Total hemoglobin mass--a new parameter to detect blood doping?

PURPOSE: All kinds of blood manipulations aim to increase the total hemoglobin mass (tHb-mass). To establish tHb-mass as an effective screening parameter for detecting blood doping, the knowledge of its normal variation over time is necessary. The aim of the present study, therefore, was to determine the intraindividual variance of tHb-mass in elite athletes during a training year emphasizing off, training, and race seasons at sea level. METHODS: tHb-mass and hemoglobin concentration ([Hb]) were determined in 24 endurance athletes five times during a year and were compared with a control group (n = 6). An analysis of covariance was used to test the effects of training phases, age, gender, competition level, body mass, and training volume. Three error models, based on 1) a total percentage error of measurement, 2) the combination of a typical percentage error (TE) of analytical origin with an absolute SD of biological origin, and 3) between-subject and within-subject variance components as obtained by an analysis of variance, were tested. RESULTS: In addition to the expected influence of performance status, the main results were that the effects of training volume (P = 0.20) and training phases (P = 0.81) on tHb-mass were not significant. We found that within-subject variations mainly have an analytical origin (TE approximately 1.4%) and a very small SD (7.5 g) of biological origin. CONCLUSION: tHb-mass shows very low individual oscillations during a training year (<6%), and these oscillations are below the expected changes in tHb-mass due to Herythropoetin (EPO) application or blood infusion (approximately 10%). The high stability of tHb-mass over a period of 1 year suggests that it should be included in an athlete's biological passport and analyzed by recently developed probabilistic inference techniques that define subject-based reference ranges.

Trabecular bone score (TBS) the new parameter of 2D texture analysis for the evaluation of 3D bone micro architecture status

X-ray is a technology that is used for numerous applications in the medical field. The process of X-ray projection gives a 2-dimension (2D) grey-level texture from a 3- dimension (3D) object. Until now no clear demonstration or correlation has positioned the 2D texture analysis as a valid indirect evaluation of the 3D microarchitecture. TBS is a new texture parameter based on the measure of the experimental variogram. TBS evaluates the variation between 2D image grey-levels. The aim of this study was to evaluate existing correlations between 3D bone microarchitecture parameters - evaluated from μCT reconstructions - and the TBS value, calculated on 2D projected images. 30 dried human cadaveric vertebrae were acquired on a micro-scanner (eXplorer Locus, GE) at isotropic resolution of 93 μm. 3D vertebral body models were used. The following 3D microarchitecture parameters were used: Bone volume fraction (BV/TV), Trabecular thickness (TbTh), trabecular space (TbSp), trabecular number (TbN) and connectivity density (ConnD). 3D/2D projections has been done by taking into account the Beer-Lambert Law at X-ray energy of 50, 100, 150 KeV. TBS was assessed on 2D projected images. Correlations between TBS and the 3D microarchitecture parameters were evaluated using a linear regression analysis. Paired T-test is used to assess the X-ray energy effects on TBS. Multiple linear regressions (backward) were used to evaluate relationships between TBS and 3D microarchitecture parameters using a bootstrap process. BV/TV of the sample ranged from 18.5 to 37.6% with an average value at 28.8%. Correlations' analysis showedthat TBSwere strongly correlatedwith ConnD(0.856≤r≤0.862; p<0.001),with TbN (0.805≤r≤0.810; p<0.001) and negatively with TbSp (−0.714≤r≤−0.726; p<0.001), regardless X-ray energy. Results show that lower TBS values are related to "degraded" microarchitecture, with low ConnD, low TbN and a high TbSp. The opposite is also true. X-ray energy has no effect onTBS neither on the correlations betweenTBS and the 3Dmicroarchitecture parameters. In this study, we demonstrated that TBS was significantly correlated with 3D microarchitecture parameters ConnD and TbN, and negatively with TbSp, no matter what X-ray energy has been used. This article is part of a Special Issue entitled ECTS 2011. Disclosure of interest: None declared.

A hierarchical approach to model parameter optimization for developmental systems.

In the context of Systems Biology, computer simulations of gene regulatory networks provide a powerful tool to validate hypotheses and to explore possible system behaviors. Nevertheless, modeling a system poses some challenges of its own: especially the step of model calibration is often difficult due to insufficient data. For example when considering developmental systems, mostly qualitative data describing the developmental trajectory is available while common calibration techniques rely on high-resolution quantitative data. Focusing on the calibration of differential equation models for developmental systems, this study investigates different approaches to utilize the available data to overcome these difficulties. More specifically, the fact that developmental processes are hierarchically organized is exploited to increase convergence rates of the calibration process as well as to save computation time. Using a gene regulatory network model for stem cell homeostasis in Arabidopsis thaliana the performance of the different investigated approaches is evaluated, documenting considerable gains provided by the proposed hierarchical approach.

