Complete Genome Sequence and Comparative Metabolic Profiling of the Prototypical Enteroaggregative Escherichia coli Strain 042

Background \ud Escherichia coli can experience a multifaceted life, in some cases acting as a commensal while in other cases causing intestinal and/or extraintestinal disease. Several studies suggest enteroaggregative E. coli are the predominant cause of E. coli-mediated diarrhea in the developed world and are second only to Campylobacter sp. as a cause of bacterial-mediated diarrhea. Furthermore, enteroaggregative E. coli are a predominant cause of persistent diarrhea in the developing world where infection has been associated with malnourishment and growth retardation. \ud \ud Methods \ud In this study we determined the complete genomic sequence of E. coli 042, the prototypical member of the enteroaggregative E. coli, which has been shown to cause disease in volunteer studies. We performed genomic and phylogenetic comparisons with other E. coli strains revealing previously uncharacterised virulence factors including a variety of secreted proteins and a capsular polysaccharide biosynthetic locus. In addition, by using Biolog™ Phenotype Microarrays we have provided a full metabolic profiling of E. coli 042 and the non-pathogenic lab strain E. coli K-12. We have highlighted the genetic basis for many of the metabolic differences between E. coli 042 and E. coli K-12. \ud \ud Conclusion \ud This study provides a genetic context for the vast amount of experimental and epidemiological data published thus far and provides a template for future diagnostic and intervention strategies

Safetxt: a pilot randomised controlled trial of an intervention delivered by mobile phone to increase safer sex behaviours in young people.

OBJECTIVE: To test the procedures proposed for a main trial of a safer sex intervention for young people delivered by mobile phone text message ('safetxt'). DESIGN AND SETTING: Pilot randomised controlled trial. Participants were recruited through sexual health services in the UK. An independent online randomisation system allocated participants to receive the safetxt intervention or to receive the control text messages (monthly messages about participation in the study). Texting software delivered the messages in accordance with a predetermined schedule. PARTICIPANTS: Residents of England aged 16-24 who had received either a positive chlamydia test result or reported unsafe sex in the last year (defined as more than 1 partner and at least 1 occasion of sex without a condom). INTERVENTION: The safetxt intervention is designed to reduce sexually transmitted infection in young people by supporting them in using condoms, telling a partner about an infection and testing before unprotected sex with a new partner. Safetxt was developed drawing on: behavioural science; face-to-face interventions; the factors known to influence safer sex behaviours and the views of young people. OUTCOMES: The coprimary outcomes of the pilot trial were the recruitment rate and completeness of follow-up. RESULTS: We recruited 200 participants within our target of 3 months and we achieved 81% (162/200) follow-up response for the proposed primary outcome of the main trial, cumulative incidence of chlamydia at 12 months. CONCLUSIONS: Recruitment, randomisation, intervention delivery and follow-up were successful and a randomised controlled trial of the safetxt intervention is feasible. TRIAL REGISTRATION NUMBER: ISRCTN02304709; Results

Masses of ground and excited-state hadrons

We present the first Dyson-Schwinger equation calculation of the light hadron spectrum that simultaneously correlates the masses of meson and baryon ground- and excited-states within a single framework. At the core of our analysis is a symmetry-preserving treatment of a vector-vector contact interaction. In comparison with relevant quantities the root-mean-square-relative-error/degree-of freedom is 13%. Notable amongst our results is agreement between the computed baryon masses and the bare masses employed in modern dynamical coupled-channels models of pion-nucleon reactions. Our analysis provides insight into numerous aspects of baryon structure; e.g., relationships between the nucleon and Delta masses and those of the dressed-quark and diquark correlations they contain.Comment: 25 pages, 7 figures, 4 table

Improving response rates using a monetary incentive for patient completion of questionnaires: an observational study

