Osteoporosis is becoming a major health problem that is associated with increased fracture risk. Previous studies have shown that osteoporosis could delay fracture healing. Although there are potential agents available to promote fracture healing of osteoporotic bone such as statins and tocotrienol, studies on direct delivery of these agents to the fracture site are limited. This study was designed to investigate the effects of two potential agents, lovastatin and tocotrienol using targeted drug delivery system on fracture healing of postmenopausal osteoporosis rats. The fracture healing was evaluated using micro CT and biomechanical parameters. Forty-eight Sprague-Dawley female rats were divided into 6 groups. The first group was sham-operated (SO), while the others were ovariectomized (OVx). After two months, the right tibiae of all rats were fractured at metaphysis region using pulsed ultrasound and were fixed with plates and screws. The SO and OVxC groups were given two single injections of lovastatin and tocotrienol carriers. The estrogen group (OVx+EST) was given daily oral gavages of Premarin (64.5 µg/kg). The Lovastatin treatment group (OVx+Lov) was given a single injection of 750 µg/kg lovastatin particles. The tocotrienol group (OVx+TT) was given a single injection of 60 mg/kg tocotrienol particles. The combination treatment group (OVx+Lov+TT) was given two single injections of 750 µg/kg lovastatin particles and 60 mg/kg tocotrienol particles. After 4 weeks of treatment, the fractured tibiae were dissected out for micro-CT and biomechanical assessments. The combined treatment group (OVx+Lov+TT) showed significantly higher callus volume and callus strength than the OVxC group (p<0.05). Both the OVx+Lov and OVx+TT groups showed significantly higher callus strength than the OVxC group (p<0.05), but not for callus volume. In conclusion, combined lovastatin and tocotrienol may promote better fracture healing of osteoporotic bone.

BackgroundPost-menopausal osteoporosis has long been treated and prevented by estrogen replacement therapy (ERT). Despite its effectiveness, ERT is associated with serious adverse effects. Labisia pumila var. alata (LP) is a herb with potential as an alternative agent to ERT due to its phytoestrogenic, antioxidative and anti-inflammatory effects on bone. This study aimed to determine the effects of LP supplementation on bone biomechanical strength of postmenopausal osteoporosis rat model.MethodsNinety-six female Sprague–Dawley rats aged 4 to 5 months old were randomly divided into six groups; six rats in the baseline group (BL) and eighteen rats in each group of; Sham- operated (Sham), ovariectomised control (OVXC) and ovariectomised with daily oral gavages of Premarin at 64.5 μg/kg (ERT), LP at 20 mg/kg (LP20) and LP at 100 mg/kg (LP100) respectively. These groups were subdivided into three, six and nine weeks of treatment periods. Rats in BL group were euthanized before the start of the study, while other rats were euthanized after completion of their treatments. Femora were dissected out for biomechanical strength analysis using Instron Universal Model 5848 Micro Tester.ResultsOVXC group showed deterioration in the bone biomechanical strength with time. Both ERT and LP supplemented rats showed improvements in bone strength parameters such as maximum load, displacement, stiffness, stress, and Young Modulus. The most improved bone strength was seen in rats given LP at the dose of 100 mg/kg for nine weeks.ConclusionLP supplementation at 100 mg/kg was more effective than ERT in reversing ovariectomy-induced bone biomechanical changes.

This paper focuses on clustering analysis using a K-means approach for fatigue feature dataset extraction. The aim of this study is to group the dataset as closely as possible (homogeneity) for the scattered dataset. Kurtosis, the wavelet-based energy coefficient and fatigue damage are calculated for all segments after the extraction process using wavelet transform. Kurtosis, the wavelet-based energy coefficient and fatigue damage are used as input data for the K-means clustering approach. K-means clustering calculates the average distance of each group from the centroid and gives the objective function values. Based on the results, maximum values of the objective function can be seen in the two centroid clusters, with a value of 11.58. The minimum objective function value is found at 8.06 for five centroid clusters. It can be seen that the objective function with the lowest value for the number of clusters is equal to five; which is therefore the best cluster for the dataset.

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This paper presents the convenient wavelet family for the fatigue strain signal analysis based on the wavelet coefficients. This study involves the Morlet and Daubechies wavelet coefficients using both the Continuous and Discrete Wavelet Transforms, respectively. The signals were collected from a front lower suspension arm of a passenger car by placing strain gauges at the highest stress locations. The car was driven over public road surfaces, i. e. pavé, highway and UKM roads. In conclusion, the Daubechies wavelet was the convenient wavelet family for the analysis. It was because the wavelet gave the higher wavelet coefficient values indicating that the resemblance between the wavelet and the signals was stronger, closer and more similar.

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This paper investigates the fatigue damage behaviour of random service loading histories and suggests a criterion to determine fatigue damaging cycles by using probability values. The rainflow strain range distribution is often used in fatigue damage prediction. The existence of very small amplitude of cycles that do not significantly contribute to damage makes the prediction process unnecessarily complex. Therefore, a criterion to determine the size of damaging cycles that contribute significantly to total damage values is desired. In this study, two datasets were compared, i.e. the standard SAE road profile for the suspension system and an experimental data recorded using a strain gauge installed on a vehicle component. Fatigue damage was determined using the Coffin-Manson, Morrow and Smith-Watson-Topper models. The distribution of strain ranges was identified by distribution fitting process. The relation between strain range magnitude and RMS values was explored using the probability of damage occurrence values. The results show that both sets of strain histories data fit well to a mixed Weibull distribution with n subpopulations. The probability of damage occurrence values obtained were very high, i.e. 0.9362 and 0.9987, indicating very high chances of damage incurred by these damaging cycles. Therefore, 2 RMS values is suggested as a criterion for damaging cycles determination.