'general' prediction equations across different (ethnic) population groups without prior testing of their validity should be avoided. Total body impedance has higher predictive value than segmental impedance