Managing Incidental Genomic Findings in Clinical Trials: Fulfillment of the Principle of Justice

<p>Managing Incidental Genomic Findings in Clinical Trials: Fulfillment of the Principle of Justice</p

Disentangling astroglial physiology with a realistic cell model in silico

Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we systematically incorporate multi-scale, tri-dimensional astroglial architecture into a realistic multi-compartmental cell model, which we constrain by empirical tests and integrate into the NEURON computational biophysical environment. This approach is implemented as a flexible astrocyte-model builder ASTRO. As a proof-of-concept, we explore an in silico astrocyte to evaluate basic cell physiology features inaccessible experimentally. Our simulations suggest that currents generated by glutamate transporters or K+ channels have negligible distant effects on membrane voltage and that individual astrocytes can successfully handle extracellular K+ hotspots. We show how intracellular Ca2+ buffers affect Ca2+ waves and why the classical Ca2+ sparks-and-puffs mechanism is theoretically compatible with common readouts of astroglial Ca2+ imaging

Identifying the science and technology dimensions of emerging public policy issues through horizon scanning

Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique [1]. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security.Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique [1]. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security

Enriching for correct prediction of biological processes using a combination of diverse classifiers

<p>Abstract</p> <p>Background</p> <p>Machine learning models (classifiers) for classifying genes to biological processes each have their own unique characteristics in what genes can be classified and to what biological processes. No single learning model is qualitatively superior to any other model and overall precision for each model tends to be low. The classification results for each classifier can be complementary and synergistic suggesting the benefit of a combination of algorithms, but often the prediction probability outputs of various learning models are neither comparable nor compatible for combining. A means to compare outputs regardless of the model and data used and combine the results into an improved comprehensive model is needed.</p> <p>Results</p> <p>Gene expression patterns from NCI's panel of 60 cell lines were used to train a Random Forest, a Support Vector Machine and a Neural Network model, plus two over-sampled models for classifying genes to biological processes. Each model produced unique characteristics in the classification results. We introduce the Precision Index measure (PIN) from the maximum posterior probability that allows assessing, comparing and combining multiple classifiers. The class specific precision measure (PIC) is introduced and used to select a subset of predictions across all classes and all classifiers with high precision. We developed a single classifier that combines the PINs from these five models in prediction and found that the PIN Combined Classifier (PINCom) significantly increased the number of correctly predicted genes over any single classifier. The PINCom applied to test genes that were not used in training also showed substantial improvement over any single model.</p> <p>Conclusions</p> <p>This paper introduces novel and effective ways of assessing predictions by their precision and recall plus a method that combines several machine learning models and capitalizes on synergy and complementation in class selection, resulting in higher precision and recall. Different machine learning models yielded incongruent results each of which were successfully combined into one superior model using the PIN measure we developed. Validation of the boosted predictions for gene functions showed the genes to be accurately predicted.</p

X-ray Absorption and Reflection in Active Galactic Nuclei

X-ray spectroscopy offers an opportunity to study the complex mixture of emitting and absorbing components in the circumnuclear regions of active galactic nuclei, and to learn about the accretion process that fuels AGN and the feedback of material to their host galaxies. We describe the spectral signatures that may be studied and review the X-ray spectra and spectral variability of active galaxies, concentrating on progress from recent Chandra, XMM-Newton and Suzaku data for local type 1 AGN. We describe the evidence for absorption covering a wide range of column densities, ionization and dynamics, and discuss the growing evidence for partial-covering absorption from data at energies > 10 keV. Such absorption can also explain the observed X-ray spectral curvature and variability in AGN at lower energies and is likely an important factor in shaping the observed properties of this class of source. Consideration of self-consistent models for local AGN indicates that X-ray spectra likely comprise a combination of absorption and reflection effects from material originating within a few light days of the black hole as well as on larger scales. It is likely that AGN X-ray spectra may be strongly affected by the presence of disk-wind outflows that are expected in systems with high accretion rates, and we describe models that attempt to predict the effects of radiative transfer through such winds, and discuss the prospects for new data to test and address these ideas.Comment: Accepted for publication in the Astronomy and Astrophysics Review. 58 pages, 9 figures. V2 has fixed an error in footnote

Caregiving process and caregiver burden: Conceptual models to guide research and practice

