Comparison of two protocols of periosteal distraction osteogenesis in a rabbit calvaria model

The regenerative pathways during periosteal distraction osteogenesis may be influenced by the local environment composed by cells, growth factors, nutrition and mechanical load. The aim of the present study was to evaluate the influence of two protocols of periosteal distraction on bone formation. Custom made distraction devices were surgically fixed onto the calvariae of 60 rabbits. After an initial healing period of 7 days, two groups of animals were submitted to distraction rates of 0.25 and 0.5 mm/24 h for 10 days, respectively. Six animals per group were sacrificed 10 (mid-distraction), 17 (end-distraction), 24 (1-week consolidation), 31 (2-week consolidation) and 77 days (2-month consolidation) after surgery. Newly formed bone was assessed by means of micro-CT and histologically. Expression of transcripts encoding tissue-specific genes (BMP-2, RUNX2, ACP5, SPARC, collagen I α1, collagen II α1 and SOX9) was analyzed by quantitative PCR. Two patterns of bone formation were observed, originating from the old bone surface in Group I and from the periosteum in Group II. Bone volume (BV) and bone mineral density (BMD) significantly increased up to the 2-month consolidation period within the groups (p < 0.05). Significantly more bone was observed in Group II compared to Group I at the 2-month consolidation period (p < 0.001). Expression of transcripts encoding osteogenic genes in bone depended on the time-point of observation (p < 0.05). Low level of transcripts reveals an indirect role of periosteum in the osteogenic process. Two protocols of periosteal distraction in the present model resulted in moderate differences in terms of bone formation.

Variable phenotypic expressivity in inbred retinal degeneration mouse lines: A comparative study of C3H/HeOu and FVB/N rd1 mice

PURPOSE Recent advances in optogenetics and gene therapy have led to promising new treatment strategies for blindness caused by retinal photoreceptor loss. Preclinical studies often rely on the retinal degeneration 1 (rd1 or Pde6b(rd1)) retinitis pigmentosa (RP) mouse model. The rd1 founder mutation is present in more than 100 actively used mouse lines. Since secondary genetic traits are well-known to modify the phenotypic progression of photoreceptor degeneration in animal models and human patients with RP, negligence of the genetic background in the rd1 mouse model is unwarranted. Moreover, the success of various potential therapies, including optogenetic gene therapy and prosthetic implants, depends on the progress of retinal degeneration, which might differ between rd1 mice. To examine the prospect of phenotypic expressivity in the rd1 mouse model, we compared the progress of retinal degeneration in two common rd1 lines, C3H/HeOu and FVB/N. METHODS We followed retinal degeneration over 24 weeks in FVB/N, C3H/HeOu, and congenic Pde6b(+) seeing mouse lines, using a range of experimental techniques including extracellular recordings from retinal ganglion cells, PCR quantification of cone opsin and Pde6b transcripts, in vivo flash electroretinogram (ERG), and behavioral optokinetic reflex (OKR) recordings. RESULTS We demonstrated a substantial difference in the speed of retinal degeneration and accompanying loss of visual function between the two rd1 lines. Photoreceptor degeneration and loss of vision were faster with an earlier onset in the FVB/N mice compared to C3H/HeOu mice, whereas the performance of the Pde6b(+) mice did not differ significantly in any of the tests. By postnatal week 4, the FVB/N mice expressed significantly less cone opsin and Pde6b mRNA and had neither ERG nor OKR responses. At 12 weeks of age, the retinal ganglion cells of the FVB/N mice had lost all light responses. In contrast, 4-week-old C3H/HeOu mice still had ERG and OKR responses, and we still recorded light responses from C3H/HeOu retinal ganglion cells until the age of 24 weeks. These results show that genetic background plays an important role in the rd1 mouse pathology. CONCLUSIONS Analogous to human RP, the mouse genetic background strongly influences the rd1 phenotype. Thus, different rd1 mouse lines may follow different timelines of retinal degeneration, making exact knowledge of genetic background imperative in all studies that use rd1 models.

