Delivery of the improved BMP-2-Advanced plasmid DNA within a gene-activated scaffold accelerates mesenchymal stem cell osteogenesis and critical size defect repair.

Gene-activated scaffolds have been shown to induce controlled, sustained release of functional transgene both in vitro and in vivo. Bone morphogenetic proteins (BMPs) are potent mediators of osteogenesis however we found that the delivery of plasmid BMP-2 (pBMP-2) alone was not sufficient to enhance bone formation. Therefore, the aim of this study was to assess if the use of a series of modified BMP-2 plasmids could enhance the functionality of a pBMP-2 gene-activated scaffold and ultimately improve bone regeneration when implanted into a critical sized bone defect in vivo. A multi-cistronic plasmid encoding both BMP-2 and BMP-7 (BMP-2/7) was employed as was a BMP-2-Advanced plasmid containing a highly truncated intron sequence. With both plasmids, the highly efficient cytomegalovirus (CMV) promoter sequence was used. However, as there have been reports that the elongated factor 1-α promoter is more efficient, particularly in stem cells, a BMP-2-Advanced plasmid containing the EF1α promoter was also tested. Chitosan nanoparticles (CS) were used to deliver each plasmid to MSCs and induced transient up-regulation of BMP-2 protein expression, in turn significantly enhancing MSC-mediated osteogenesis when compared to untreated controls (p < 0.001). When incorporated into a bone mimicking collagen-hydroxyapatite scaffold, the BMP-2-Advanced plasmid, under the control of the CMV promotor, induced MSCs to produce approximately 2500 μg of calcium per scaffold, significantly higher (p < 0.001) than all other groups. Just 4 weeks post-implantation in vivo, this cell-free gene-activated scaffold induced significantly more bone tissue formation compared to a pBMP-2 gene-activated scaffold (p < 0.001) as indicated by microCT and histomorphometry. Immunohistochemistry revealed that the BMP-2-Advanced plasmid accelerated differentiation of osteoprogenitor cells to mature osteoblasts, thus causing rapid healing of the bone defects. This study confirms that optimising the plasmid construct can enhance the functionality of gene-activated scaffolds and translate to accelerated bone formation in a critical sized defect

Comparing families of dynamic causal models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data

Genetic properties of feed efficiency parameters in meat-type chickens

<p>Abstract</p> <p>Background</p> <p>Feed cost constitutes about 70% of the cost of raising broilers, but the efficiency of feed utilization has not kept up the growth potential of today's broilers. Improvement in feed efficiency would reduce the amount of feed required for growth, the production cost and the amount of nitrogenous waste. We studied residual feed intake (RFI) and feed conversion ratio (FCR) over two age periods to delineate their genetic inter-relationships.</p> <p>Methods</p> <p>We used an animal model combined with Gibb sampling to estimate genetic parameters in a pedigreed random mating broiler control population.</p> <p>Results</p> <p>Heritability of RFI and FCR was 0.42-0.45. Thus selection on RFI was expected to improve feed efficiency and subsequently reduce feed intake (FI). Whereas the genetic correlation between RFI and body weight gain (BWG) at days 28-35 was moderately positive, it was negligible at days 35-42. Therefore, the timing of selection for RFI will influence the expected response. Selection for improved RFI at days 28-35 will reduce FI, but also increase growth rate. However, selection for improved RFI at days 35-42 will reduce FI without any significant change in growth rate. The nature of the pleiotropic relationship between RFI and FCR may be dependent on age, and consequently the molecular factors that govern RFI and FCR may also depend on stage of development, or on the nature of resource allocation of FI above maintenance directed towards protein accretion and fat deposition. The insignificant genetic correlation between RFI and BWG at days 35-42 demonstrates the independence of RFI on the level of production, thereby making it possible to study the molecular, physiological and nutrient digestibility mechanisms underlying RFI without the confounding effects of growth. The heritability estimate of FCR was 0.49 and 0.41 for days 28-35 and days 35-42, respectively.</p> <p>Conclusion</p> <p>Selection for FCR will improve efficiency of feed utilization but because of the genetic dependence of FCR and its components, selection based on FCR will reduce FI and increase growth rate. However, the correlated responses in both FI and BWG cannot be predicted accurately because of the inherent problem of FCR being a ratio trait.</p

