Osteoporosis and periodontitis in older subjects participating in the Swedish National Survey on Aging and Care (SNAC-Blekinge)

Abstract Objective. We assessed the relationships between (I) ultrasonography calcaneus T-scores (PIXI) and mandibular cortex characteristics on oral panoramic radiographs in older subjects; and (II) osteoporosis and periodontitis. Material and methods. We examined 778 subjects (53% women) aged 59-96 years. Periodontitis was defined by alveolar bone loss assessed from panoramic radiographs. Results. PIXI calcaneus T-values ?-2.5 (osteoporosis) were found in 16.3% of women and in 8.1% of men. PIXI calcaneus T-values <-1.6 (osteoporosis, adjusted) were found in 34.2% of women and in 21.4% of men. The age of the subjects and PIXI T-values were significantly correlated in women (Pearson's r = 0.37, P < 0.001) and men (Pearson's r = 0.19, P < 0.001). Periodontitis was found in 18.7% of subjects defined by alveolar bone level ?5 mm. Subjects with osteoporosis defined by adjusted PIXI T-values had fewer remaining teeth [mean difference 4.1, 95% confidence interval (CI) -1.1 to -6.5, P < 0.001]. The crude odds ratio (OR) of an association between the panoramic assessment of mandibular cortex erosions as a sign of osteoporosis and the adjusted T-value (T-value cut-off <-1.6) was 4.8 (95% CI 3.1-7.2, P < 0.001; Pearson ?(2) = 60.1, P < 0.001). A significant OR between osteoporosis and periodontitis was only found in women for the T-value cut-off ?-2.5 (crude OR 1.8, 95% CI 1.1-3.3, P < 0.03). Conclusions. An association between osteoporosis and periodontitis was only confirmed in women. The likelihood that the mandibular cortex index agrees with adjusted PIXI T-values is significant.

Effect of smoking on the results of a chlorhexidine digluconate treatment extended up to 3 months after scaling and root planing-a pilot study

The aim of the study was to evaluate the impact of smoking on a prolongated chlorhexidine digluconate regimen after scaling and root planing. Forty-two smokers (test group) and 85 nonsmoking patients (control group) with generalized chronic periodontitis were examined for clinical attachment level (CAL), probing depth (PD), bleeding on probing (BoP), and Plaque Index (Pl) at baseline and after 1 and 3 months. During scaling and root planing, a 0.2% chlorhexidine digluconate solution and a 1% chlorhexidine digluconate gel were used. The subjects used a 0.2% chlorhexidine digluconate solution twice daily for 3 months. The Mann-Whitney U and Wilcoxon tests were used for statistical analysis. There were significant improvements of all studied variables after 1 and 3 months in both groups. After 3 months, the mean improvement in the test group was 1.62 mm for CAL, 2.85 mm for PD, and 48% for BoP; in the control group, the values were 2.18 mm for CAL, 2.81 mm for PD, and 47% for BoP. Only the maximum changes of CAL between 1 and 3 months (test group, 0.32 mm vs 0.69 mm in the control group) and PD (test group, 0.47 mm vs 0.76 mm in the control group) were significantly different between the groups (P < .05 and P = .05, respectively). The present data appear to suggest that the use of chlorhexidine digluconate twice daily during a period of 3 months following nonsurgical periodontal therapy may result in significant clinical improvements in smokers and nonsmokers.

Proliferation, differentiation and gene expression of osteoblasts in boron-containing associated with dexamethasone deliver from mesoporous bioactive glass scaffolds

Boron is one of the trace elements in the human body which plays an important role in bone growth. Porous mesopore bioactive glass (MBG) scaffolds are proposed as potential bone regeneration materials due to their excellent bioactivity and drug-delivery ability. The aims of the present study were to develop boron-containing MBG (B-MBG) scaffolds by sol-gel method and to evaluate the effect of boron on the physiochemistry of B-MBG scaffolds and the response of osteoblasts to these scaffolds. Furthermore, the effect of dexamethasone (DEX) delivery in B-MBG scaffold system was investigated on the proliferation, differentiation and bone-related gene expression of osteoblasts. The composition, microstructure and mesopore properties (specific surface area, nano-pore volume and nano-pore distribution) of B-MBG scaffolds have been characterized. The effect of boron contents and large-pore porosity on the loading and release of DEX in B-MBG scaffolds were also investigated. The results have shown that the incorporation of boron into MBG scaffolds slightly decreases the specific surface area and pore volume, but maintains well-ordered mesopore structure and high surface area and nano-pore volume compared to non-mesopore bioactive glass. Boron contents in MBG scaffolds did not influence the nano-pore size distribution or the loading and release of DEX. B-MBG scaffolds have the ability to maintain a sustained release of DEX in a long-term span. Incorporating boron into MBG glass scaffolds led to a controllable release of boron ions and significantly improved the proliferation and bone-related gene expression (Col I and Runx2) of osteoblasts. Furthermore, the sustained release of DEX from B-MBG scaffolds significantly enhanced alkaline phosphatase (ALP) activity and gene expressions (Col I, Runx2, ALP and BSP) of osteoblasts. These results suggest that boron plays an important role in enhancing osteoblast proliferation in B-MBG scaffold system and DEX-loaded B-MBG scaffolds show great potential as a release system to enhance osteogenic property for bone tissue engineering application.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Multi-Scale Modeling for Image Analysis of Brain Tumor Studies

Image-based modeling of tumor growth combines methods from cancer simulation and medical imaging. In this context, we present a novel approach to adapt a healthy brain atlas to MR images of tumor patients. In order to establish correspondence between a healthy atlas and a pathologic patient image, tumor growth modeling in combination with registration algorithms is employed. In a first step, the tumor is grown in the atlas based on a new multi-scale, multi-physics model including growth simulation from the cellular level up to the biomechanical level, accounting for cell proliferation and tissue deformations. Large-scale deformations are handled with an Eulerian approach for finite element computations, which can operate directly on the image voxel mesh. Subsequently, dense correspondence between the modified atlas and patient image is established using nonrigid registration. The method offers opportunities in atlasbased segmentation of tumor-bearing brain images as well as for improved patient-specific simulation and prognosis of tumor progression.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.