Application of data mining to intensive care unit microbiologic data.

We describe refinements to and new experimental applications of the Data Mining Surveillance System (DMSS), which uses a large electronic health-care database for monitoring emerging infections and antimicrobial resistance. For example, information from DMSS can indicate potentially important shifts in infection and antimicrobial resistance patterns in the intensive care units of a single health-care facility

Proof of concept for the simplified breakdown of cellulose by combining Pseudomonas putida strains with surface displayed thermophilic endocellulase, exocellulase and β-glucosidase

BACKGROUND: The production and employment of cellulases still represents an economic bottleneck in the conversion of lignocellulosic biomass to biofuels and other biocommodities. This process could be simplified by displaying the necessary enzymes on a microbial cell surface. Such an approach, however, requires an appropriate host organism which on the one hand can withstand the rough environment coming along with lignocellulose hydrolysis, and on the other hand does not consume the generated glucose so that it remains available for subsequent fermentation steps. RESULTS: The robust soil bacterium Pseudomonas putida showed a strongly reduced uptake of glucose above a temperature of 50 °C, while remaining structurally intact hence recyclable, which makes it suitable for cellulose hydrolysis at elevated temperatures. Consequently, three complementary, thermophilic cellulases from Ruminiclostridium thermocellum were displayed on the surface of the bacterium. All three enzymes retained their activity on the cell surface. A mixture of three strains displaying each one of these enzymes was able to synergistically hydrolyze filter paper at 55 °C, producing 20 μg glucose per mL cell suspension in 24 h. CONCLUSION: We could establish Pseudomonas putida as host for the surface display of cellulases, and provided proof-of-concept for a fast and simple cellulose breakdown process at elevated temperatures. This study opens up new perspectives for the application of P. putida in the production of biofuels and other biotechnological products.<br

The workability of Escherichia coli BL21 (DE3) and Pseudomonas putida KT2440 expression platforms with autodisplayed cellulases: a comparison

This article comparatively reports the workability of Escherichia coli BL21(DE3) and Pseudomonas putida KT2440 cell factories for the expression of three model autodisplayed cellulases (i.e., endoglucanase, BsCel5A; exoglucanase, CelK; β-glucosidase, BglA). The differentiation of the recombinant cells was restricted to their cell growth and enzyme expression/activity attributes. Comparatively, the recombinant E. coli showed higher cell growth rates but lower enzyme activities than the recombinant P. putida. However, the endo-, exoglucanase, and β-glucosidase on the surfaces of both cell factories showed activity over a broad range of pH (4-10) and temperature (30-100 °C). The pH and temperature optima were pH 6, 60 °C (BsCel5A); pH 6, 60-70 °C (CelK); and pH 6, 50 °C (BglA). Overall, the P. putida cell factory with autodisplayed enzymes demonstrated higher bioactivity and remarkable biochemical characteristics and thus was chosen for the saccharification of filter paper. A volumetric blend of the three cellulases with P. putida as the host yielded a ratio of 1:1:1.5 of endoglucanase, exoglucanase, and β-glucosidase, respectively, as the optimum blend composition for filter paper degradation. At an optical density (578 nm) of 50, the blend generated a maximum sugar yield of about 0.7 mg/ml (~ 0.08 U/g) from Whatman filter paper (Ø 6 mm, ~ 2.5 mg) within 24 h

Mining multi-item drug adverse effect associations in spontaneous reporting systems

<p>Abstract</p> <p>Background</p> <p>Multi-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was noted in several prominent pharmacovigilance studies. In this paper we examine the application of a well established data mining method known as association rule mining, which we tailored to the above problem, and demonstrate its value. The method was applied to the FDAs spontaneous adverse event reporting system (AERS) with minimal restrictions and expectations on its output, an experiment that has not been previously done on the scale and generality proposed in this work.</p> <p>Results</p> <p>Based on a set of 162,744 reports of suspected ADEs reported to AERS and published in the year 2008, our method identified 1167 multi-item ADE associations. A taxonomy that characterizes the associations was developed based on a representative sample. A significant number (67% of the total) of potential multi-item ADE associations identified were characterized and clinically validated by a domain expert as previously recognized ADE associations. Several potentially novel ADEs were also identified. A smaller proportion (4%) of associations were characterized and validated as known drug-drug interactions.</p> <p>Conclusions</p> <p>Our findings demonstrate that multi-item ADEs are present and can be extracted from the FDA’s adverse effect reporting system using our methodology, suggesting that our method is a valid approach for the initial identification of multi-item ADEs. The study also revealed several limitations and challenges that can be attributed to both the method and quality of data.</p

