International outbreak of Salmonella Oranienburg due to German chocolate

BACKGROUND: This report describes a large international chocolate-associated Salmonella outbreak originating from Germany. METHODS: We conducted epidemiologic investigations including a case-control study, and food safety investigations. Salmonella (S.) Oranienburg isolates were subtyped by the use of pulsed-field gel electrophoresis (PFGE). RESULTS: From 1 October 2001 through 24 March 2002, an estimated excess of 439 S. Oranienburg notifications was registered in Germany. Simultaneously, an increase in S. Oranienburg infections was noted in other European countries in the Enter-net surveillance network. In a multistate matched case-control study in Germany, daily consumption of chocolate (matched odds ratio [MOR]: 4.8; 95% confidence interval [CI]: 1.3–26.5), having shopped at a large chain of discount grocery stores (MOR: 4.2; CI: 1.2–23.0), and consumption of chocolate purchased there (MOR: 5.0; CI: 1.1–47.0) were associated with illness. Subsequently, two brands from the same company, one exclusively produced for that chain, tested positive for S. Oranienburg. In two other European countries and in Canada chocolate from company A was ascertained that also contained S. Oranienburg. Isolates from humans and from chocolates had indistinguishable PFGE profiles. No source or point of contamination was identified. Epidemiological identification of chocolate as a vehicle of infections required two months, and was facilitated by proxy measures. CONCLUSIONS: Despite the use of improved production technologies, the chocolate industry continues to carry a small risk of manufacturing Salmonella-containing products. Particularly in diffuse outbreak-settings, clear associations with surrogates of exposure should suffice to trigger public health action. Networks such as Enter-net have become invaluable for facilitating rapid and appropriate management of international outbreaks

Orally Administered P22 Phage Tailspike Protein Reduces Salmonella Colonization in Chickens: Prospects of a Novel Therapy against Bacterial Infections

One of the major causes of morbidity and mortality in man and economically important animals is bacterial infections of the gastrointestinal (GI) tract. The emergence of difficult-to-treat infections, primarily caused by antibiotic resistant bacteria, demands for alternatives to antibiotic therapy. Currently, one of the emerging therapeutic alternatives is the use of lytic bacteriophages. In an effort to exploit the target specificity and therapeutic potential of bacteriophages, we examined the utility of bacteriophage tailspike proteins (Tsps). Among the best-characterized Tsps is that from the Podoviridae P22 bacteriophage, which recognizes the lipopolysaccharides of Salmonella enterica serovar Typhimurium. In this study, we utilized a truncated, functionally equivalent version of the P22 tailspike protein, P22sTsp, as a prototype to demonstrate the therapeutic potential of Tsps in the GI tract of chickens. Bacterial agglutination assays showed that P22sTsp was capable of agglutinating S. Typhimurium at levels similar to antibodies and incubating the Tsp with chicken GI fluids showed no proteolytic activity against the Tsp. Testing P22sTsp against the three major GI proteases showed that P22sTsp was resistant to trypsin and partially to chymotrypsin, but sensitive to pepsin. However, in formulated form for oral administration, P22sTsp was resistant to all three proteases. When administered orally to chickens, P22sTsp significantly reduced Salmonella colonization in the gut and its further penetration into internal organs. In in vitro assays, P22sTsp effectively retarded Salmonella motility, a factor implicated in bacterial colonization and invasion, suggesting that the in vivo decolonization ability of P22sTsp may, at least in part, be due to its ability to interfere with motility… Our findings show promise in terms of opening novel Tsp-based oral therapeutic approaches against bacterial infections in production animals and potentially in humans

Can Survival Prediction Be Improved By Merging Gene Expression Data Sets?

BACKGROUND:High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS:Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS:Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression

