Anisotropic properties of human tibial cortical bone as measured by nanoindentation

The purpose of this study was to investigate the effects of elastic anisotropy on nanoindentation measurements in human tibial cortical bone. Nanoindentation was conducted in 12 different directions in three principal planes for both osteonic and interstitial lamellae. The experimental indentation modulus was found to vary with indentation direction and showed obvious anisotropy (oneway analysis of variance test, P < 0.0001). Because experimental indentation modulus in a specific direction is determined by all of the elastic constants of cortical bone, a complex theoretical model is required to analyze the experimental results. A recently developed analysis of indentation for the properties of anisotropic materials was used to quantitatively predict indentation modulus by using the stiffness matrix of human tibial cortical bone, which was obtained from previous ultrasound studies. After allowing for the effects of specimen preparation (dehydrated specimens in nanoindentation tests vs. moist specimens in ultrasound tests) and the structural properties of bone (different microcomponents with different mechanical properties), there were no statistically significant differences between the corrected experimental indentation modulus (Mexp) values and corresponding predicted indentation modulus (Mpre) values (two-tailed unpaired t-test, P < 0.5). The variation of Mpre values was found to exhibit the same trends as the corrected Mexp data. These results show that the effects of anisotropy on nanoindentation measurements can be quantitatively evaluated. © 2002 Orthopaedic Research Society. Published by Elsevier Science Ltd. All rights reserved.

The primary amine metabolite of sibutramine stimulates lipolysis in adipocytes isolated from lean and obese mice and in isolated human adipocytes

Sibutramine is a satiety-inducing serotonin-noradrenaline reuptake inhibitor that acts predominantly via its primary and secondary metabolites. This study investigates the possibility that sibutramine and/or its metabolites could act directly on white adipose tissue to increase lipolysis. Adipocytes were isolated by a collagenase digestion procedure from homozygous lean (+/+) and obese-diabetic ob/ob mice, and from lean nondiabetic human subjects. The lipolytic activity of adipocyte preparations was measured by the determination of glycerol release over a 2-hour incubation period. The primary amine metabolite of sibutramine M2, caused a concentration-dependent stimulation of glycerol release by murine lean and obese adipocytes (maximum increase by 157 ± 22 and 245 ± 1696, respectively, p < 0.05). Neither sibutramine nor its secondary amine metabolite M1 had any effect on lipolytic activity. Preliminary studies indicated that M2-induced lipolysis was mediated via a beta-adrenergic action. The non-selective beta-adrenoceptor antagonist propranolol (10-6M) strongly inhibited M2-stimulated lipolysis in lean and obese murine adipocytes. M2 similarly increased lipolysis by isolated human omental and subcutaneous adipocytes (maximum increase by 194 ± 33 and 136 ± 4%, respectively, p < 0.05) with EC50 values of 12 nM and 3 nM, respectively. These results indicate that the sibutramine metabolite M2 can act directly on murine and human adipose tissue to increase lipolysis via a pathway involving beta-adrenoceptors. © Georg Thieme Verlag KG Stuttgart.

Modulating membership grades to gain consensus for fuzzy set uncertainty values in a clinical decision support system

This paper deals with a very important issue in any knowledge engineering discipline: the accurate representation and modelling of real life data and its processing by human experts. The work is applied to the GRiST Mental Health Risk Screening Tool for assessing risks associated with mental-health problems. The complexity of risk data and the wide variations in clinicians' expert opinions make it difficult to elicit representations of uncertainty that are an accurate and meaningful consensus. It requires integrating each expert's estimation of a continuous distribution of uncertainty across a range of values. This paper describes an algorithm that generates a consensual distribution at the same time as measuring the consistency of inputs. Hence it provides a measure of the confidence in the particular data item's risk contribution at the input stage and can help give an indication of the quality of subsequent risk predictions. © 2010 IEEE.