Accuracy of linear measurements using three imaging modalities: two lateral cephalograms and one 3D model from CBCT data

BACKGROUND The aim of this study was to evaluate the accuracy of linear measurements on three imaging modalities: lateral cephalograms from a cephalometric machine with a 3 m source-to-mid-sagittal-plane distance (SMD), from a machine with 1.5 m SMD and 3D models from cone-beam computed tomography (CBCT) data. METHODS Twenty-one dry human skulls were used. Lateral cephalograms were taken, using two cephalometric devices: one with a 3 m SMD and one with a 1.5 m SMD. CBCT scans were taken by 3D Accuitomo® 170, and 3D surface models were created in Maxilim® software. Thirteen linear measurements were completed twice by two observers with a 4 week interval. Direct physical measurements by a digital calliper were defined as the gold standard. Statistical analysis was performed. RESULTS Nasion-Point A was significantly different from the gold standard in all methods. More statistically significant differences were found on the measurements of the 3 m SMD cephalograms in comparison to the other methods. Intra- and inter-observer agreement based on 3D measurements was slightly better than others. LIMITATIONS Dry human skulls without soft tissues were used. Therefore, the results have to be interpreted with caution, as they do not fully represent clinical conditions. CONCLUSIONS 3D measurements resulted in a better observer agreement. The accuracy of the measurements based on CBCT and 1.5 m SMD cephalogram was better than a 3 m SMD cephalogram. These findings demonstrated the linear measurements accuracy and reliability of 3D measurements based on CBCT data when compared to 2D techniques. Future studies should focus on the implementation of 3D cephalometry in clinical practice.

Quality of care for uninsured type 2 diabetic patients in a financial assistance medical home model

The medically uninsured population in the United States is 16% or 42 million people and consists of a significant number of Type 2 diabetic patients which is the predominant form of diabetes with 798,000 new cases diagnosed each year. There is limited health services research on uninsured populations concerning health system measures or specific disease conditions. ^ The purpose of this investigation was to determine the impact a newly implemented health care program had on the quality of care provided to patients with Type 2 diabetes. The primary study objective was to compare the quality of care while controlling for utilization, and health status of patients in the new program to their status during the previous financial assistance program. The research design was a retrospective matched-pairs design. The study population consisted of 225 patients who received medical care during 1996 and 1997 at the University Health System in San Antonio, Texas. ^ Six quality of care measures individually failed to demonstrate a statistically significant difference when compared between the two periods. However, an index measure reflecting the number of patients who received all six of the quality of care measures demonstrated a statistically significant increase in 1997 (p-value < 0.05). In 1996, 8 patients (2.6%) received all six medical management components. In 1997, 38 patients (16.8%) received all six medical management components. Four regression models were analyzed; two out of the four models demonstrated inconsistent results based on the program membership variable. ^ It is concluded that there has been a small effect of the Carelink program demonstrated by an increase from 8 to 38 patients receiving all quality of care components for Type 2 diabetics at the UHS. It is recommended that additional research be conducted in order to evaluate the quality of care provided to Type 2 diabetic patients. ^

An empirical model to estimate the demand for primary care in urban settings

Objective. To measure the demand for primary care and its associated factors by building and estimating a demand model of primary care in urban settings.^ Data source. Secondary data from 2005 California Health Interview Survey (CHIS 2005), a population-based random-digit dial telephone survey, conducted by the UCLA Center for Health Policy Research in collaboration with the California Department of Health Services, and the Public Health Institute between July 2005 and April 2006.^ Study design. A literature review was done to specify the demand model by identifying relevant predictors and indicators. CHIS 2005 data was utilized for demand estimation.^ Analytical methods. The probit regression was used to estimate the use/non-use equation and the negative binomial regression was applied to the utilization equation with the non-negative integer dependent variable.^ Results. The model included two equations in which the use/non-use equation explained the probability of making a doctor visit in the past twelve months, and the utilization equation estimated the demand for primary conditional on at least one visit. Among independent variables, wage rate and income did not affect the primary care demand whereas age had a negative effect on demand. People with college and graduate educational level were associated with 1.03 (p < 0.05) and 1.58 (p < 0.01) more visits, respectively, compared to those with no formal education. Insurance was significantly and positively related to the demand for primary care (p < 0.01). Need for care variables exhibited positive effects on demand (p < 0.01). Existence of chronic disease was associated with 0.63 more visits, disability status was associated with 1.05 more visits, and people with poor health status had 4.24 more visits than those with excellent health status. ^ Conclusions. The average probability of visiting doctors in the past twelve months was 85% and the average number of visits was 3.45. The study emphasized the importance of need variables in explaining healthcare utilization, as well as the impact of insurance, employment and education on demand. The two-equation model of decision-making, and the probit and negative binomial regression methods, was a useful approach to demand estimation for primary care in urban settings.^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Instrumental variable method for the causal relationship between soluble receptor for advanced glycation end-products (sRAGE) and risk of pancreatic cancer

