In Vivo Comparison of Hard Tissue Regeneration with Human Mesenchymal Stem Cells Processed with Either the FICOLL Method or the BMAC Method

Objective: To compare new bone formation in maxillary sinus augmentation procedures using biomaterial associated with mesenchymal stem cells (MSCs) separated by two different isolation methods. Background: In regenerative medicine open cell concentration systems are only allowed for clinical application under good manufacturing practice conditions. Methods: Mononuclear cells, including MSCs, were concentrated with either the synthetic poylsaccharid (FICOLL) method (classic open system-control group, n = 6 sinus) or the bone marrow aspirate concentrate (BMAC) method (closed system-test group, n = 12 sinus) and transplanted in combination with biomaterial. A sample of the cells was characterized by their ability to differentiate. After 4.1 months (SD +/- 1.0) bone biopsies were obtained and analyzed. Results: The new bone formation in the BMAC group was 19.9% (90% confidence interval [CI], 10.9-29), and in the FICOLL group was 15.5% (90% CI, 8.6-22.4). The 4.4% difference was not significant (90% CI, -4.6-13.5; p = 0.39). MSCs could be differentiated into osteogenic, chondrogenic, and adipogenic lineages. Conclusion: MSCs harvested from bone marrow aspirate in combination with bovine bone matrix particles can form lamellar bone and provide a reliable base for dental implants. The closed BMAC system is suited to substitute the open FICOLL system in bone regeneration procedures.

Fluorescence spectroscopy to diagnose hepatic steatosis in a rat model of fatty liver

Steatosis is diagnosed on the basis of the macroscopic aspect of the liver evaluated by the surgeon at the time of organ extraction or by means of a frozen biopsy. In the present study, the applicability of laser-induced fluorescence (LIF) spectroscopy was investigated as a method for the diagnosis of different degrees of steatosis experimentally induced in rats. Rats received a high-lipid diet for different periods of time. The animals were divided into groups according to the degree of induced steatosis diagnosis by histology. The concentration of fat in the liver was correlated with LIF by means of the steatosis fluorescence factor (SFF). The histology classification, according to liver fat concentration was, Severe Steatosis, Moderate Steatosis, Mild Steatosis and Control (no liver steatosis). Fluorescence intensity could be directly correlated with fat content. It was possible to estimate an average of fluorescence intensity variable by means of different confidence intervals (P=95%) for each steatosis group. SFF was significantly higher in the Severe Steatosis group (P < 0.001) compared with the Moderate Steatosis, Mild Steatosis and Control groups. The various degrees of steatosis could be directly correlated with SFF. LIF spectroscopy proved to be a method capable of identifying the degree of hepatic steatosis in this animal model, and has the potential of clinical application for non-invasive evaluation of the degree of steatosis.

Methods of Estimation in Multiple Linear Regression: Application to Clinical Data

Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.

Complementarity of Subjective Global Assessment (SGA) and Nutritional Risk Screening 2002 (NRS 2002) for predicting poor clinical outcomes in hospitalized patients

Background & aims: We evaluated the ability of Nutritional Risk Screening 2002 (NRS 2002) and Subjective Global Assessment (SGA) to predict malnutrition related to poor clinical outcomes. Methods: We assessed 705 patients at a public university hospital within 48 h of admission. Logistic regression and number needed to screen (NNS) were calculated to test the complementarity between the tools and their ability to predict very long length of hospital stay (VLLOS), complications, and death. Results: Of the patients screened, 27.9% were at nutritional risk (NRS+) and 38.9% were malnourished (SGA B or C). Compared to those patients not at nutritional risk, NRS+, SGA B or C patients were at increased risk for complications (p = 0.03, 0.02, and 0.003, respectively). NRS+ patients had an increased risk of death (p = 0.03), and SGA B and C patients had an increased likelihood of VLLOS (p = 0.008 and p < 0.0001, respectively). Patients who were both NRS+ and SGA C had lower estimates of NNS than patients who were NRS+ or SGA C only, though their confidence intervals did overlap. Conclusions: The concurrent application of SGA in NRS+ patients might enhance the ability to predict poor clinical outcomes in hospitalized patients in Brazil. (C) 2010 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients

In this paper, we present different ofrailtyo models to analyze longitudinal data in the presence of covariates. These models incorporate the extra-Poisson variability and the possible correlation among the repeated counting data for each individual. Assuming a CD4 counting data set in HIV-infected patients, we develop a hierarchical Bayesian analysis considering the different proposed models and using Markov Chain Monte Carlo methods. We also discuss some Bayesian discrimination aspects for the choice of the best model.