An intervention program to reduce the number of hospitalizations of elderly patients in a primary care clinic

<p>Abstract</p> <p>Background</p> <p>The elderly population consumes a large share of medical resources in the western world. A significant portion of the expense is related to hospitalizations.</p> <p>Objectives</p> <p>To evaluate an intervention program designed to reduce the number of hospitalization of elderly patients by a more optimal allocation of resources in primary care.</p> <p>Methods</p> <p>A multidimensional intervention program was conducted that included the re-engineering of existing work processes with a focus on the management of patient problems, improving communication with outside agencies, and the establishment of a system to monitor quality of healthcare parameters. Data on the number of hospitalizations and their cost were compared before and after implementation of the intervention program.</p> <p>Results</p> <p>As a result of the intervention the mean expenditure per elderly patient was reduced by 22.5%. The adjusted number of hospitalizations/1,000 declined from 15.1 to 10.7 (29.3%). The number of adjusted hospitalization days dropped from 132 to 82 (37.9%) and the mean hospitalization stay declined from 8.2 to 6.7 days (17.9%). The adjusted hospitalization cost ($/1,000 patients) dropped from$32,574 to $18,624 (42.8%). The overall clinic expense, for all age groups, dropped by 9.9%.</p> <p>Conclusion</p> <p>Implementation of the intervention program in a single primary care clinic led to a reduction in hospitalizations for the elderly patient population and to a more optimal allocation of healthcare resources.</p

Who Benefits Most from a University Degree?: A Cross-National Comparison of Selection and Wage Returns in the US, UK, and Germany

Recent research on economic returns to higher education in the United States suggests that those with the highest wage returns to a college degree are least likely to obtain one. We extend the study of heterogeneous returns to tertiary education across multiple institutional contexts, investigating how the relationship between wage returns and the propensity to complete a degree varies by the level of expansion, differentiation, and cost of higher education. Drawing on panel data and matching techniques, we compare findings from the US with selection into degree completion in Germany and the UK. Contrary to previous studies, we find little evidence for population level heterogeneity in economic returns to higher education

Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

BACKGROUND: We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox's proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso. RESULTS: Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk) along with the respective sets of compound markers. CONCLUSIONS: The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers

Implementing the Creating Learning Environments for Compassionate Care (CLECC) programme in acute hospital settings: a pilot RCT and feasibility study

BACKGROUND: Concerns about the degree of compassion in health care have become a focus for national and international attention. However, existing research on compassionate care interventions provides scant evidence of effectiveness or the contexts in which effectiveness is achievable. OBJECTIVES: To assess the feasibility of implementing the Creating Learning Environments for Compassionate Care (CLECC) programme in acute hospital settings and to evaluate its impact on patient care. DESIGN: Pilot cluster randomised trial (CRT) and associated process and economic evaluations. SETTING: Six inpatient ward nursing teams (clusters) in two English NHS hospitals randomised to intervention (n = 4) or control (n = 2). PARTICIPANTS: Patients (n = 639), staff (n = 211) and visitors (n = 188). INTERVENTION: CLECC is a workplace educational intervention focused on developing sustainable leadership and work team practices (dialogue, reflective learning, mutual support) theorised to support the delivery of compassionate care. The control setting involved no planned staff team-based educational activity. MAIN OUTCOME MEASURES: Quality of Interaction Schedule (QuIS) for staff–patient interactions, patient-reported evaluations of emotional care in hospital (PEECH) and nurse-reported empathy (as assessed via the Jefferson Scale of Empathy). DATA SOURCES: Structured observations of staff–patient interactions; patient, visitor and staff questionnaires and qualitative interviews; and qualitative observations of CLECC activities. RESULTS: The pilot CRT proceeded as planned and randomisation was acceptable to teams. There was evidence of potential contamination between wards in the same hospital. QuIS performed well, achieving a 93% recruitment rate, with 25% of the patient sample cognitively impaired. At follow-up there were more positive (78% vs. 74%) and fewer negative (8% vs. 11%) QuIS ratings for intervention wards than for control wards. In total, 63% of intervention ward patients achieved the lowest possible (i.e. more negative) scores on the PEECH connection subscale, compared with 79% of control group patients. These differences, although supported by the qualitative findings, are not statistically significant. No statistically significant differences in nursing empathy were observed, although response rates to staff questionnaire were low (36%). Process evaluation: the CLECC intervention is feasible to implement in practice with medical and surgical nursing teams in acute care hospitals. Strong evidence of good staff participation was found in some CLECC activities and staff reported benefits throughout its introductory period and beyond. Further impact and sustainability were limited by the focus on changing ward team behaviours rather than wider system restructuring. Economic evaluation: the costs associated with using CLECC were identified and it is recommend that an impact inventory be used in any future study. LIMITATIONS: Findings are not generalisable outside hospital nursing teams, and this feasibility work is not powered to detect differences attributable to the CLECC intervention. CONCLUSIONS: Use of the experimental methods is feasible. The use of structured observation of staff–patient interaction quality is a promising primary outcome that is inclusive of patient groups often excluded from research, but further validation is required. Further development of the CLECC intervention should focus on ensuring that it is adequately supported by resources, norms and relationships in the wider system by, for instance, improving the cognitive participation of senior nurse managers. Funding is being sought for a more definitive evaluation. TRIAL REGISTRATION: Current Controlled Trials ISRCTN16789770. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 6, No. 33. See the NIHR Journals Library website for further project information. The systematic review reported in Chapter 2 was funded by the NIHR Collaboration for Leadership in Applied Health Research and Care Wessex, the University of Örebro and the Karolinska Institutet