This thesis project is motivated by the potential problem of using observational data to draw inferences about a causal relationship in observational epidemiology research when controlled randomization is not applicable. Instrumental variable (IV) method is one of the statistical tools to overcome this problem. Mendelian randomization study uses genetic variants as IVs in genetic association study. In this thesis, the IV method, as well as standard logistic and linear regression models, is used to investigate the causal association between risk of pancreatic cancer and the circulating levels of soluble receptor for advanced glycation end-products (sRAGE). Higher levels of serum sRAGE were found to be associated with a lower risk of pancreatic cancer in a previous observational study (255 cases and 485 controls). However, such a novel association may be biased by unknown confounding factors. In a case-control study, we aimed to use the IV approach to confirm or refute this observation in a subset of study subjects for whom the genotyping data were available (178 cases and 177 controls). Two-stage IV method using generalized method of moments-structural mean models (GMM-SMM) was conducted and the relative risk (RR) was calculated. In the first stage analysis, we found that the single nucleotide polymorphism (SNP) rs2070600 of the receptor for advanced glycation end-products (AGER) gene meets all three general assumptions for a genetic IV in examining the causal association between sRAGE and risk of pancreatic cancer. The variant allele of SNP rs2070600 of the AGER gene was associated with lower levels of sRAGE, and it was neither associated with risk of pancreatic cancer, nor with the confounding factors. It was a potential strong IV (F statistic = 29.2). However, in the second stage analysis, the GMM-SMM model failed to converge due to non- concaveness probably because of the small sample size. Therefore, the IV analysis could not support the causality of the association between serum sRAGE levels and risk of pancreatic cancer. Nevertheless, these analyses suggest that rs2070600 was a potentially good genetic IV for testing the causality between the risk of pancreatic cancer and sRAGE levels. A larger sample size is required to conduct a credible IV analysis.^

The two -state model of receptor activation: The agonist and the efficacy

The Two State model describes how drugs activate receptors by inducing or supporting a conformational change in the receptor from “off” to “on”. The beta 2 adrenergic receptor system is the model system which was used to formalize the concept of two states, and the mechanism of hormone agonist stimulation of this receptor is similar to ligand activation of other seven transmembrane receptors. Hormone binding to beta 2 adrenergic receptors stimulates the intracellular production of cyclic adenosine monophosphate (cAMP), which is mediated through the stimulatory guanyl nucleotide binding protein (Gs) interacting with the membrane bound enzyme adenylylcyclase (AC). ^ The effects of cAMP include protein phosphorylation, metabolic regulation and transcriptional regulation. The beta 2 adrenergic receptor system is the most well known of its family of G protein coupled receptors. Ligands have been scrutinized extensively in search of more effective therapeutic agents at this receptor as well as for insight into the biochemical mechanism of receptor activation. Hormone binding to receptor is thought to induce a conformational change in the receptor that increases its affinity for inactive Gs, catalyzes the release of GDP and subsequent binding of GTP and activation of Gs. ^ However, some beta 2 ligands are more efficient at this transformation than others, and the underlying mechanism for this drug specificity is not fully understood. The central problem in pharmacology is the characterization of drugs in their effect on physiological systems, and consequently, the search for a rational scale of drug effectiveness has been the effort of many investigators, which continues to the present time as models are proposed, tested and modified. ^ The major results of this thesis show that for many b2 -adrenergic ligands, the Two State model is quite adequate to explain their activity, but dobutamine (+/−3,4-dihydroxy-N-[3-(4-hydroxyphenyl)-1-methylpropyl]- b -phenethylamine) fails to conform to the predictions of the Two State model. It is a weak partial agonist, but it forms a large amount of high affinity complexes, and these complexes are formed at low concentrations much better than at higher concentrations. Finally, dobutamine causes the beta 2 adrenergic receptor to form high affinity complexes at a much faster rate than can be accounted for by its low efficiency activating AC. Because the Two State model fails to predict the activity of dobutamine in three different ways, it has been disproven in its strictest form. ^

Two intertidal, non-calcifying macroalgae (Palmaria palmata and Saccharina latissima) show complex and variable responses to short-term CO2 acidification

