The medium-term sustainability of organisational innovations in the national health service

Background: There is a growing recognition of the importance of introducing new ways of working into the UK's National Health Service (NHS) and other health systems, in order to ensure that patient care is provided as effectively and efficiently as possible. Researchers have examined the challenges of introducing new ways of working-'organisational innovations'-into complex organisations such as the NHS, and this has given rise to a much better understanding of how this takes place-and why seemingly good ideas do not always result in changes in practice. However, there has been less research on the medium-and longer-term outcomes for organisational innovations and on the question of how new ways of working, introduced by frontline clinicians and managers, are sustained and become established in day-to-day practice. Clearly, this question of sustainability is crucial if the gains in patient care that derive from organisational innovations are to be maintained, rather than lost to what the NHS Institute has called the 'improvement-evaporation effect'. Methods: The study will involve research in four case-study sites around England, each of which was successful in sustaining its new model of service provision beyond an initial period of pilot funding for new genetics services provided by the Department of Health. Building on findings relating to the introduction and sustainability of these services already gained from an earlier study, the research will use qualitative methods-in-depth interviews, observation of key meetings, and analysis of relevant documents-to understand the longer-term challenges involved in each case and how these were surmounted. The research will provide lessons for those seeking to sustain their own organisational innovations in wide-ranging clinical areas and for those designing the systems and organisations that make up the NHS, to make them more receptive contexts for the sustainment of innovation. Discussion: Through comparison and contrast across four sites, each involving different organisational innovations, different forms of leadership, and different organisational contexts to contend with, the findings of the study will have wide relevance. The research will produce outputs that are useful for managers and clinicians responsible for organisational innovation, policy makers and senior managers, and academics

Structure-guided selection of specificity determining positions in the human kinome

Background: The human kinome contains many important drug targets. It is well-known that inhibitors of protein kinases bind with very different selectivity profiles. This is also the case for inhibitors of many other protein families. The increased availability of protein 3D structures has provided much information on the structural variation within a given protein family. However, the relationship between structural variations and binding specificity is complex and incompletely understood. We have developed a structural bioinformatics approach which provides an analysis of key determinants of binding selectivity as a tool to enhance the rational design of drugs with a specific selectivity profile. Results: We propose a greedy algorithm that computes a subset of residue positions in a multiple sequence alignment such that structural and chemical variation in those positions helps explain known binding affinities. By providing this information, the main purpose of the algorithm is to provide experimentalists with possible insights into how the selectivity profile of certain inhibitors is achieved, which is useful for lead optimization. In addition, the algorithm can also be used to predict binding affinities for structures whose affinity for a given inhibitor is unknown. The algorithm’s performance is demonstrated using an extensive dataset for the human kinome. Conclusion: We show that the binding affinity of 38 different kinase inhibitors can be explained with consistently high precision and accuracy using the variation of at most six residue positions in the kinome binding site. We show for several inhibitors that we are able to identify residues that are known to be functionally important

Social media use, attitudes, behaviours and perceptions of online professionalism amongst dental students

Use of social media has increased amongst health professionals. This has benefits for patient care but also introduces risks for confidentiality and professional fitness to practise. This study aimed to examine dental student attitudes towards professional behaviour on social media. The secondary aim was to establish the extent and nature of social media use and exposure to potentially unprofessional behaviours. A cross-sectional study was carried out in one dental school. Data were collected using questionnaires to examine social media use, perceptions and attitudes towards social media and professional behaviours online. Students who responded (n=155) all used social media at least once per week; most used more than one platform. Students were aware of the relationship between social media use and professional practice. Posting drunken photographs and interacting with staff and patients online were widely considered as unprofessional. Security settings affected behaviour and most had seen inappropriate behaviours online. Students use social media extensively. Students are aware of the risks but there is a greater sense of safety in closed groups and many students are exposed to potentially inappropriate content online. This suggests that there are opportunities to reduce these risks through training to help students manage these risks

Gravitational Radiation from Post-Newtonian Sources and Inspiralling Compact Binaries

