An optimized in vitro model of the respiratory tract wall to study particle cell interactions

As a part of the respiratory tissue barrier, lung epithelial cells play an important role against the penetration of the body by inhaled particulate foreign materials. In most cell culture models, which are designed to study particle-cell interactions, the cells are immersed in medium. This does not reflect the physiological condition of lung epithelial cells which are exposed to air, separated from it only by a very thin liquid lining layer with a surfactant film at the air-liquid interface. In this study, A549 epithelial cells were grown on microporous membranes in a two chamber system. After the formation of a confluent monolayer the cells were exposed to air. The morphology of the cells and the expression of tight junction proteins were studied with confocal laser scanning and transmission electron microscopy. Air-exposed cells maintained monolayer structure for 2 days, expressed tight junctions and developed transepithelial electrical resistance. Surfactant was produced and released at the apical side of the air-exposed epithelial cells. In order to study particle-cell interactions fluorescent 1 microm polystyrene particles were sprayed over the epithelial surface. After 4 h, 8.8% of particles were found inside the epithelium. This fraction increased to 38% after 24 h. During all observations, particles were always found in the cells but never between them. In this study, we present an in vitro model of the respiratory tract wall consisting of air-exposed lung epithelial cells covered by a liquid lining layer with a surfactant film to study particle-cell interactions.

Statistical inference for the optimal approximating model

In the setting of high-dimensional linear models with Gaussian noise, we investigate the possibility of confidence statements connected to model selection. Although there exist numerous procedures for adaptive (point) estimation, the construction of adaptive confidence regions is severely limited (cf. Li in Ann Stat 17:1001–1008, 1989). The present paper sheds new light on this gap. We develop exact and adaptive confidence regions for the best approximating model in terms of risk. One of our constructions is based on a multiscale procedure and a particular coupling argument. Utilizing exponential inequalities for noncentral χ2-distributions, we show that the risk and quadratic loss of all models within our confidence region are uniformly bounded by the minimal risk times a factor close to one.

An Emerging Technology: To Model Patient Education for the Dental Hygiene Student

Introduction: Could Livestream Video, an emerging audiovisual media, be used more effectively than the traditional demonstration method of teaching or modeling dental hygiene students on how to provide chairside patient education? [See PDF for complete abstract]

Social Support at the Workplace, Motivation to Transfer, and Training Transfer: A Multi-Level Indirect Effects Model

Supervisor support, peer support and transfer motivation have been identified as important predictors for training transfer. Transfer motivation is supposed to mediate the support–training transfer relationship. Especially after team training interventions that include all team members (i.e., intact-team training), individual perception of these factors might be shared among team members. However, an integration of the team level in the training transfer process is rare, yet still needed. Analyzing 194 employees from 34 teams in the context of intact-team training interventions, we found similar relationships and processes at both levels of analysis: Social support enhances transfer motivation at the individual and team levels. Furthermore, motivation to transfer increases training transfer and serves as a connecting mechanism in the social support–training transfer link. The results underline the importance of (1) considering multiple levels in theories and research about the training transfer process and (2) ensuring the practice of individual-directed support and a shared, supportive climate within teams.

Molecular Consequences of the SERPINH1/HSP47 Mutation in the Dachshund Natural Model of Osteogenesis Imperfecta.

Osteogenesis imperfecta (OI) is a heritable connective tissue disease characterized by bone fragility and increased risk of fractures. Up to now, mutations in at least 18 genes have been associated with dominant and recessive forms of OI that affect the production or post-translational processing of procollagen or alter bone homeostasis. Among those, SERPINH1 encoding heat shock protein 47 (HSP47), a chaperone exclusive for collagen folding in the ER, was identified to cause a severe form of OI in dachshunds (L326P) as well as in humans (one single case with a L78P mutation). To elucidate the disease mechanism underlying OI in the dog model, we applied a range of biochemical assays to mutant and control skin fibroblasts as well as on bone samples. These experiments revealed that type I collagen synthesized by mutant cells had decreased electrophoretic mobility. Procollagen was retained intracellularly with concomitant dilation of ER cisternae and activation of the ER stress response markers GRP78 and phospho-eIF2α, thus suggesting a defect in procollagen processing. In line with the migration shift detected on SDS-PAGE of cell culture collagen, extracts of bone collagen from the OI dog showed a similar mobility shift, and on tandem mass spectrometry, the chains were post-translationally overmodified. The bone collagen had a higher content of pyridinoline than control dog bone. We conclude that the SERPINH1 mutation in this naturally occurring model of OI impairs how HSP47 acts as a chaperone in the ER. This results in abnormal post-translational modification and cross-linking of the bone collagen.

Development and characterization of a scaffold-free 3D spheroid model of iPSC-derived human cardiomyocytes

Purpose: Cardiomyocytes are terminally differentiated cells in the adult heart and ischemia and cardiotoxic compounds can lead to cell death and irreversible decline of cardiac function. As testing platforms, isolated organs and primary cells from rodents have been the standard in research and toxicology, but there is a need for better models that more faithfully recapitulate native human biology. Hence, a new in vitro model comprising the advantages of 3D cell culture and the availability of induced pluripotent stem cells (iPSC) from human origin was developed and characterized. Methods: Human cardiomyocytes (CMs) derived from induced pluripotent stem cells (iPSCs) were studied in standard 2D culture and as cardiac microtissues (MTs) formed in hanging drops. 2D cultures were examined using immunofluorescence microscopy and Western blotting while the cardiac MTs were subjected to immunofluorescence, contractility, and pharmacological investigations. Results: iPSC-derived CMs in 2D culture showed well-formed myofibrils, cell-cell contacts positive for connexin-43, and other typical cardiac proteins. The cells reacted to pro-hypertrophic growth factors with a substantial increase in myofibrils and sarcomeric proteins. In hanging drop cultures, iPSC-derived cardiomyocytes formed spheroidal MTs within 4 days showing a homogeneous tissue structure with well-developed myofibrils extending throughout the whole spheroid without a necrotic core. MTs showed spontaneous contractions for more than 4 weeks that were recorded by optical motion tracking, sensitive to temperature, and responsive to electrical pacing. Contractile pharmacology was tested with several agents known to modulate cardiac rate and viability. Calcium-transients underlay the contractile activity and were also responsive to electrical stimulation, caffeine-induced Ca2+-release, extracellular calcium levels. Conclusions: 3D culture using iPSC-derived human cardiomyocytes provides an organoid human-based cellular platform that is free of necrosis and recapitulates vital cardiac functionality, thereby providing new and promising relevant model for the evaluation and development of new therapies and detection of cardiotoxicity.

Roadmap for implementing the Chronic Care Model for weight management: Healthcare Institution's Response to Obesity (HIRO)

Background. Excess weight and obesity are at epidemic proportions in the United States and place individuals at increased risk for a variety of chronic conditions. Rates of diabetes, high blood pressure, coronary artery disease, stroke, cancer, and arthritis are all influenced by the presence of obesity. Small reductions in excess weight can produce significant positive clinical outcomes. Healthcare organizations have a vital role to play in the identification and management of obesity. Currently, healthcare providers do not adequately diagnose and manage excess weight in patients. Lack of skill, time, and knowledge are commonly cited as reasons for non-adherence to recommended standards of care. The Chronic Care Model offers an approach to healthcare organizations for chronic disease management. The model consists of six elements that work together to empower both providers and patients to have more productive interactions: the community, the health system itself, self-management support, delivery system design, decision support, and clinical information systems. The model and its elements may offer a framework through which healthcare organizations can adapt to support, educate, and empower providers and patients in the management of excess weight and obesity. Successful management of excess weight will reduce morbidity and mortality of many chronic conditions. Purpose. The purpose of this review is to synthesize existing research on the effectiveness of the Chronic Care Model and its elements as they relate to weight management and behaviors associated with maintaining a healthy weight. Methods: A narrative review of the literature between November 1998 and November 2008 was conducted. The review focused on clinical trials, systematic reviews, and reports related to the chronic care model or its elements and weight management, physical activity, nutrition, or diabetes. Fifty-nine articles are included in the review. Results. This review highlights the use of the Chronic Care Model and its elements that can result in improved quality of care and clinical outcomes related to weight management, physical activity, nutrition, and diabetes. Conclusions. Healthcare organizations can use the Chronic Care Model framework to implement changes within their systems to successfully address overweight and obesity in their patient populations. Specific recommendations for operationalizing the Chronic Care Model elements for weight management are presented.^

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 Bayesian approach to estimating the regression coefficients of a multinomial logit model

A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ordinal scale response categories is presented. A Monte Carlo method is used to construct the posterior distribution of the link function. The link function is treated as an arbitrary scalar function. Then the Gauss-Markov theorem is used to determine a function of the link which produces a random vector of coefficients. The posterior distribution of the random vector of coefficients is used to estimate the regression coefficients. The method described is referred to as a Bayesian generalized least square (BGLS) analysis. Two cases involving multinominal logit models are described. Case I involves a cumulative logit model and Case II involves a proportional-odds model. All inferences about the coefficients for both cases are described in terms of the posterior distribution of the regression coefficients. The results from the BGLS method are compared to maximum likelihood estimates of the regression coefficients. The BGLS method avoids the nonlinear problems encountered when estimating the regression coefficients of a generalized linear model. The method is not complex or computationally intensive. The BGLS method offers several advantages over Bayesian approaches. ^