Acinetobacter baumannii in intensive care unit: A novel system to study clonal relationship among the isolates

<p>Abstract</p> <p>Background</p> <p>The nosocomial infections surveillance system must be strongly effective especially in highly critic areas, such as Intensive Care Units (ICU). These areas are frequently an epidemiological epicentre for transmission of multi-resistant pathogens, like <it>Acinetobacter baumannii</it>. As an epidemic outbreak occurs it is very important to confirm or exclude the genetic relationship among the isolates in a short time. There are several molecular typing systems used with this aim. The Repetitive sequence-based PCR (REP-PCR) has been recognized as an effective method and it was recently adapted to an automated format known as the DiversiLab system.</p> <p>Methods</p> <p>In the present study we have evaluated the combination of a newly introduced software package for the control of hospital infection (VIGI@ct) with the DiversiLab system. In order to evaluate the reliability of the DiversiLab its results were also compared with those obtained using f-AFLP.</p> <p>Results</p> <p>The combination of VIGI@ct and DiversiLab enabled an earlier identification of an <it>A. baumannii </it>epidemic cluster, through the confirmation of the genetic relationship among the isolates. This cluster regards 56 multi-drug-resistant <it>A. baumannii </it>isolates from several specimens collected from 13 different patients admitted to the ICU in a ten month period. The <it>A. baumannii </it>isolates were clonally related being their similarity included between 97 and 100%. The results of the DiversiLab were confirmed by f-AFLP analysis.</p> <p>Conclusion</p> <p>The early identification of the outbreak has led to the prompt application of operative procedures and precautions to avoid the spread of pathogen. To date, 6 months after the last <it>A. baumannii </it>isolate, no other related case has been identified.</p

Use of Binary Cumulative Sums and Moving Averages in Nosocomial Infection Cluster Detection1

Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the CUSUM and MA (window size, k, α, β, p0, p1) that detected both outbreaks, then calculated an associated positive predictive value (PPV) and time until detection (TUD) for sensitive charts. For CUSUM, optimal performance (high PPV, low TUD, fully sensitive) was for 0.1 <α ≤0.25 and 0.2 <β <0.25, with p0 = 0.05, with a mean TUD of 20 (range 8–43) isolates. Mean PPV was 96.5% (relaxed criteria) to 82.6% (strict criteria). MAs had a mean PPV of 88.5% (relaxed criteria) to 46.1% (strict criteria). CUSUM and MA may be useful techniques for automated surveillance of resistant infections

Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy

<p>Abstract</p> <p>Background</p> <p>Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.</p> <p>Methods</p> <p>Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available. These models are re-executed with individual patient data allowing for patient-specific guidance via a Bayesian forecasting approach. The models are called and executed in an interactive manner through our web-based dashboard environment which interfaces to the hospital's electronic medical records system.</p> <p>Results</p> <p>The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events. Projected plasma concentrations are viewable against protocol-specific nomograms to provide dosing guidance for potential rescue therapy with leucovorin. These data are also viewable against common biomarkers used to assess patient safety (e.g., vital signs and plasma creatinine levels). As additional data become available via therapeutic drug monitoring, the model is re-executed and projections are revised.</p> <p>Conclusion</p> <p>The management of pediatric pharmacotherapy can be greatly enhanced via the immediate feedback provided by decision analytics which incorporate the current, best-available knowledge pertaining to dose-exposure and exposure-response relationships, especially for narrow therapeutic agents that are difficult to manage.</p

A Soluble Form of the High Affinity IgE Receptor, Fc-Epsilon-RI, Circulates in Human Serum

Soluble IgE receptors are potential in vivo modulators of IgE-mediated immune responses and are thus important for our basic understanding of allergic responses. We here characterize a novel soluble version of the IgE-binding alpha-chain of Fc-epsilon-RI (sFcεRI), the high affinity receptor for IgE. sFcεRI immunoprecipitates as a protein of ∼40 kDa and contains an intact IgE-binding site. In human serum, sFcεRI is found as a soluble free IgE receptor as well as a complex with IgE. Using a newly established ELISA, we show that serum sFcεRI levels correlate with serum IgE in patients with elevated IgE. We also show that serum of individuals with normal IgE levels can be found to contain high levels of sFcεRI. After IgE-antigen-mediated crosslinking of surface FcεRI, we detect sFcεRI in the exosome-depleted, soluble fraction of cell culture supernatants. We further show that sFcεRI can block binding of IgE to FcεRI expressed at the cell surface. In summary, we here describe the alpha-chain of FcεRI as a circulating soluble IgE receptor isoform in human serum