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Abstract Background Promoter identification is a first step in the quest to explain gene regulation in bacteria. It has been demonstrated that the initiation of bacterial transcription depends upon the stability and topology of DNA in the promoter region as well as the binding affinity between the RNA polymerase σ-factor and promoter. However, promoter prediction algorithms to date have not explicitly used an ensemble of these factors as predictors. In addition, most promoter models have been trained on data from Escherichia coli. Although it has been shown that transcriptional mechanisms are similar among various bacteria, it is quite possible that the differences between Escherichia coli and Chlamydia trachomatis are large enough to recommend an organism-specific modeling effort. Results Here we present an iterative stochastic model building procedure that combines such biophysical metrics as DNA stability, curvature, twist and stress-induced DNA duplex destabilization along with duration hidden Markov model parameters to model Chlamydia trachomatis σ66 promoters from 29 experimentally verified sequences. Initially, iterative duration hidden Markov modeling of the training set sequences provides a scoring algorithm for Chlamydia trachomatis RNA polymerase σ66/DNA binding. Subsequently, an iterative application of Stepwise Binary Logistic Regression selects multiple promoter predictors and deletes/replaces training set sequences to determine an optimal training set. The resulting model predicts the final training set with a high degree of accuracy and provides insights into the structure of the promoter region. Model based genome-wide predictions are provided so that optimal promoter candidates can be experimentally evaluated, and refined models developed. Co-predictions with three other algorithms are also supplied to enhance reliability. Conclusion This strategy and resulting model support the conjecture that DNA biophysical properties, along with RNA polymerase σ-factor/DNA binding collaboratively, contribute to a sequence's ability to promote transcription. This work provides a baseline model that can evolve as new Chlamydia trachomatis σ66 promoters are identified with assistance from the provided genome-wide predictions. The proposed methodology is ideal for organisms with few identified promoters and relatively small genomes.

Objective: To determine dissemination outcomes and faculty perceptions of senior research projects conducted from 2008 to 2011 by PharmD students in a curricular pathway focused on direct patient care., Methods: Preceptors' reported dissemination outcomes of research projects were surveyed and their perceptions of the precepting experience were rated using a web-based survey. Results were compared to those from an earlier pharmaceutical care cohort (2002-2007) and a combined cohort of 2, more research-intensive curricular pathways at the school., Results: The overall response rate was 90.2%. Project dissemination included 61.3% at an institutional forum, 42.3% as a submitted publication, 37.8% as a poster, and 4.5% as an oral presentation. Projects completed from 2008-2011 were significantly more likely than those from 2002-2007 to be submitted for publication (42.3% vs 10.7%, p<0.001) and published (28.8% vs 5.3%, p<0.001). Most preceptors found their research projects valuable to them professionally (88.3%) and to their own or another institution (83.5% and 78.5%, respectively). Ninety-five percent of preceptors would precept again., Conclusion: Dissemination rates for pharmaceutical care projects increased over time. Despite modest dissemination levels, the majority of preceptors agreed that required student research projects provide a valuable learning experience for students.

Previous cross-sectional analyses demonstrated that CD8(+) and CD4(+) T-cell reactivity to islet-specific antigens was more prevalent in T1D subjects than in healthy donors (HD). Here, we examined T1D-associated epitope-specific CD4(+) T-cell cytokine production and autoreactive CD8(+) T-cell frequency on a monthly basis for one year in 10 HD, 33 subjects with T1D, and 15 subjects with T2D. Autoreactive CD4(+) T-cells from both T1D and T2D subjects produced more IFN-γ when stimulated than cells from HD. In contrast, higher frequencies of islet antigen-specific CD8(+) T-cells were detected only in T1D. These observations support the hypothesis that general beta-cell stress drives autoreactive CD4(+) T-cell activity while islet over-expression of MHC class I commonly seen in T1D mediates amplification of CD8(+) T-cells and more rapid beta-cell loss. In conclusion, CD4(+) T-cell autoreactivity appears to be present in both T1D and T2D while autoreactive CD8(+) T-cells are unique to T1D. Thus, autoreactive CD8(+) cells may serve as a more T1D-specific biomarker., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Multiple immune parameters such as frequencies of autoreactive CD4(+), CD8(+) T-cells and CD4(+)CD25(+)Foxp3(+) T-cells have been explored as biomarkers in human T1D. However, intra-individual temporal variation of these parameters has not been assessed systematically over time. We determined the variation in each of these parameters in a cohort of T1D and healthy donors (HDs), at monthly intervals for one year. Despite low intra- and inter-assay co-efficient of variation (CV), mean CVs for each of the immune parameters were 119.1% for CD4(+) T-cell-derived IFN-γ, 50.44% for autoreactive CD8(+) T-cells, and 31.24% for CD4(+)CD25(+)Foxp3(+) T-cells. Further, both HDs and T1D donors had similar CVs. The variation neither correlated with BMI, age, disease duration or insulin usage, nor were there detectable cyclical patterns of variation. However, averaging results from multiple visits for an individual provided a better estimate of the CV between visits. Based on our data we predict that by averaging values from three visits a treatment effect on these parameters with a 50% effect size could be detected with the same power using 1.8-4-fold fewer patients within a trial compared to using values from a single visit. Thus, our present data contribute to a more robust, accurate endpoint design for future clinical trials in T1D and aid in the identification of truly efficacious therapies.

Over the past decade, the percentage of children who are obese has rapidly increased. As weight has been shown to correlate strongly with the size of various organs, we have observed that obese children have larger kidneys than their normal-weight counterparts. This study sought to quantify this observation by establishing the normal limits of renal length for this population. We examined 204 healthy patients seen for benign hematuria between January 2000 and May 2008. Both right and left kidney lengths significantly correlated with age, height, weight, body mass index and body surface area in our obese patients (n = 59). Height was also found to be a significant predictor of kidney length, in the obese group, by multiple linear regression analysis and was used to create renal length nomograms for the obese pediatric population (P < 0.01). According to this analysis, obese patients had significantly larger kidneys than those of normal-weight patients (P < 0.01). By defining the normal limits of renal length for this group, unnecessary evaluation for nephromegaly will be avoided.

We reviewed the appointment data for a psychiatry service in California that provided consultations and also therapy through telepsychiatry. Over an 18-month period, there were 7523 telepsychiatry appointments and 115,148 conventional (face-to-face) appointments. A higher proportion of the telepsychiatry appointments was kept (92% telepsychiatry vs. 87% non-telepsychiatry). Also, telepsychiatry appointments were significantly less likely to be cancelled by patients (3.5% vs. 4.8%) and significantly less likely to be no-shows (4.2% vs. 7.8%). These findings were similar in three of the four counties where the service was delivered. However, one county was different, and further examination suggested that the morale of the staff and patients may have contributed to the unenthusiastic acceptance of telepsychiatry. We conclude that telepsychiatry can be used effectively in continuing care settings as well as in evaluation settings, and that staff and patient morale are important factors in successful telepsychiatry.

An algorithm is presented for detecting a quantitative pattern in peptide fragments that bind class II major histocompatibility complex (MHC) molecules. It is referred to as a meta-algorithm because it requires successive applications of Stepwise Discriminate Analysis (SDA). On every iteration the best subsequence candidates are selected from sequences known to bind class II MHC molecules. When SDA compares probable binding subsequences with subsequences known not to bind class II MHC molecules, a quantitative model emerges that is capable of classifying subsequences as binding or non-binding. In an iterative manner, the resultant model is utilized as a criterion for selecting probable binding subsequence candidates. The procedure is repeated until models converge. In the illustrated examples, the final models correctly classify over 95% of the peptides in a database of peptides whose binding affinity for HLA-DR1 is known. The final model can then be used to predict the binding affinity of peptides that have not yet been laboratory tested.

Biochemists and molecular biologists have suggested motifs for characterizing the binding of peptide fragments and class II major histocompatibility complex (MHC) molecules based on laboratory results and crystal structures. The iterative algorithm presented here converts a suggested motif into a quantitative data-based model. The database accessed consists of peptide fragments known to bind or not bind to class II MHC molecules of particular haplotypes. Stepwise discriminant analysis is utilized to increase or decrease motif coefficients until the resulting motif classifies all binders and non-binders correctly. Stepwise discriminant analysis is a standard multivariate statistical procedure and is available in comprehensive commercial statistical packages. Program 7M of BMDP Statistical Software was used in this study.

Multiple regression analysis is utilized to predict peptides that bind major histocompatibility complex (MHC) class II molecules of the d haplotype. The predictor variables, obtained from the primary sequence of protein fragments, include pairs of amino acid residues separated by zero to seven intervening residues. The resulting models demonstrate strong predictive validity, and have interpretations in accordance with recent discoveries regarding the binding mechanisms of class I MHC molecules.

Bayesian discriminant analysis is used to predict whether or not a given protein segment will activate helper T cells. The predictor variables are drawn from the products of frequencies of amino acid residues. The model's predictive validity compares favourably with that of alternative modelling strategies, suggesting that this approach merits further investigation.