Probabilistic evidential assessment of gunshot residue particle evidence (Part II) : Bayesian parameter estimation for experimental count data.

Part I of this series of articles focused on the construction of graphical probabilistic inference procedures, at various levels of detail, for assessing the evidential value of gunshot residue (GSR) particle evidence. The proposed models - in the form of Bayesian networks - address the issues of background presence of GSR particles, analytical performance (i.e., the efficiency of evidence searching and analysis procedures) and contamination. The use and practical implementation of Bayesian networks for case pre-assessment is also discussed. This paper, Part II, concentrates on Bayesian parameter estimation. This topic complements Part I in that it offers means for producing estimates useable for the numerical specification of the proposed probabilistic graphical models. Bayesian estimation procedures are given a primary focus of attention because they allow the scientist to combine (his/her) prior knowledge about the problem of interest with newly acquired experimental data. The present paper also considers further topics such as the sensitivity of the likelihood ratio due to uncertainty in parameters and the study of likelihood ratio values obtained for members of particular populations (e.g., individuals with or without exposure to GSR).

S-system parameter estimation for noisy metabolic profiles using newton-flow analysis.

Biochemical systems are commonly modelled by systems of ordinary differential equations (ODEs). A particular class of such models called S-systems have recently gained popularity in biochemical system modelling. The parameters of an S-system are usually estimated from time-course profiles. However, finding these estimates is a difficult computational problem. Moreover, although several methods have been recently proposed to solve this problem for ideal profiles, relatively little progress has been reported for noisy profiles. We describe a special feature of a Newton-flow optimisation problem associated with S-system parameter estimation. This enables us to significantly reduce the search space, and also lends itself to parameter estimation for noisy data. We illustrate the applicability of our method by applying it to noisy time-course data synthetically produced from previously published 4- and 30-dimensional S-systems. In addition, we propose an extension of our method that allows the detection of network topologies for small S-systems. We introduce a new method for estimating S-system parameters from time-course profiles. We show that the performance of this method compares favorably with competing methods for ideal profiles, and that it also allows the determination of parameters for noisy profiles.

Parameter estimation for the distribution of single cell lag times.

In Quantitative Microbial Risk Assessment, it is vital to understand how lag times of individual cells are distributed over a bacterial population. Such identified distributions can be used to predict the time by which, in a growth-supporting environment, a few pathogenic cells can multiply to a poisoning concentration level. We model the lag time of a single cell, inoculated into a new environment, by the delay of the growth function characterizing the generated subpopulation. We introduce an easy-to-implement procedure, based on the method of moments, to estimate the parameters of the distribution of single cell lag times. The advantage of the method is especially apparent for cases where the initial number of cells is small and random, and the culture is detectable only in the exponential growth phase.

Interhuman transmission as a potential key parameter for geographical variation in the prevalence of Pneumocystis jirovecii dihydropteroate synthase mutations.

BACKGROUND: Pneumocystis jirovecii dihydropteroate synthase (DHPS) mutations are associated with failure of prophylaxis with sulfa drugs. This retrospective study sought to better understand the geographical variation in the prevalence of these mutations. METHODS: DHPS polymorphisms in 394 clinical specimens from immunosuppressed patients who received a diagnosis of P. jirovecii pneumonia and who were hospitalized in 3 European cities were examined using polymerase chain reaction (PCR) single-strand conformation polymorphism. Demographic and clinical characteristics were obtained from patients' medical charts. RESULTS: Of the 394 patients, 79 (20%) were infected with a P. jirovecii strain harboring one or both of the previously reported DHPS mutations. The prevalence of DHPS mutations was significantly higher in Lyon than in Switzerland (33.0% vs 7.5%; P < .001). The proportion of patients with no evidence of sulfa exposure who harbored a mutant P. jirovecii DHPS genotype was significantly higher in Lyon than in Switzerland (29.7% vs 3.0%; P < .001). During the study period in Lyon, in contrast to the Swiss hospitals, measures to prevent dissemination of P. jirovecii from patients with P. jirovecii pneumonia were generally not implemented, and most patients received suboptimal prophylaxis, the failure of which was strictly associated with mutated P. jirovecii. Thus, nosocomial interhuman transmission of mutated strains directly or indirectly from other individuals in whom selection of mutants occurred may explain the high proportion of mutations without sulfa exposure in Lyon. CONCLUSIONS: Interhuman transmission of P. jirovecii, rather than selection pressure by sulfa prophylaxis, may play a predominant role in the geographical variation in the prevalence in the P. jirovecii DHPS mutations.