Human Cytomegalovirus Impairs the Function of Plasmacytoid Dendritic Cells in Lymphoid Organs

Human dendritic cells (DCs) are the main antigen presenting cells (APC) and can be divided into two main populations, myeloid and plasmacytoid DCs (pDCs), the latter being the main producers of Type I Interferon. The vast majority of pDCs can be found in lymphoid organs, where the main pool of all immune cells is located, but a minority of pDCs also circulate in peripheral blood. Human cytomegalovirus (HCMV) employs multiple mechanisms to evade the immune system. In this study, we could show that pDCs obtained from lymphoid organs (tonsils) (tpDCs) and from blood (bpDCs) are different subpopulations in humans. Interestingly, these populations react in opposite manner to HCMV-infection. TpDCs were fully permissive for HCMV. Their IFN-α production and the expression of costimulatory and adhesion molecules were altered after infection. In contrast, in bpDCs HCMV replication was abrogated and the cells were activated with increased IFN-α production and upregulation of MHC class I, costimulatory, and adhesion molecules. HCMV-infection of both, tpDCs and bpDCs, led to a decreased T cell stimulation, probably mediated through a soluble factor produced by HCMV-infected pDCs. We propose that the HCMV-mediated impairment of tpDCs is a newly discovered mechanism selectively targeting the host's major population of pDCs residing in lymphoid organs

Applying Bayesian model averaging for uncertainty estimation of input data in energy modelling

Background Energy scenarios that are used for policy advice have ecological and social impact on society. Policy measures that are based on modelling exercises may lead to far reaching financial and ecological consequences. The purpose of this study is to raise awareness that energy modelling results are accompanied with uncertainties that should be addressed explicitly. Methods With view to existing approaches of uncertainty assessment in energy economics and climate science, relevant requirements for an uncertainty assessment are defined. An uncertainty assessment should be explicit, independent of the assessor&#8217;s expertise, applicable to different models, including subjective quantitative and statistical quantitative aspects, intuitively understandable and be reproducible. Bayesian model averaging for input variables of energy models is discussed as method that satisfies these requirements. A definition of uncertainty based on posterior model probabilities of input variables to energy models is presented. Results The main findings are that (1) expert elicitation as predominant assessment method does not satisfy all requirements, (2) Bayesian model averaging for input variable modelling meets the requirements and allows evaluating a vast amount of potentially relevant influences on input variables and (3) posterior model probabilities of input variable models can be translated in uncertainty associated with the input variable. Conclusions An uncertainty assessment of energy scenarios is relevant if policy measures are (partially) based on modelling exercises. Potential implications of these findings include that energy scenarios could be associated with uncertainty that is presently neither assessed explicitly nor communicated adequately