Background: Poor response rates to postal questionnaires can introduce bias and reduce the statistical power of a study. To improve response rates in our trial in primary care we tested the effect of introducing an unconditional direct payment of 5 pound for the completion of postal questionnaires. Methods: We recruited patients in general practice with knee problems from sites across the United Kingdom. An evidence-based strategy was used to follow-up patients at twelve months with postal questionnaires. This included an unconditional direct payment of 5 pound to patients for the completion and return of questionnaires. The first 105 patients did not receive the 5 pound incentive, but the subsequent 442 patients did. We used logistic regression to analyse the effect of introducing a monetary incentive to increase the response to postal questionnaires. Results: The response rate following reminders for the historical controls was 78.1% ( 82 of 105) compared with 88.0% ( 389 of 442) for those patients who received the 5 pound payment (diff = 9.9%, 95% CI 2.3% to 19.1%). Direct payments significantly increased the odds of response ( adjusted odds ratio = 2.2, 95% CI 1.2 to 4.0, P = 0.009) with only 12 of 442 patients declining the payment. The incentive did not save costs to the trial - the extra cost per additional respondent was almost 50 pound. Conclusion: The direct payment of 5 pound significantly increased the completion of postal questionnaires at negligible increase in cost for an adequately powered study

Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes.

The PROGRESS series (www.progress-partnership.org) sets out a framework of four interlinked prognosis research themes and provides examples from several disease fields to show why evidence from prognosis research is crucial to inform all points in the translation of biomedical and health related research into better patient outcomes. Recommendations are made in each of the four papers to improve current research standards What is prognosis research? Prognosis research seeks to understand and improve future outcomes in people with a given disease or health condition. However, there is increasing evidence that prognosis research standards need to be improved Why is prognosis research important? More people now live with disease and conditions that impair health than at any other time in history; prognosis research provides crucial evidence for translating findings from the laboratory to humans, and from clinical research to clinical practice This first article introduces the framework of four interlinked prognosis research themes and then focuses on the first of the themes - fundamental prognosis research, studies that aim to describe and explain future outcomes in relation to current diagnostic and treatment practices, often in relation to quality of care Fundamental prognosis research provides evidence informing healthcare and public health policy, the design and interpretation of randomised trials, and the impact of diagnostic tests on future outcome. It can inform new definitions of disease, may identify unanticipated benefits or harms of interventions, and clarify where new interventions are required to improve prognosis

State sampling dependence of the Hopfield network inference

The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations. We present the system in the glassy phase with low temperature and high memory load. We find that the inference error is very sensitive to the form of state sampling. When a single state is sampled to compute magnetizations and correlations, the inference error is almost indistinguishable irrespective of the sampled state. However, the error can be greatly reduced if the data is collected with state transitions. Our result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.Comment: 4 pages, 1 figure, further discussions added and relevant references adde

Cyclin-dependent kinase 9 as a potential target for anti-TNF resistant inflammatory bowel disease

BACKGROUND AND AIMS: Resistance to single cytokine blockade, namely anti-TNF therapy, is a growing concern for patients with inflammatory bowel disease (IBD). The transcription factor T-bet is a critical regulator of intestinal homeostasis, is genetically linked to mucosal inflammation and controls the expression of multiples genes such as the pro-inflammatory cytokines IFN-γ and TNF. Inhibiting T-bet may therefore offer a more attractive prospect for treating IBD but remains challenging to target therapeutically. In this study, we evaluate the effect of targeting the transactivation function of T-bet using inhibitors of P-TEFb (CDK9-cyclin T), a transcriptional elongation factor downstream of T-bet. METHODS: Using an adaptive immune-mediated colitis model, human colonic lymphocytes from IBD patients and multiple large clinical datasets, we investigate the effect of CDK9 inhibitors on cytokine production and gene expression in colonic CD4+ T cells and link these genetic modules to clinical response in patients with IBD. RESULTS: Systemic CDK9 inhibition led to histological improvement of immune-mediated colitis and was associated with targeted suppression of colonic CD4+ T cell-derived IFN-γ and IL-17A. In colonic lymphocytes from IBD patients, CDK9 inhibition potently repressed genes responsible for pro-inflammatory signalling, and in particular genes regulated by T-bet. Remarkably, CDK9 inhibition targeted genes that were highly expressed in anti-TNF resistant IBD and that predicted non-response to anti-TNF therapy. CONCLUSION: Collectively, our findings reveal CDK9 as a potential target for anti-TNF resistant IBD, which has the potential for rapid translation to the clinic

Financial considerations in the conduct of multi-centre randomised controlled trials: evidence from a qualitative study.

National Coordinating Centre for Research Methodology; Medical Research Council, UK Department of Health; Chief Scientist OfficeNot peer reviewedPublisher PD

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.</p