Detection of Optimal Models in Parameter Space with Support Vector Machines

The paper proposes an approach aimed at detecting optimal model parameter combinations to achieve the most representative description of uncertainty in the model performance. A classification problem is posed to find the regions of good fitting models according to the values of a cost function. Support Vector Machine (SVM) classification in the parameter space is applied to decide if a forward model simulation is to be computed for a particular generated model. SVM is particularly designed to tackle classification problems in high-dimensional space in a non-parametric and non-linear way. SVM decision boundaries determine the regions that are subject to the largest uncertainty in the cost function classification, and, therefore, provide guidelines for further iterative exploration of the model space. The proposed approach is illustrated by a synthetic example of fluid flow through porous media, which features highly variable response due to the parameter values' combination.

Post-detection analysis for grating-based ultra-small angle X-ray scattering.

Until recently, the hard X-ray, phase-sensitive imaging technique called grating interferometry was thought to provide information only in real space. However, by utilizing an alternative approach to data analysis we demonstrated that the angular resolved ultra-small angle X-ray scattering distribution can be retrieved from experimental data. Thus, reciprocal space information is accessible by grating interferometry in addition to real space. Naturally, the quality of the retrieved data strongly depends on the performance of the employed analysis procedure, which involves deconvolution of periodic and noisy data in this context. The aim of this article is to compare several deconvolution algorithms to retrieve the ultra-small angle X-ray scattering distribution in grating interferometry. We quantitatively compare the performance of three deconvolution procedures (i.e., Wiener, iterative Wiener and Lucy-Richardson) in case of realistically modeled, noisy and periodic input data. The simulations showed that the algorithm of Lucy-Richardson is the more reliable and more efficient as a function of the characteristics of the signals in the given context. The availability of a reliable data analysis procedure is essential for future developments in grating interferometry.

CD34/QBEND10 immunostaining in bone marrow biopsies: an additional parameter for the diagnosis and classification of myelodysplastic syndromes.

CD34/QBEND10 immunostaining has been assessed in 150 bone marrow biopsies (BMB) including 91 myelodysplastic syndromes (MDS), 16 MDS-related AML, 25 reactive BMB, and 18 cases where RA could neither be established nor ruled out. All cases were reviewed and classified according to the clinical and morphological FAB criteria. The percentage of CD34-positive (CD34 +) hematopoietic cells and the number of clusters of CD34+ cells in 10 HPF were determined. In most cases the CD34+ cell count was similar to the blast percentage determined morphologically. In RA, however, not only typical blasts but also less immature hemopoietic cells lying morphologically between blasts and promyelocytes were stained with CD34. The CD34+ cell count and cluster values were significantly higher in RA than in BMB with reactive changes (p&lt;0.0001 for both), in RAEB than in RA (p=0.0006 and p=0.0189, respectively), in RAEBt than in RAEB (p=0.0001 and p=0.0038), and in MDS-AML than in RAEBt (p&lt;0.0001 and p=0.0007). Presence of CD34+ cell clusters in RA correlated with increased risk of progression of the disease. We conclude that CD34 immunostaining in BMB is a useful tool for distinguishing RA from other anemias, assessing blast percentage in MDS cases, classifying them according to FAB, and following their evolution.

Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ).

Normal ageing is associated with characteristic changes in brain microstructure. Although in vivo neuroimaging captures spatial and temporal patterns of age-related changes of anatomy at the macroscopic scale, our knowledge of the underlying (patho)physiological processes at cellular and molecular levels is still limited. The aim of this study is to explore brain tissue properties in normal ageing using quantitative magnetic resonance imaging (MRI) alongside conventional morphological assessment. Using a whole-brain approach in a cohort of 26 adults, aged 18-85years, we performed voxel-based morphometric (VBM) analysis and voxel-based quantification (VBQ) of diffusion tensor, magnetization transfer (MT), R1, and R2* relaxation parameters. We found age-related reductions in cortical and subcortical grey matter volume paralleled by changes in fractional anisotropy (FA), mean diffusivity (MD), MT and R2*. The latter were regionally specific depending on their differential sensitivity to microscopic tissue properties. VBQ of white matter revealed distinct anatomical patterns of age-related change in microstructure. Widespread and profound reduction in MT contrasted with local FA decreases paralleled by MD increases. R1 reductions and R2* increases were observed to a smaller extent in overlapping occipito-parietal white matter regions. We interpret our findings, based on current biophysical models, as a fingerprint of age-dependent brain atrophy and underlying microstructural changes in myelin, iron deposits and water. The VBQ approach we present allows for systematic unbiased exploration of the interaction between imaging parameters and extends current methods for detection of neurodegenerative processes in the brain. The demonstrated parameter-specific distribution patterns offer insights into age-related brain structure changes in vivo and provide essential baseline data for studying disease against a background of healthy ageing.