The article reviews the current status of a theoretical approach to the problem of the emission of gravitational waves by isolated systems in the context of general relativity. Part A of the article deals with general post-Newtonian sources. The exterior field of the source is investigated by means of a combination of analytic post-Minkowskian and multipolar approximations. The physical observables in the far-zone of the source are described by a specific set of radiative multipole moments. By matching the exterior solution to the metric of the post-Newtonian source in the near-zone we obtain the explicit expressions of the source multipole moments. The relationships between the radiative and source moments involve many non-linear multipole interactions, among them those associated with the tails (and tails-of-tails) of gravitational waves. Part B of the article is devoted to the application to compact binary systems. We present the equations of binary motion, and the associated Lagrangian and Hamiltonian, at the third post-Newtonian (3PN) order beyond the Newtonian acceleration. The gravitational-wave energy flux, taking consistently into account the relativistic corrections in the binary moments as well as the various tail effects, is derived through 3.5PN order with respect to the quadrupole formalism. The binary's orbital phase, whose prior knowledge is crucial for searching and analyzing the signals from inspiralling compact binaries, is deduced from an energy balance argument.Comment: 109 pages, 1 figure; this version is an update of the Living Review article originally published in 2002; available on-line at http://www.livingreviews.org

Activity of the multikinase inhibitor dasatinib against ovarian cancer cells

BackgroundHere, we explore the therapeutic potential of dasatinib, a small-molecule inhibitor that targets multiple cytosolic and membrane-bound tyrosine kinases, including members of the Src kinase family, EphA2, and focal adhesion kinase for the treatment of ovarian cancer.MethodsWe examined the effects of dasatinib on proliferation, invasion, apoptosis, cell-cycle arrest, and kinase activity using a panel of 34 established human ovarian cancer cell lines. Molecular markers for response prediction were studied using gene expression profiling. Multiple drug effect/combination index (CI) isobologram analysis was used to study the interactions with chemotherapeutic drugs.ResultsConcentration-dependent anti-proliferative effects of dasatinib were seen in all ovarian cancer cell lines tested, but varied significantly between individual cell lines with up to a 3 log-fold difference in the IC(50) values (IC(50) range: 0.001-11.3 micromol l(-1)). Dasatinib significantly inhibited invasion, and induced cell apoptosis, but less cell-cycle arrest. At a wide range of clinically achievable drug concentrations, additive and synergistic interactions were observed for dasatinib plus carboplatin (mean CI values, range: 0.73-1.11) or paclitaxel (mean CI values, range: 0.76-1.05). In this study, 24 out of 34 (71%) representative ovarian cancer cell lines were highly sensitive to dasatinib, compared with only 8 out of 39 (21%) representative breast cancer cell lines previously reported. Cell lines with high expression of Yes, Lyn, Eph2A, caveolin-1 and 2, moesin, annexin-1, and uPA were particularly sensitive to dasatinib.ConclusionsThese data provide a clear biological rationale to test dasatinib as a single agent or in combination with chemotherapy in patients with ovarian cancer

MetaMine – A tool to detect and analyse gene patterns in their environmental context

Background Modern sequencing technologies allow rapid sequencing and bioinformatic analysis of genomes and metagenomes. With every new sequencing project a vast number of new proteins become available with many genes remaining functionally unclassified based on evidences from sequence similarities alone. Extending similarity searches with gene pattern approaches, defined as genes sharing a distinct genomic neighbourhood, have shown to significantly improve the number of functional assignments. Further functional evidences can be gained by correlating these gene patterns with prevailing environmental parameters. MetaMine was developed to approach the large pool of unclassified proteins by searching for recurrent gene patterns across habitats based on key genes. Results MetaMine is an interactive data mining tool which enables the detection of gene patterns in an environmental context. The gene pattern search starts with a user defined environmentally interesting key gene. With this gene a BLAST search is carried out against the Microbial Ecological Genomics DataBase (MEGDB) containing marine genomic and metagenomic sequences. This is followed by the determination of all neighbouring genes within a given distance and a search for functionally equivalent genes. In the final step a set of common genes present in a defined number of distinct genomes is determined. The gene patterns found are associated with their individual pattern instances describing gene order and directions. They are presented together with information about the sample and the habitat. MetaMine is implemented in Java and provided as a client/server application with a user-friendly graphical user interface. The system was evaluated with environmentally relevant genes related to the methane-cycle and carbon monoxide oxidation. Conclusion MetaMine offers a targeted, semi-automatic search for gene patterns based on expert input. The graphical user interface of MetaMine provides a user-friendly overview of the computed gene patterns for further inspection in an ecological context. Prevailing biological processes associated with a key gene can be used to infer new annotations and shape hypotheses to guide further analyses. The use-cases demonstrate that meaningful gene patterns can be quickly detected using MetaMine

Prediction and Testing of Biological Networks Underlying Intestinal Cancer

Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data

Networks of High Mutual Information Define the Structural Proximity of Catalytic Sites: Implications for Catalytic Residue Identification

Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution