189 resultados para spatially varying object pixel density
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Background: Bone health is a concern when treating early stage breast cancer patients with adjuvant aromatase inhibitors. Early detection of patients (pts) at risk of osteoporosis and fractures may be helpful for starting preventive therapies and selecting the most appropriate endocrine therapy schedule. We present statistical models describing the evolution of lumbar and hip bone mineral density (BMD) in pts treated with tamoxifen (T), letrozole (L) and sequences of T and L. Methods: Available dual-energy x-ray absorptiometry exams (DXA) of pts treated in trial BIG 1-98 were retrospectively collected from Swiss centers. Treatment arms: A) T for 5 years, B) L for 5 years, C) 2 years of T followed by 3 years of L and, D) 2 years of L followed by 3 years of T. Pts without DXA were used as a control for detecting selection biases. Patients randomized to arm A were subsequently allowed an unplanned switch from T to L. Allowing for variations between DXA machines and centres, two repeated measures models, using a covariance structure that allow for different times between DXA, were used to estimate changes in hip and lumbar BMD (g/cm2) from trial randomization. Prospectively defined covariates, considered as fixed effects in the multivariable models in an intention to treat analysis, at the time of trial randomization were: age, height, weight, hysterectomy, race, known osteoporosis, tobacco use, prior bone fracture, prior hormone replacement therapy (HRT), bisphosphonate use and previous neo-/adjuvant chemotherapy (ChT). Similarly, the T-scores for lumbar and hip BMD measurements were modeled using a per-protocol approach (allowing for treatment switch in arm A), specifically studying the effect of each therapy upon T-score percentage. Results: A total of 247 out of 546 pts had between 1 and 5 DXA; a total of 576 DXA were collected. Number of DXA measurements per arm were; arm A 133, B 137, C 141 and D 135. The median follow-up time was 5.8 years. Significant factors positively correlated with lumbar and hip BMD in the multivariate analysis were weight, previous HRT use, neo-/adjuvant ChT, hysterectomy and height. Significant negatively correlated factors in the models were osteoporosis, treatment arm (B/C/D vs. A), time since endocrine therapy start, age and smoking (current vs. never).Modeling the T-score percentage, differences from T to L were -4.199% (p = 0.036) and -4.907% (p = 0.025) for the hip and lumbar measurements respectively, before any treatment switch occurred. Conclusions: Our statistical models describe the lumbar and hip BMD evolution for pts treated with L and/or T. The results of both localisations confirm that, contrary to expectation, the sequential schedules do not seem less detrimental for the BMD than L monotherapy. The estimated difference in BMD T-score percent is at least 4% from T to L.
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Congenital malformations or injuries of the urethra can be treated using existing autologous tissue, but these procedures are sometimes associated with severe complications. Therefore, tissue engineering may be advantageous for generating urethral grafts. We evaluated engineered high-density collagen gel tubes as urethral grafts in 16 male New Zealand white rabbits. The constructs were either acellular or seeded with autologous smooth muscle cells, isolated from an open bladder biopsy. After the formation of a urethral defect by excision, the tissue-engineered grafts were interposed between the remaining urethral ends. No catheter was placed postoperatively. The animals were evaluated at 1 or 3 months by contrast urethrography and histological examination. Comparing the graft caliber to the control urethra at 3 months, a larger caliber was found in the cell-seeded grafts (96.6% of the normal caliber) than in the acellular grafts (42.3%). Histology of acellular and cell-seeded grafts did not show any sign of inflammation, and spontaneous regrowth of urothelium could be demonstrated in all grafts. Urethral fistulae, sometimes associated with stenosis, were observed, which might be prevented by urethral catheter application. High-density collagen gel tubes may be clinically useful as an effective treatment of congenital and acquired urethral pathologies.
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In order to understand how plasticity is related to neurodegeneration, we studied synaptic proteins with quantitative immunohistochemistry in the entorhinal cortex from Alzheimer patients and age-matched controls. We observed a significant decrease in presynaptic synaptophysin and an increase in postsynaptic density protein PSD-95, positively correlated with beta amyloid and phosphorylated Tau proteins in Alzheimer cases. Furthermore, Alzheimer-like neuritic retraction was generated in okadaic acid (OA) treated SH-SY5Y neuroblastoma cells with no decrease in PSD-95 expression. However, in a SH-SY5Y clone with decreased expression of transcription regulator LMO4 (as observed in Alzheimer's disease) and increased neuritic length, PSD-95 expression was enhanced but did not change with OA treatment. Therefore, increased PSD-95 immunoreactivity in the entorhinal cortex might result from compensatory mechanisms, as in the SH-SY5Y clone, whereas increased Alzheimer-like Tau phosphorylation is not related to PSD-95 expression, as suggested by the OA-treated cell models.