Ocean acidification, the result of increased dissolution of carbon dioxide (CO2) in seawater, is a leading subject of current research. The effects of acidification on non-calcifying macroalgae are, however, still unclear. The current study reports two 1-month studies using two different macroalgae, the red alga Palmaria palmata (Rhodophyta) and the kelp Saccharina latissima (Phaeophyta), exposed to control (pHNBS = 8.04) and increased (pHNBS = 7.82) levels of CO2-induced seawater acidification. The impacts of both increased acidification and time of exposure on net primary production (NPP), respiration (R), dimethylsulphoniopropionate (DMSP) concentrations, and algal growth have been assessed. In P. palmata, although NPP significantly increased during the testing period, it significantly decreased with acidification, whereas R showed a significant decrease with acidification only. S. latissima significantly increased NPP with acidification but not with time, and significantly increased R with both acidification and time, suggesting a concomitant increase in gross primary production. The DMSP concentrations of both species remained unchanged by either acidification or through time during the experimental period. In contrast, algal growth differed markedly between the two experiments, in that P. palmata showed very little growth throughout the experiment, while S. latissima showed substantial growth during the course of the study, with the latter showing a significant difference between the acidified and control treatments. These two experiments suggest that the study species used here were resistant to a short-term exposure to ocean acidification, with some of the differences seen between species possibly linked to different nutrient concentrations between the experiments.

Two-way traveltimes of internal layers and ice thickness between Dome C - Vostok - Dome A

Chinese scientists will start to drill a deep ice core at Kunlun station near Dome A in the near future. Recent work has predicted that Dome A is a location where ice older than 1 million years can be found. We model flow, temperature and the age of the ice by applying a three-dimensional, thermomechanically coupled full-Stokes model to a 70 × 70 km**2 domain around Kunlun station, using isotropic non-linear rheology and different prescribed anisotropic ice fabrics that vary the evolution from isotropic to single maximum at 1/3 or 2/3 depths. The variation in fabric is about as important as the uncertainties in geothermal heat flux in determining the vertical advection which in consequence controls both the basal temperature and the age profile. We find strongly variable basal ages across the domain since the ice varies greatly in thickness, and any basal melting effectively removes very old ice in the deepest parts of the subglacial valleys. Comparison with dated radar isochrones in the upper one third of the ice sheet cannot sufficiently constrain the age of the deeper ice, with uncertainties as large as 500 000 years in the basal age. We also assess basal age and thermal state sensitivities to geothermal heat flux and surface conditions. Despite expectations of modest changes in surface height over a glacial cycle at Dome A, even small variations in the evolution of surface conditions cause large variation in basal conditions, which is consistent with basal accretion features seen in radar surveys.

Two-dimensional (2-D) peat microtopographical model

Matlab script file of a two-dimensional (2-D) peat microtopographical model together with other supplementary files that are required to run the model.

Age determination and model of sediment core RAPiD-15-4P

Greenland ice core records indicate that the last deglaciation (~7-21 ka) was punctuated by numerous abrupt climate reversals involving temperature changes of up to 5°C-10°C within decades. However, the cause behind many of these events is uncertain. A likely candidate may have been the input of deglacial meltwater, from the Laurentide ice sheet (LIS), to the high-latitude North Atlantic, which disrupted ocean circulation and triggered cooling. Yet the direct evidence of meltwater input for many of these events has so far remained undetected. In this study, we use the geochemistry (paired Mg/Ca-d18O) of planktonic foraminifera from a sediment core south of Iceland to reconstruct the input of freshwater to the northern North Atlantic during abrupt deglacial climate change. Our record can be placed on the same timescale as ice cores and therefore provides a direct comparison between the timing of freshwater input and climate variability. Meltwater events coincide with the onset of numerous cold intervals, including the Older Dryas (14.0 ka), two events during the Allerød (at ~13.1 and 13.6 ka), the Younger Dryas (12.9 ka), and the 8.2 ka event, supporting a causal link between these abrupt climate changes and meltwater input. During the Bølling-Allerød warm interval, we find that periods of warming are associated with an increased meltwater flux to the northern North Atlantic, which in turn induces abrupt cooling, a cessation in meltwater input, and eventual climate recovery. This implies that feedback between climate and meltwater input produced a highly variable climate. A comparison to published data sets suggests that this feedback likely included fluctuations in the southern margin of the LIS causing rerouting of LIS meltwater between southern and eastern drainage outlets, as proposed by Clark et al. (2001, doi:10.1126/science.1062517).

A two-dimensional finite element model of front surface current flow in cells under non-uniform, concentrated illumination

A two-dimensional finite element model of current flow in the front surface of a PV cell is presented. In order to validate this model we perform an experimental test. Later, particular attention is paid to the effects of non-uniform illumination in the finger direction which is typical in a linear concentrator system. Fill factor, open circuit voltage and efficiency are shown to decrease with increasing degree of non-uniform illumination. It is shown that these detrimental effects can be mitigated significantly by reoptimization of the number of front surface metallization fingers to suit the degree of non-uniformity. The behavior of current flow in the front surface of a cell operating at open circuit voltage under non-uniform illumination is discussed in detail.