BACKGROUND: Parental care for a child with a developmental disability is an enormous responsibility, one that can far exceed that of typical parental care. While most parents adapt well to the situation of caring for a child with a disability, some do not. To understand parents' adaptations to their children's disabilities, the complex nature of stress processes must be accounted for and the constructs and factors that play a role in the caregiving must be considered. DISCUSSION: Evidence suggests that there is considerable variation in how caregivers adapt to their caregiving demands. Many studies have sought to qualify the association between caregiving and health outcomes of the caregivers. Contextual factors such as SES, child factors such as child behaviour problems and severity of disability, intra-psychic factors such as mastery and self-esteem, coping strategies and social supports have all been associated with psychological and/or physical outcome or parents or primary caregivers. In reviewing these issues, the literature appears to be limited by the use of traditional analytic approaches which examine the relationship between a factor and an outcome. It is clear, however, that changes to single factors, as represented in these studies, occur very rarely even in the experimental context. The literature has also been limited by lack of reliance on specific theoretical frameworks. SUMMARY: This conceptual paper documents the state of current knowledge and explores the current theoretical frameworks that have been used to describe the caregiving process from two diverse fields, pediatrics and geriatrics. Integration of these models into one comprehensive model suitable for this population of children with disabilities and their caregivers is proposed. This model may guide future research in this area

Self-Reported and Actual Beta-Blocker Prescribing for Heart Failure Patients: Physician Predictors

Beta-blockers reduce mortality among patients with systolic heart failure (HF), yet primary care provider prescription rates remain low.To examine the association between primary care physician characteristics and both self-reported and actual prescription of beta-blockers among patients with systolic HF.Cross-sectional survey with supplementary retrospective chart review.Primary care providers at three New York City Veterans Affairs medical centers.Main outcomes were: 1) self-reported prescribing of beta-blockers, and 2) actual prescribing of beta-blockers among HF patients. Physician HF practice patterns and confidence levels, as well as socio-demographic and clinical characteristics, were also assessed.Sixty-nine of 101 physicians (68%) completed the survey examining self-reported prescribing of beta-blockers. Physicians who served as inpatient ward attendings self-reported significantly higher rates of beta-blocker prescribing among their HF patients when compared with physicians who did not attend (78% vs. 58%; p = 0.002), as did physicians who were very confident in managing HF patients when compared with physicians who were not (82% vs. 68%; p = 0.009). Fifty-one of these 69 surveyed physicians (74%) were successfully matched to 287 HF patients for whom beta-blocker prescribing data was available. Physicians with greater self-reported rates of prescribing beta-blockers were significantly more likely to actually prescribe beta-blockers (p = 0.02); however, no other physician characteristics were significantly associated with actual prescribing of beta-blockers among HF patients.Physician teaching responsibilities and confidence levels were associated with self-reported beta-blocker prescribing among their HF patients. Educational efforts focused on improving confidence levels in HF care and increasing exposure to teaching may improve beta-blocker presciption in HF patients managed in primary care

The influence of tumor size and environment on gene expression in commonly used human tumor lines

BACKGROUND: The expression profiles of solid tumor models in rodents have been only minimally studied despite their extensive use to develop anticancer agents. We have applied RNA expression profiling using Affymetrix U95A GeneChips to address fundamental biological questions about human tumor lines. METHODS: To determine whether gene expression changed significantly as a tumor increased in size, we analyzed samples from two human colon carcinoma lines (Colo205 and HCT-116) at three different sizes (200 mg, 500 mg and 1000 mg). To investigate whether gene expression was influenced by the strain of mouse, tumor samples isolated from C.B-17 SCID and Nu/Nu mice were also compared. Finally, the gene expression differences between tissue culture and in vivo samples were investigated by comparing profiles from lines grown in both environments. RESULTS: Multidimensional scaling and analysis of variance demonstrated that the tumor lines were dramatically different from each other and that gene expression remained constant as the tumors increased in size. Statistical analysis revealed that 63 genes were differentially expressed due to the strain of mouse the tumor was grown in but the function of the encoded proteins did not link to any distinct biological pathways. Hierarchical clustering of tissue culture and xenograft samples demonstrated that for each individual tumor line, the in vivo and in vitro profiles were more similar to each other than any other profile. We identified 36 genes with a pattern of high expression in xenograft samples that encoded proteins involved in extracellular matrix, cell surface receptors and transcription factors. An additional 17 genes were identified with a pattern of high expression in tissue culture samples and encoded proteins involved in cell division, cell cycle and RNA production. CONCLUSIONS: The environment a tumor line is grown in can have a significant effect on gene expression but tumor size has little or no effect for subcutaneously grown solid tumors. Furthermore, an individual tumor line has an RNA expression pattern that clearly defines it from other lines even when grown in different environments. This could be used as a quality control tool for preclinical oncology studies