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In the cerebrospinal fluid of 26 drug-naive schizophrenics (DSM-III- R), we observed that the level of glutathione ([GSH]) and of its metabolite γ-Glu-Gln was decreased by 27% and 16% respectively. Using a new in-vivo method based on magnetic resonance spec- troscopy, [GSH] was measured in the medial prefrontal cortex of 18 schizophrenics and found to be 52 % lower than in controls (n = 20). This is consistent with the recently observed decreased mRNA levels in fibroblasts of patients (n=32) of the two GSH synthesizing en- zymes (glutathione synthetase (GSS), and glutamate-cysteine ligase M (GCLM) the modulatory subunit of glutamate-cysteine ligase). Moreover, the level of GCLM expression in fibroblasts correlates neg- atively with the psychopathology (positive, general and some nega- tive symptoms). Thus, the observed difference in gene expression is not only the cause of low brain [GSH], but is also related to the sever- ity of symptoms, suggesting that fibroblasts are adequate surrogate for brain tissue. A hypothesis was proposed, based on a central role of GSH in the pathophysiology of schizophrenia. GSH is an important endogenous redox regulator and neuroactive substance. GSH is pro- tecting cells from damage by reactive oxygen species generated, among others, by the metabolism of dopamine. A GSH deficit-in- duced oxidative stress would lead to lipid peroxidation and micro-le- sions in the surrounding of catecholamine terminals, affecting the synaptic contacts on dendritic spines of cortical neurones, where ex- citatory glutamatergic terminals converge with dopaminergic ones. This would lead to spines degeneration and abnormal nervous con- nections or structural disconnectivity, possibly responsible for posi- tive, perceptive and cognitive symptoms of schizophrenia. In addi- tion, a GSH deficit could also lead to a functional disconnectivity by depressing NMDA neurotransmission, in analogy to phencyclidine effects. Present experimental biochemical, cell biological and behav- ioral data are consistent with the proposed mechanism: decreasing pharmacologically [GSH] in experimental models, with or without blocking DA uptake (GBR12909), induces morphological and behav- ioral changes similar to those observed in patients. Dendritic spines: (a) In neuronal cultures, low [GSH] and DA induce decreased density of neural processes; (b) In developing rats (p5-p16), [GSH] deficit and GBR induce a decrease in normal spines in prefrontal pyramids and in GABA-parvalbumine but not of -calretinine immunoreactivity in anterior cingulate. NMDA-dependant synaptic plasticity: GSH deple- I/13 tion in hippocampal slices impairs long-term potentiation. Develop- ing rats with low [GSH] and GBR have deficit in olfactory integration and in object recognition which appears earlier in males than fe- males, in analogy to the delay of the psychosis onset between man and woman. In summary, a deficit of GSH and/or GSH-related enzymes during early development could constitute a major vulnerability fac- tor in schizophrenia.
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quantiNemo is an individual-based, genetically explicit stochastic simulation program. It was developed to investigate the effects of selection, mutation, recombination and drift on quantitative traits with varying architectures in structured populations connected by migration and located in a heterogeneous habitat. quantiNemo is highly flexible at various levels: population, selection, trait(s) architecture, genetic map for QTL and/or markers, environment, demography, mating system, etc. quantiNemo is coded in C++ using an object-oriented approach and runs on any computer platform. Availability: Executables for several platforms, user's manual, and source code are freely available under the GNU General Public License at http://www2.unil.ch/popgen/softwares/quantinemo.
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BACKGROUND: The risk of osteoporosis and fracture influences the selection of adjuvant endocrine therapy. We analyzed bone mineral density (BMD) in Swiss patients of the Breast International Group (BIG) 1-98 trial [treatment arms: A, tamoxifen (T) for 5 years; B, letrozole (L) for 5 years; C, 2 years of T followed by 3 years of L; D, 2 years of L followed by 3 years of T]. PATIENTS AND METHODS: Dual-energy X-ray absorptiometry (DXA) results were retrospectively collected. Patients without DXA served as control group. Repeated measures models using covariance structures allowing for different times between DXA were used to estimate changes in BMD. Prospectively defined covariates were considered as fixed effects in the multivariable models. RESULTS: Two hundred and sixty-one of 546 patients had one or more DXA with 577 lumbar and 550 hip measurements. Weight, height, prior hormone replacement therapy, and hysterectomy were positively correlated with BMD; the correlation was negative for letrozole arms (B/C/D versus A), known osteoporosis, time on trial, age, chemotherapy, and smoking. Treatment did not influence the occurrence of osteoporosis (T score < -2.5 standard deviation). CONCLUSIONS: All aromatase inhibitor regimens reduced BMD. The sequential schedules were as detrimental for bone density as L monotherapy.