Professional Service Utilisation among Patients with Severe Mental Disorders

<p>Abstract</p> <p>Background</p> <p>Generally, patients with serious mental disorders (SMD) are frequent users of services who generate high care-related costs. Current reforms aim to increase service integration and primary care for improved patient care and health-care efficiency. This article identifies and compares variables associated with the use by patients with SMD of services offered by psychiatrists, case managers, and general practitioners (GPs). It also compares frequent and infrequent service use.</p> <p>Method</p> <p>One hundred forty patients with SMD from five regions in Quebec, Canada, were interviewed on their use of services in the previous year. Patients were also required to complete a questionnaire on needs-assessment. In addition, data were collected from clinical records. Descriptive, bivariate, and multivariate analyses were conducted.</p> <p>Results</p> <p>Most patients used services from psychiatrists and case managers, but no more than half consulted GPs. Most patients were followed at least by two professionals, chiefly psychiatrists and case managers. Care access, continuity of care, and total help received were the most important variables associated with the different types of professional consultation. These variables were also associated with frequent use of professional service, as compared with infrequent service use. In all, enabling factors rather than need factors were the core predictors of frequency of service utilisation by patients with SMD.</p> <p>Conclusion</p> <p>This study reveals that health care system organisation and professional practice - rather than patient need profiles - are the core predictors of professional consultation by patients with SMD. The homogeneity of our study population, i.e. mainly users with schizophrenia, recently discharged from hospital, may partly account for these results. Our findings also underscored the limited involvement of GPs in this patient population's care. As comorbidity is often associated with serious mental disorders, closer follow-up by GPs is needed. Globally, more effort should be directed at increasing shared-care initiatives, which would enhance coordination among psychiatrists, GPs, and psychosocial teams (including case managers). Finally, there is a need to increase awareness among health care providers, especially GPs, of the level of care required by patients with disabling and serious mental disorders.</p

Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics

The Phenotype MicroArray (OmniLog® PM) system is able to simultaneously capture a large number of phenotypes by recording an organism's respiration over time on distinct substrates. This technique targets the object of natural selection itself, the phenotype, whereas previously addressed '-omics' techniques merely study components that finally contribute to it. The recording of respiration over time, however, adds a longitudinal dimension to the data. To optimally exploit this information, it must be extracted from the shapes of the recorded curves and displayed in analogy to conventional growth curves.The free software environment R was explored for both visualizing and fitting of PM respiration curves. Approaches using either a model fit (and commonly applied growth models) or a smoothing spline were evaluated. Their reliability in inferring curve parameters and confidence intervals was compared to the native OmniLog® PM analysis software. We consider the post-processing of the estimated parameters, the optimal classification of curve shapes and the detection of significant differences between them, as well as practically relevant questions such as detecting the impact of cultivation times and the minimum required number of experimental repeats.We provide a comprehensive framework for data visualization and parameter estimation according to user choices. A flexible graphical representation strategy for displaying the results is proposed, including 95% confidence intervals for the estimated parameters. The spline approach is less prone to irregular curve shapes than fitting any of the considered models or using the native PM software for calculating both point estimates and confidence intervals. These can serve as a starting point for the automated post-processing of PM data, providing much more information than the strict dichotomization into positive and negative reactions. Our results form the basis for a freely available R package for the analysis of PM data

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Ketoconazole in the treatment of cryptococcosis of the central nervous system

Two patients with cryptococcosis of the CNS were treated with ketoconazole (KTZ), an imidazole derivative with fungistatic properties; they had either failed standard therapy (Amphotericin-B + 5-Flurocytosine) or suffred intolerable side-effects to it. Both patients were administered KTZ 800 mg/day as monotherapy for six months without interruption and both responded. One month after KTZ therapy was withdrawn, however, a relapse of the infection was seen in one case. Side-effects were minimal during the trial of treatment. KTZ could be a useful drug in some cases of neurocryptococcosis

Factors inhibiting the implementation of the atraumatic restorative treatment approach in public oral health services in Gauteng province, South Africa.

Contains fulltext : 51790.pdf (publisher's version ) (Open Access

The effect of indigenous bacterial populations on buccal epithelial cells on subsequent microbial adhesion in vitro

Despite the numerous investigations on the adhesion of microorganisms to buccal epithelial cells, it is difficult to correlate different results, as disparate adherence values have been reported for a given organism. As one reason for this disparity may be the indigenous or natural bacterial populations on human buccal epithelial cells, the effect of the latter on subsequent microbial adherence in vitro was examined. There was a highly significant correlation between the degree of natural bacterial colonization on pooled buccal epithelial cells from 8 healthy donors and the adhesion of a single isolate each of Streptococcus mitis, Escherichia coli and Actinomyces naeslundii. However, no such relationship could be established for Candida albicans, Streptococcus milleri and another isolate of Streptococcus mitis. As in previous studies, variation in adherence values was found, both between samples from different donors, and from the same donor over time, but to a far lesser degree in pooled samples from different donors. These results imply that natural bacterial populations on buccal epithelial cells may affect the adhesion values derived from laboratory experimentation, and hence such data should be interpreted with caution.link_to_subscribed_fulltex