Application of computed tomography images in the evaluation of magnetic nanoparticles biodistribution

In this work we evaluate the effectiveness of computed tomography images as a tool to determine magnetic nanoparticle biodistribution over biological tissues. For this purpose, tomography images for magnetic nanoparticles, composed of Fe(3)O(4), coated with 2,3-dimercaptosuccinic acid (DMSA), were generated at several material concentrations. The comparison of CT numbers, calculated from these images generated at clinical conditions, with typical CT numbers for biological tissues, shows that the detection of nanoparticle in most tissues is only possible for high material concentrations. (C) 2010 Elsevier B.V. All rights reserved.

Multiple Pathways Analysis of Brain Functional Networks from EEG Signals: An Application to Real Data

In the present study, we propose a theoretical graph procedure to investigate multiple pathways in brain functional networks. By taking into account all the possible paths consisting of h links between the nodes pairs of the network, we measured the global network redundancy R (h) as the number of parallel paths and the global network permeability P (h) as the probability to get connected. We used this procedure to investigate the structural and dynamical changes in the cortical networks estimated from a dataset of high-resolution EEG signals in a group of spinal cord injured (SCI) patients during the attempt of foot movement. In the light of a statistical contrast with a healthy population, the permeability index P (h) of the SCI networks increased significantly (P < 0.01) in the Theta frequency band (3-6 Hz) for distances h ranging from 2 to 4. On the contrary, no significant differences were found between the two populations for the redundancy index R (h) . The most significant changes in the brain functional network of SCI patients occurred mainly in the lower spectral contents. These changes were related to an improved propagation of communication between the closest cortical areas rather than to a different level of redundancy. This evidence strengthens the hypothesis of the need for a higher functional interaction among the closest ROIs as a mechanism to compensate the lack of feedback from the peripheral nerves to the sensomotor areas.

Refraction corrected transmission ultrasound computed tomography for application in breast imaging

Purpose: We present an iterative framework for CT reconstruction from transmission ultrasound data which accurately and efficiently models the strong refraction effects that occur in our target application: Imaging the female breast. Methods: Our refractive ray tracing framework has its foundation in the fast marching method (FNMM) and it allows an accurate as well as efficient modeling of curved rays. We also describe a novel regularization scheme that yields further significant reconstruction quality improvements. A final contribution is the development of a realistic anthropomorphic digital breast phantom based on the NIH Visible Female data set. Results: Our system is able to resolve very fine details even in the presence of significant noise, and it reconstructs both sound speed and attenuation data. Excellent correspondence with a traditional, but significantly more computationally expensive wave equation solver is achieved. Conclusions: Apart from the accurate modeling of curved rays, decisive factors have also been our regularization scheme and the high-quality interpolation filter we have used. An added benefit of our framework is that it accelerates well on GPUs where we have shown that clinical 3D reconstruction speeds on the order of minutes are possible.

A robust Bayesian approach to null intercept measurement error model with application to dental data

Measurement error models often arise in epidemiological and clinical research. Usually, in this set up it is assumed that the latent variable has a normal distribution. However, the normality assumption may not be always correct. Skew-normal/independent distribution is a class of asymmetric thick-tailed distributions which includes the Skew-normal distribution as a special case. In this paper, we explore the use of skew-normal/independent distribution as a robust alternative to null intercept measurement error model under a Bayesian paradigm. We assume that the random errors and the unobserved value of the covariate (latent variable) follows jointly a skew-normal/independent distribution, providing an appealing robust alternative to the routine use of symmetric normal distribution in this type of model. Specific distributions examined include univariate and multivariate versions of the skew-normal distribution, the skew-t distributions, the skew-slash distributions and the skew contaminated normal distributions. The methods developed is illustrated using a real data set from a dental clinical trial. (C) 2008 Elsevier B.V. All rights reserved.