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CONTEXT: In the Health Outcomes and Reduced Incidence with Zoledronic Acid Once Yearly - Pivotal Fracture Trial (HORIZON-PFT), zoledronic acid (ZOL) 5 mg significantly reduced fracture risk. OBJECTIVE: The aim of the study was to identify factors associated with greater efficacy during ZOL 5 mg treatment. DESIGN, SETTING, AND PATIENTS: We conducted a subgroup analysis (preplanned and post hoc) of a multicenter, double-blind, placebo-controlled, 36-month trial in 7765 women with postmenopausal osteoporosis. Intervention: A single infusion of ZOL 5 mg or placebo was administered at baseline, 12, and 24 months. MAIN OUTCOME MEASURES: Primary endpoints were new vertebral fracture and hip fracture. Secondary endpoints were nonvertebral fracture and change in femoral neck bone mineral density (BMD). Baseline risk factor subgroups were age, BMD T-score and vertebral fracture status, total hip BMD, race, weight, geographical region, smoking, height loss, history of falls, physical activity, prior bisphosphonates, creatinine clearance, body mass index, and concomitant osteoporosis medications. RESULTS: Greater ZOL induced effects on vertebral fracture risk were seen with younger age (treatment-by-subgroup interaction, P = 0.05), normal creatinine clearance (P = 0.04), and body mass index >or= 25 kg/m(2) (P = 0.02). There were no significant treatment-factor interactions for hip or nonvertebral fracture or for change in BMD. CONCLUSIONS: ZOL appeared more effective in preventing vertebral fracture in younger women, overweight/obese women, and women with normal renal function. ZOL had similar effects irrespective of fracture risk factors or femoral neck BMD.
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The breccia-hosted epithermal Au-Ag deposit of Rosia Montana is located 7 kin northeast of Abrud, in the northern part of the South Apuseni Mountains, Romania. Estimated total reserves of 214.91 million metric toils (Mt) of ore at 1.46 g/t An and 6.9 g/t Ag (10.1 Moz of An and 47.6 Moz of Ag) make Rosia Montana one of the largest gold deposits in Europe. At this location, Miocene calc-alkaline magmatic and hydrothermal activity was associated with local extensional tectonics within a strike-slip regime related to the indentation of the Adriatic microplate into the European plate during the Carpathian orogenesis. The host rocks of the magmatic complex consist of pre-Mesozoic metamorphosed continental crust covered by Cretaceous turbiditic sediment (flysch). Magmatic activity at Rosia Montana and its surroundings occurred in several pulses and lasted about 7 m.y, Rosia Montana is a breccia-hosted epithermal system related to strong phreatomagmatic activity due to the shallow emplacement of the Montana dacite. The Montana dacite intruded Miocene volcaniclastic material (volcaniclastic breccias) and crops out at Cetate and Carnic Hills. Current mining is focused primarily on the Cetate open pit, which was mapped in detail, leading to the recognition of three distinct breccia bodies: the dacite breccia with a dominantly hydrothermal matrix, the gray polymict breccia with a greater proportion of sand-sized matrix support, and the black polymict breccia, which reached to the surface, contains carbonized tree trunks and has a dominantly barren elastic matrix. The hydrothermal alteration is pervasive. Adularia alteration with a phyllic overprint is ubiquitous; silicification and argillic alteration occur locally. Mineralization consists of quartz, adularia, carbonates (commonly Mn-rich), pyrite, Fe-poor sphalerite, galena, chalcopyrite, tetrahedrite, and native gold and occurs as disseminations, as well as in veins and filling vugs within the Montana dacite and the different breccias. The age of mineralization (12.85 +/- 0.07 Ma) was determined by Ar-40- Ar-39 dating on hydrothermal adularia crystals from vugs in the dacite breccia in the Cetate open pit. Microthermometric measurements of fluid inclusions in quartz phenocrysts from the Montana dacite revealed two fluid types that are absent from the hydrothermal breccia and must have been trapped at depth prior to dacite dome emplacement: brine inclusions (32-55 -wt % NaCl equiv, homogenizing at T-h > 460 degrees C) and intermediate density fluids (4.9-15.6 wt % NaCl equiv, T, between 345 degrees-430 degrees C). Secondary aqueous fluid inclusion assemblages in the phenocrysts have salinities of 0.2 to 2.2 wt percent NaCl equiv and T-h of 200 degrees to 280 degrees C. Fluid inclusion assemblages in hydrothermal quartz from breccias and veins have salinities of 0.2 to 3.4 wt percent NaCl equiv and T-h, from 200 degrees to 270 degrees C. The oxygen isotope composition of several zones of an ore-related epithermal quartz crystal indicate a very constant delta O-18 of 4.5 to 5.0 per mil for the