In Vivo Activation of the Intracrine Vitamin D Pathway in Innate Immune Cells and Mammary Tissue during a Bacterial Infection

Numerous in vitro studies have shown that toll-like receptor signaling induces 25-hydroxyvitamin D3 1α-hydroxylase (1α-OHase; CYP27B1) expression in macrophages from various species. 1α-OHase is the primary enzyme that converts 25-hydroxyvitamin D3 to 1,25-dihydroxyvitamin D3 (1,25(OH)2D3). Subsequently, synthesis of 1,25(OH)2D3 by 1α-OHase in macrophages has been shown to modulate innate immune responses of macrophages. Despite the numerous in vitro studies that have shown 1α-OHase expression is induced in macrophages, however, evidence that 1α-OHase expression is induced by pathogens in vivo is limited. The objective of this study was to evaluate 1α-OHase gene expression in macrophages and mammary tissue during an in vivo bacterial infection with Streptococcus uberis. In tissue and secreted cells from the infected mammary glands, 1α-OHase gene expression was significantly increased compared to expression in tissue and cells from the healthy mammary tissue. Separation of the cells by FACS9 revealed that 1α-OHase was predominantly expressed in the CD14+ cells isolated from the infected mammary tissue. The 24-hydroxylase gene, a gene that is highly upregulated by 1,25(OH)2D3, was significantly more expressed in tissue and cells from the infected mammary tissue than from the healthy uninfected mammary tissue thus indicating significant local 1,25(OH)2D3 production at the infection site. In conclusion, this study provides the first in vivo evidence that 1α-OHase expression is upregulated in macrophages in response to bacterial infection and that 1α-OHase at the site of infection provides 1,25(OH)2D3 for local regulation of vitamin D responsive genes

User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner

Background: The rigorous elicitation of user needs is a crucial step for both medical device design and purchasing. However, user needs elicitation is often based on qualitative methods whose findings can be difficult to integrate into medical decision-making. This paper describes the application of AHP to elicit user needs for a new CT scanner for use in a public hospital. Methods: AHP was used to design a hierarchy of 12 needs for a new CT scanner, grouped into 4 homogenous categories, and to prepare a paper questionnaire to investigate the relative priorities of these. The questionnaire was completed by 5 senior clinicians working in a variety of clinical specialisations and departments in the same Italian public hospital. Results: Although safety and performance were considered the most important issues, user needs changed according to clinical scenario. For elective surgery, the five most important needs were: spatial resolution, processing software, radiation dose, patient monitoring, and contrast medium. For emergency, the top five most important needs were: patient monitoring, radiation dose, contrast medium control, speed run, spatial resolution. Conclusions: AHP effectively supported user need elicitation, helping to develop an analytic and intelligible framework of decision-making. User needs varied according to working scenario (elective versus emergency medicine) more than clinical specialization. This method should be considered by practitioners involved in decisions about new medical technology, whether that be during device design or before deciding whether to allocate budgets for new medical devices according to clinical functions or according to hospital department

Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

Background: Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies. Results: In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT. Conclusion: Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on http://sunflower.kuicr.kyoto-u.ac.jp/~tamura/slpfa.html webcite

A method to improve protein subcellular localization prediction by integrating various biological data sources

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is crucial information to elucidate protein functions. Owing to the need for large-scale genome analysis, computational method for efficiently predicting protein subcellular localization is highly required. Although many previous works have been done for this task, the problem is still challenging due to several reasons: the number of subcellular locations in practice is large; distribution of protein in locations is imbalanced, that is the number of protein in each location remarkably different; and there are many proteins located in multiple locations. Thus it is necessary to explore new features and appropriate classification methods to improve the prediction performance.</p> <p>Results</p> <p>In this paper we propose a new predicting method which combines two key ideas: 1) Information of neighbour proteins in a probabilistic gene network is integrated to enrich the prediction features. 2) Fuzzy k-NN, a classification method based on fuzzy set theory is applied to predict protein locating in multiple sites. Experiment was conducted on a dataset consisting of 22 locations from Budding yeast proteins and significant improvement was observed.</p> <p>Conclusion</p> <p>Our results suggest that the neighbourhood information from functional gene networks is predictive to subcellular localization. The proposed method thus can be integrated and complementary to other available prediction methods.</p

ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins

<p>Abstract</p> <p>Background</p> <p>The expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.</p> <p>Results</p> <p>Here, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.</p> <p>Conclusion</p> <p>These results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at <url>http://www.imtech.res.in/raghava/eslpred2/</url>.</p

Financial impact of reducing door-to-balloon time in ST-elevation myocardial infarction: a single hospital experience

<p>Abstract</p> <p>Background</p> <p>The impact of reducing door-to-balloon time on hospital revenues, costs, and net income is unknown.</p> <p>Methods</p> <p>We prospectively determined the impact on hospital finances of (1) emergency department physician activation of the catheterization lab and (2) immediate transfer of the patient to an immediately available catheterization lab by an in-house transfer team consisting of an emergency department nurse, a critical care unit nurse, and a chest pain unit nurse. We collected financial data for 52 consecutive ST-elevation myocardial infarction patients undergoing emergency percutaneous intervention from October 1, 2004–August 31, 2005 and compared this group to 80 consecutive ST-elevation myocardial infarction patients from September 1, 2005–June 26, 2006 after protocol implementation.</p> <p>Results</p> <p>Per hospital admission, insurance payments (hospital revenue) decreased ($35,043 ±$36,670 vs. $25,329 ±$16,185, P = 0.039) along with total hospital costs ($28,082 ±$31,453 vs. $18,195 ±$9,242, P = 0.009). Hospital net income per admission was unchanged ($6962 vs.$7134, P = 0.95) as the drop in hospital revenue equaled the drop in costs. For every $1000 reduction in total hospital costs, insurance payments (hospital revenue) dropped$1077 for private payers and $1199 for Medicare/Medicaid. A decrease in hospital charges ($70,430 ± $74,033 vs.$53,514 ± $23,378, P = 0.059), diagnosis related group relative weight (3.7479 ± 2.6731 vs. 2.9729 ± 0.8545, P = 0.017) and outlier payments with hospital revenue>$100,000 (7.7% vs. 0%, P = 0.022) all contributed to decreasing ST-elevation myocardial infarction hospitalization revenue. One-year post-discharge financial follow-up revealed similar results: Insurance payments: $49,959 ±$53,741 vs. $35,937 ±$23,125, P = 0.044; Total hospital costs: $39,974 ±$37,434 vs. $26,778 ±$15,561, P = 0.007; Net Income: $9984 vs.$9159, P = 0.855.</p> <p>Conclusion</p> <p>All of the financial benefits of reducing door-to-balloon time in ST-elevation myocardial infarction go to payers both during initial hospitalization and after one-year follow-up.</p> <p>Trial Registration</p> <p><b>ClinicalTrials.gov ID</b>: NCT00800163</p

Mechanism-Based Screen Establishes Signalling Framework for DNA Damage-Associated G1 Checkpoint Response

DNA damage activates checkpoint controls which block progression of cells through the division cycle. Several different checkpoints exist that control transit at different positions in the cell cycle. A role for checkpoint activation in providing resistance of cells to genotoxic anticancer therapy, including chemotherapy and ionizing radiation, is widely recognized. Although the core molecular functions that execute different damage activated checkpoints are known, the signals that control checkpoint activation are far from understood. We used a kinome-spanning RNA interference screen to delineate signalling required for radiation-mediated retinoblastoma protein activation, the recognized executor of G1 checkpoint control. Our results corroborate the involvement of the p53 tumour suppressor (TP53) and its downstream targets p21CIP1/WAF1 but infer lack of involvement of canonical double strand break (DSB) recognition known for its role in activating TP53 in damaged cells. Instead our results predict signalling involving the known TP53 phosphorylating kinase PRPK/TP53RK and the JNK/p38MAPK activating kinase STK4/MST1, both hitherto unrecognised for their contribution to DNA damage G1 checkpoint signalling. Our results further predict a network topology whereby induction of p21CIP1/WAF1 is required but not sufficient to elicit checkpoint activation. Our experiments document a role of the kinases identified in radiation protection proposing their pharmacological inhibition as a potential strategy to increase radiation sensitivity in proliferating cancer cells