Computer simulating a trial of a load-bearing implant: example of an intramedullary prosthesis

Computational modelling is becoming ever more important for obtaining regulatory approval for new medical devices. An accepted approach is to infer performance in a population from an analysis conducted for an idealised or ‘average’ patient; we present here a method for predicting the performance of an orthopaedic implant when released into a population—effectively simulating a clinical trial. Specifically we hypothesise that an analysis based on a method for predicting the performance in a population will lead to different conclusions than an analysis based on an idealised or ‘average’ patient. To test this hypothesis we use a finite element model of an intramedullary implant in a bone whose size and remodelling activity is different for each individual in the population. We compare the performance of a low Young’s modulus implant (View the MathML source) to one with a higher Young’s modulus (200 GPa). Cyclic loading is applied and failure is assumed when the migration of the implant relative to the bone exceeds a threshold magnitude. The analysis for an idealised of ‘average’ patient predicts that the lower modulus device survives longer whereas the analysis simulating a clinical trial predicts no statistically-significant tendency (p=0.77) for the low modulus device to perform better. It is concluded that population-based simulations of implant performance–simulating a clinical trial–present a very valuable opportunity for more realistic computational pre-clinical testing of medical devices.

Sedative load of medications prescribed for older people with dementia in care homes

The objective of this study was to determine the sedative load and use of sedative and psychotropic medications among older people with dementia living in (residential) care homes.

Electric vehicles and displaced gaseous emission

Electric vehicles (EV) do not emit tailpipe exhaust fumes in the same manner as internal combustion engine vehicles. Optimal benefits can only be achieved, if EVS are deployed effectively, so that the tailpipe emissions are not substituted by additional emissions in the electricity sector. This paper examines the potential contributions that Plug in Hybrid Electric Vehicles can make in reducing carbon dioxide. The paper presents the results of the generation expansion model for Northern Ireland and the Republic of Ireland built using the dynamic programming based long term generation expansion planning tool called the Wien Automatic System Planning IV tool. The model optimizes power dispatch using hourly electricity demand curves for each year up to 2020, while incorporating generator characteristics and certain operational requirements such as energy not served and loss of load probability while satisfying constraints on environmental emissions, fuel availability and generator operational and maintenance costs. In order to simulate the effect of PHEV, two distinct charging scenarios are applied based on a peak tariff and an off peak tariff. The importance and influence of the charging regime on the amount of energy used and gaseous emissions displaced is determined and discussed.

Scaling in the time-dependent failure of a fiber bundle with local load sharing

We study the scaling behaviors of a time-dependent fiber-bundle model with local load sharing. Upon approaching the complete failure of the bundle, the breaking rate of fibers diverges according to r(t)proportional to(T-f-t)(-xi) where T-f is the lifetime of the bundle and xi approximate to 1.0 is a universal scaling exponent. The average lifetime of the bundle [T-f] scales with the system size as N-delta, where delta depends on the distribution of individual fiber as well as the breakdown rule. [S1063-651X(99)13902-3].

Failure of fiber bundles with local load sharing

We develop a recursion-relation approach for calculating the failure probabilities of a fiber bundle with local load sharing. This recursion relation is exact, so it provides a way to test the validity of the various approximate methods. Applying the exact calculation to uniform and Weibull threshold distributions, we find that the most probable failure load coincides with the average strength as the size of the system N --> infinity.

BURST-SIZE DISTRIBUTION IN FIBER-BUNDLES WITH LOCAL LOAD-SHARING

A critical load x(c) is introduced into the fiber-bundle model with local load-sharing. The critical load is defined as the average load per fiber that causes the final complete failure. It is shown that x(c) --> 0 when the size of the system N --> infinity. A power law for the burst-size distribution, D(DELTA) is-proportional-to DELTA(-xi) is approximately correct. The exponent xi is not universal, since it depends on the strength distribution as well as the size of the system.

Genetic variants influencing human aging from late-onset Alzheimer's disease (LOAD) genome-wide association studies (GWAS)

Genetics plays a crucial role in human aging with up to 30% of those living to the mid-80s being determined by genetic variation. Survival to older ages likely entails an even greater genetic contribution. There is increasing evidence that genes implicated in age-related diseases, such as cancer and neuronal disease, play a role in affecting human life span. We have selected the 10 most promising late-onset Alzheimer's disease (LOAD) susceptibility genes identified through several recent large genome-wide association studies (GWAS). These 10 LOAD genes (APOE, CLU, PICALM, CR1, BIN1, ABCA7, MS4A6A, CD33, CD2AP, and EPHA1) have been tested for association with human aging in our dataset (1385 samples with documented age at death [AAD], age range: 58-108 years; mean age at death: 80.2) using the most significant single nucleotide polymorphisms (SNPs) found in the previous studies. Apart from the APOE locus (rs2075650) which showed compelling evidence of association with risk on human life span (p = 5.27 × 10(-4)), none of the other LOAD gene loci demonstrated significant evidence of association. In addition to examining the known LOAD genes, we carried out analyses using age at death as a quantitative trait. No genome-wide significant SNPs were discovered. Increasing sample size and statistical power will be imperative to detect genuine aging-associated variants in the future. In this report, we also discuss issues relating to the analysis of genome-wide association studies data from different centers and the bioinformatic approach required to distinguish spurious genome-wide significant signals from real SNP associations.