Scattering of repetitive DNA sequences in the albumin and vitellogenin gene loci of Xenopus laevis.

We have analyzed middle repetitive DNA in the albumin and vitellogenin gene families of Xenopus laevis. Mapping specific repetitive DNA sequences derived from introns of the A1 vitellogenin gene reveals that these sequences are scattered within and around the four vitellogenin genes (A1, A2, B1 and B2) and the two albumin genes (74 kd and 68 kd). Three repetitive DNA elements present in the A1 vitellogenin transcriptional unit are also located in introns of the 74 kd albumin gene. This apparently random distribution of middle repetitive DNA in the two gene families suggests that the analyzed sequences are not involved in gene regulation, but rather that they might represent unstable genetic elements. This hypothesis is further supported by the finding that size polymorphism in the A1 vitellogenin gene and in the 74 kd albumin gene is correlated with the presence or absence of repetitive DNA.

Foam pore size is a critical interface parameter of suction-based wound healing devices.

BACKGROUND: Suction-based wound healing devices with open-pore foam interfaces are widely used to treat complex tissue defects. The impact of changes in physicochemical parameters of the wound interfaces has not been investigated. METHODS: Full-thickness wounds in diabetic mice were treated with occlusive dressing or a suction device with a polyurethane foam interface varying in mean pore size diameter. Wound surface deformation on day 2 was measured on fixed tissues. Histologic cross-sections were analyzed for granulation tissue thickness (hematoxylin and eosin), myofibroblast density (α-smooth muscle actin), blood vessel density (platelet endothelial cell adhesion molecule-1), and cell proliferation (Ki67) on day 7. RESULTS: Polyurethane foam-induced wound surface deformation increased with polyurethane foam pore diameter: 15 percent (small pore size), 60 percent (medium pore size), and 150 percent (large pore size). The extent of wound strain correlated with granulation tissue thickness that increased 1.7-fold in small pore size foam-treated wounds, 2.5-fold in medium pore size foam-treated wounds, and 4.9-fold in large pore size foam-treated wounds (p < 0.05) compared with wounds treated with an occlusive dressing. All polyurethane foams increased the number of myofibroblasts over occlusive dressing, with maximal presence in large pore size foam-treated wounds compared with all other groups (p < 0.05). CONCLUSIONS: The pore size of the interface material of suction devices has a significant impact on the wound healing response. Larger pores increased wound surface strain, tissue growth, and transformation of contractile cells. Modification of the pore size is a powerful approach for meeting biological needs of specific wounds.

Estimation of the correlation structure of crustal velocity heterogeneity from seismic reflection data

Numerous sources of evidence point to the fact that heterogeneity within the Earth's deep crystalline crust is complex and hence may be best described through stochastic rather than deterministic approaches. As seismic reflection imaging arguably offers the best means of sampling deep crustal rocks in situ, much interest has been expressed in using such data to characterize the stochastic nature of crustal heterogeneity. Previous work on this problem has shown that the spatial statistics of seismic reflection data are indeed related to those of the underlying heterogeneous seismic velocity distribution. As of yet, however, the nature of this relationship has remained elusive due to the fact that most of the work was either strictly empirical or based on incorrect methodological approaches. Here, we introduce a conceptual model, based on the assumption of weak scattering, that allows us to quantitatively link the second-order statistics of a 2-D seismic velocity distribution with those of the corresponding processed and depth-migrated seismic reflection image. We then perform a sensitivity study in order to investigate what information regarding the stochastic model parameters describing crustal velocity heterogeneity might potentially be recovered from the statistics of a seismic reflection image using this model. Finally, we present a Monte Carlo inversion strategy to estimate these parameters and we show examples of its application at two different source frequencies and using two different sets of prior information. Our results indicate that the inverse problem is inherently non-unique and that many different combinations of the vertical and lateral correlation lengths describing the velocity heterogeneity can yield seismic images with the same 2-D autocorrelation structure. The ratio of all of these possible combinations of vertical and lateral correlation lengths, however, remains roughly constant which indicates that, without additional prior information, the aspect ratio is the only parameter describing the stochastic seismic velocity structure that can be reliably recovered.