mineralizing fluid, despite significant salinity and temperature variation over time. Following microthermometry, selected fluid inclusion assemblages were analyzed by laser ablation-inductively coupled-plasma mass spectrometry (LA-ICMS). Despite systematic differences in salinity between phenocryst-hosted fluids trapped at depth and fluids from quartz in the epithermal breccias, all fluids have overlapping major and trace cation ratios, including identical Na/K/Rb/Sr/Cs/Ba. Consistent with the constant near-magmatic oxygen isotope composition of the hydrothermal fluids, these data strongly indicate a common magmatic component of these chemically conservative solutes in all fluids. Cu, Pb, Zn, and Mn show variations in concentration relative to the relatively non-reactive alkalis, reflecting the precipitation of sulfide minerals together with An in the epithermal breccia, and possibly of Cu in an inferred subjacent porphyry environment. The magmatic-hydrothermal processes responsible for epithermal Au-Ag mineralization at Rosia Montana are, however, not directly related to the formation of the spatially associated porphyry Cu-Au deposit of Rosia Poieni, which occurred lout 3 m.y. later.
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Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.
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In the wake of the 1989 Exxon Valdez oil spill, spatially and temporally spill-correlated biological effects consistent with polycyclic aromatic hydrocarbon (PAH) exposure were observed. Some works have proposed that confounding sources from local source rocks, prominently coals, are the provenance of the PAHs. Representative coal deposits along the southeast Alaskan coast (Kulthieth Formation) were sampled and fully characterized chemically and geologically. The coals have variable but high total organic carbon content technically classifying as coals and coaly shale, and highly varying PAH contents. Even for coals with high PAH content (approximately 4000 ppm total PAHs), a PAH-sensitive bacterial biosensor demonstrates nondetectable bioavailability as quantified, based on naphthalene as a test calibrant. These results are consistent with studies indicating that materials such as coals strongly diminish the bioavailability of hydrophobic organic compounds and support previous work suggesting that hydrocarbons associated with the regional background in northern Gulf of Alaska marine sediments are not appreciably bioavailable.
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This work consists of three essays investigating the ability of structural macroeconomic models to price zero coupon U.S. government bonds. 1. A small scale 3 factor DSGE model implying constant term premium is able to provide reasonable a fit for the term structure only at the expense of the persistence parameters of the structural shocks. The test of the structural model against one that has constant but unrestricted prices of risk parameters shows that the exogenous prices of risk-model is only weakly preferred. We provide an MLE based variance-covariance matrix of the Metropolis Proposal Density that improves convergence speeds in MCMC chains. 2. Affine in observable macro-variables, prices of risk specification is excessively flexible and provides term-structure fit without significantly altering the structural parameters. The exogenous component of the SDF is separating the macro part of the model from the term structure and the good term structure fit has as a driving force an extremely volatile SDF and an implied average short rate that is inexplicable. We conclude that the no arbitrage restrictions do not suffice to temper the SDF, thus there is need for more restrictions. We introduce a penalty-function methodology that proves useful in showing that affine prices of risk specifications are able to reconcile stable macro-dynamics with good term structure fit and a plausible SDF. 3. The level factor is reproduced most importantly by the preference shock to which it is strongly and positively related but technology and monetary shocks, with negative loadings, are also contributing to its replication. The slope factor is only related to the monetary policy shocks and it is poorly explained. We find that there are gains in in- and out-of-sample forecast of consumption and inflation if term structure information is used in a time varying hybrid prices of risk setting. In-sample yield forecast are better in models with non-stationary shocks for the period 1982-1988. After this period, time varying market price of risk models provide better in-sample forecasts. For the period 2005-2008, out of sample forecast of consumption and inflation are better if term structure information is incorporated in the DSGE model but yields are better forecasted by a pure macro DSGE model.
