High performance additive manufactured scaffolds for bone tissue engineering application

This study demonstrates the feasibility of additive manufactured poly(3-caprolactone)/silanized tricalcium phosphate (PCL/TCP(Si)) scaffolds coated with carbonated hydroxyapatite (CHA)-gelatin composite for bone tissue engineering. In order to reinforce PCL/TCP scaffolds to match the mechanical properties of cancellous bone, TCP has been modified with 3-glycidoxypropyl trimethoxysilane (GPTMS) and incorporated into PCL to synthesize a PCL/TCP(Si) composite. The successful modification is confirmed by X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR) analysis. Additive manufactured PCL/TCP(Si) scaffolds have been fabricated using a screw extrusion system (SES). Compression testing demonstrates that both the compressive modulus and compressive yield strength of the developed PCL/TCP(Si) scaffolds fall within the lower ranges of mechanical properties for cancellous bone, with a compressive modulus and compressive yield strength of 6.0 times and 2.3 times of those of PCL/TCP scaffolds, respectively. To enhance the osteoconductive property of the developed PCL/TCP(Si) scaffolds, a CHA-gelatin composite has been coated onto the scaffolds via a biomimetic co-precipitation process, which is verified by using scanning electron microscopy (SEM) and XPS. Confocal laser microscopy and SEM images reveal a most uniform distribution of porcine bone marrow stromal cells (BMSCs) and cellsheet accumulation on the CHA-gelatin composite coated PCL/TCP(Si) scaffolds. The proliferation rate of BMSCs on the CHA-gelatin composite coated PCL/TCP(Si) scaffolds is 2.0 and 1.4 times higher compared to PCL/TCP(Si) and CHA coated PCL/TCP(Si) scaffolds, respectively, by day 10. Furthermore, the reverse transcription polymerase chain reaction (RT-PCR) and western blot analyses reveal that CHA-gelatin composite coated PCL/TCP(Si) scaffolds stimulate osteogenic differentiation of BMSCs the most compared to the other scaffolds. In vitro results of SEM, confocal microscopy and proliferation rate also show that there is no detrimental effect of GPTMS modification on biocompatibility of the scaffolds.

Asymptotic and numerical results for a model of solvent-dependent drug diffusion through polymeric spheres

A model for drug diffusion from a spherical polymeric drug delivery device is considered. The model contains two key features. The first is that solvent diffuses into the polymer, which then transitions from a glassy to a rubbery state. The interface between the two states of polymer is modelled as a moving boundary, whose speed is governed by a kinetic law; the same moving boundary problem arises in the one-phase limit of a Stefan problem with kinetic undercooling. The second feature is that drug diffuses only through the rubbery region, with a nonlinear diffusion coefficient that depends on the concentration of solvent. We analyse the model using both formal asymptotics and numerical computation, the latter by applying a front-fixing scheme with a finite volume method. Previous results are extended and comparisons are made with linear models that work well under certain parameter regimes. Finally, a model for a multi-layered drug delivery device is suggested, which allows for more flexible control of drug release.

Rademacher and Gaussian complexities: Risk bounds and structural results

We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and Gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes. We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.

Sparseness vs estimating conditional probabilities : some asymptotic results

One of the nice properties of kernel classifiers such as SVMs is that they often produce sparse solutions. However, the decision functions of these classifiers cannot always be used to estimate the conditional probability of the class label. We investigate the relationship between these two properties and show that these are intimately related: sparseness does not occur when the conditional probabilities can be unambiguously estimated. We consider a family of convex loss functions and derive sharp asymptotic results for the fraction of data that becomes support vectors. This enables us to characterize the exact trade-off between sparseness and the ability to estimate conditional probabilities for these loss functions.

Correlates of Pedometer Use: Results from a Community-based Physical Activity Intervention Trial (10,000 Steps Rockhampton)

Background Pedometers have become common place in physical activity promotion, yet little information exists on who is using them. The multi-strategy, community-based 10,000 Steps Rockhampton physical activity intervention trial provided an opportunity to examine correlates of pedometer use at the population level. Methods Pedometer use was promoted across all intervention strategies including: local media, pedometer loan schemes through general practice, other health professionals and libraries, direct mail posted to dog owners, walking trail signage, and workplace competitions. Data on pedometer use were collected during the 2-year follow-up telephone interviews from random population samples in Rockhampton, Australia, and a matched comparison community (Mackay). Logistic regression analyses were used to determine the independent influence of interpersonal characteristics and program exposure variables on pedometer use. Results Data from 2478 participants indicated that 18.1% of Rockhampton and 5.6% of Mackay participants used a pedometer in the previous 18-months. Rockhampton pedometer users (n = 222) were more likely to be female (OR = 1.59, 95% CI: 1.11, 2.23), aged 45 or older (OR = 1.69, 95% CI: 1.16, 2.46) and to have higher levels of education (university degree OR = 4.23, 95% CI: 1.86, 9.6). Respondents with a BMI > 30 were more likely to report using a pedometer (OR = 1.68, 95% CI: 1.11, 2.54) than those in the healthy weight range. Compared with those in full-time paid work, respondents in 'home duties' were significantly less likely to report pedometer use (OR = 0.18, 95% CI: 0.06, 0.53). Exposure to individual program components, in particular seeing 10,000 Steps street signage and walking trails or visiting the website, was also significantly associated with greater pedometer use. Conclusion Pedometer use varies between population subgroups, and alternate strategies need to be investigated to engage men, people with lower levels of education and those in full-time 'home duties', when using pedometers in community-based physical activity promotion initiatives.