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The trabecular bone score (TBS) is an index of bone microarchitectural texture calculated from anteroposterior dual-energy X-ray absorptiometry (DXA) scans of the lumbar spine (LS) that predicts fracture risk, independent of bone mineral density (BMD). The aim of this study was to compare the effects of yearly intravenous zoledronate (ZOL) versus placebo (PLB) on LS BMD and TBS in postmenopausal women with osteoporosis. Changes in TBS were assessed in the subset of 107 patients recruited at the Department of Osteoporosis of the University Hospital of Berne, Switzerland, who were included in the HORIZON trial. All subjects received adequate calcium and vitamin D3. In these patients randomly assigned to either ZOL (n = 54) or PLB (n = 53) for 3 years, BMD was measured by DXA and TBS assessed by TBS iNsight (v1.9) at baseline and 6, 12, 24, and 36 months after treatment initiation. Baseline characteristics (mean ± SD) were similar between groups in terms of age, 76.8 ± 5.0 years; body mass index (BMI), 24.5 ± 3.6 kg/m(2) ; TBS, 1.178 ± 0.1 but for LS T-score (ZOL-2.9 ± 1.5 versus PLB-2.1 ± 1.5). Changes in LS BMD were significantly greater with ZOL than with PLB at all time points (p < 0.0001 for all), reaching +9.58% versus +1.38% at month 36. Change in TBS was significantly greater with ZOL than with PLB as of month 24, reaching +1.41 versus-0.49% at month 36; p = 0.031, respectively. LS BMD and TBS were weakly correlated (r = 0.20) and there were no correlations between changes in BMD and TBS from baseline at any visit. In postmenopausal women with osteoporosis, once-yearly intravenous ZOL therapy significantly increased LS BMD relative to PLB over 3 years and TBS as of 2 years. © 2013 American Society for Bone and Mineral Research.
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The aim of this paper is to bring into consideration a way of studying culture in infancy. An emphasis is put on the role that the material object plays in early interactive processes. Accounted as a cultural artefact, the object is seen as a fundamental element within triadic mother‐object‐ infant interactions and is believed to be a driving force both for communicative and cognitive development. In order to reconsider the importance of the object in child development and to present an approach of studying object construction, accounts in literature on early communication development and the importance of the object are reviewed and discussed under the light of the cultural specificity of the material object.
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INTRODUCTION: One quarter of osteoporotic fractures occur in men. TBS, a gray-level measurement derived from lumbar spine DXA image texture, is related to microarchitecture and fracture risk independently of BMD. Previous studies reported the ability of spine TBS to predict osteoporotic fractures in women. Our aim was to evaluate the ability of TBS to predict clinical osteoporotic fractures in men. METHODS: 3620 men aged ≥50 (mean 67.6years) at the time of baseline DXA (femoral neck, spine) were identified from a database (Province of Manitoba, Canada). Health service records were assessed for the presence of non-traumatic osteoporotic fracture after BMD testing. Lumbar spine TBS was derived from spine DXA blinded to clinical parameters and outcomes. We used Cox proportional hazard regression to analyze time to first fracture adjusted for clinical risk factors (FRAX without BMD), osteoporosis treatment and BMD (hip or spine). RESULTS: Mean followup was 4.5years. 183 (5.1%) men sustain major osteoporotic fractures (MOF), 91 (2.5%) clinical vertebral fractures (CVF), and 46 (1.3%) hip fractures (HF). Correlation between spine BMD and spine TBS was modest (r=0.31), less than correlation between spine and hip BMD (r=0.63). Significantly lower spine TBS were found in fracture versus non-fracture men for MOF (p<0.001), HF (p<0.001) and CVF (p=0.003). Area under the receiver operating characteristic curve (AUC) for incident fracture discrimination with TBS was significantly better than chance (MOF AUC=0.59, p<0.001; HF AUC=0.67, p<0.001; CVF AUC=0.57, p=0.032). TBS predicted MOF and HF (but not CVF) in models adjusted for FRAX without BMD and osteoporosis treatment. TBS remained a predictor of HF (but not MOF) after further adjustment for hip BMD or spine BMD. CONCLUSION: We observed that spine TBS predicted MOF and HF independently of the clinical FRAX score, HF independently of FRAX and BMD in men. Studies with more incident fractures are needed to confirm these findings.
