A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

<p>Abstract</p> <p>Background</p> <p>Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.</p> <p>Methods</p> <p>Eight models were developed: Bayes linear and quadratic models, <it>k</it>-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.</p> <p>Results</p> <p>Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and <it>k</it>-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, <it>k</it>-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.</p> <p>Conclusion</p> <p>Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.</p

Grifonin-1: A Small HIV-1 Entry Inhibitor Derived from the Algal Lectin, Griffithsin

Background: Griffithsin, a 121-residue protein isolated from a red algal Griffithsia sp., binds high mannose N-linked glycans of virus surface glycoproteins with extremely high affinity, a property that allows it to prevent the entry of primary isolates and laboratory strains of T- and M-tropic HIV-1. We used the sequence of a portion of griffithsin's sequence as a design template to create smaller peptides with antiviral and carbohydrate-binding properties. Methodology/Results: The new peptides derived from a trio of homologous β-sheet repeats that comprise the motifs responsible for its biological activity. Our most active antiviral peptide, grifonin-1 (GRFN-1), had an EC50 of 190.8±11.0 nM in in vitro TZM-bl assays and an EC50 of 546.6±66.1 nM in p24gag antigen release assays. GRFN-1 showed considerable structural plasticity, assuming different conformations in solvents that differed in polarity and hydrophobicity. Higher concentrations of GRFN-1 formed oligomers, based on intermolecular β-sheet interactions. Like its parent protein, GRFN-1 bound viral glycoproteins gp41 and gp120 via the N-linked glycans on their surface. Conclusion: Its substantial antiviral activity and low toxicity in vitro suggest that GRFN-1 and/or its derivatives may have therapeutic potential as topical and/or systemic agents directed against HIV-1

Identification of restriction endonuclease with potential ability to cleave the HSV-2 genome: Inherent potential for biosynthetic versus live recombinant microbicides

<p>Abstract</p> <p>Background</p> <p>Herpes Simplex virus types 1 and 2 are enveloped viruses with a linear dsDNA genome of ~120–200 kb. Genital infection with HSV-2 has been denoted as a major risk factor for acquisition and transmission of HIV-1. Developing biomedical strategies for HSV-2 prevention is thus a central strategy in reducing global HIV-1 prevalence. This paper details the protocol for the isolation of restriction endunucleases (REases) with potent activity against the HSV-2 genome and models two biomedical interventions for preventing HSV-2.</p> <p>Methods and Results</p> <p>Using the whole genome of HSV-2, 289 REases and the bioinformatics software Webcutter2; we searched for potential recognition sites by way of genome wide palindromics. REase application in HSV-2 biomedical therapy was modeled concomitantly. Of the 289 enzymes analyzed; 77(26.6%) had potential to cleave the HSV-2 genome in > 100 but < 400 sites; 69(23.9%) in > 400 but < 700 sites; and the 9(3.1%) enzymes: BmyI, Bsp1286I, Bst2UI, BstNI, BstOI, EcoRII, HgaI, MvaI, and SduI cleaved in more than 700 sites. But for the 4: PacI, PmeI, SmiI, SwaI that had no sign of activity on HSV-2 genomic DNA, all 130(45%) other enzymes cleaved < 100 times. In silico palindromics has a PPV of 99.5% for in situ REase activity (2) Two models detailing how the REase EcoRII may be applied in developing interventions against HSV-2 are presented: a nanoparticle for microbicide development and a "recombinant lactobacillus" expressing cell wall anchored receptor (truncated nectin-1) for HSV-2 plus EcoRII.</p> <p>Conclusion</p> <p>Viral genome slicing by way of these bacterially- derived R-M enzymatic peptides may have therapeutic potential in HSV-2 infection; a cofactor for HIV-1 acquisition and transmission.</p

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

<p>Abstract</p> <p>Background</p> <p>Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.</p> <p>Methods</p> <p>Models based on Bayes rule, <it>k-</it>nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.</p> <p>Results</p> <p>Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. <it>k</it>-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.</p> <p>Conclusion</p> <p>Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p

Methionine Sulfoxide Reductase A (MsrA) Deficient Mycoplasma genitalium Shows Decreased Interactions with Host Cells

Mycoplasma genitalium is an important sexually transmitted pathogen that affects both men and women. In genital-mucosal tissues, it initiates colonization of epithelial cells by attaching itself to host cells via several identified bacterial ligands and host cell surface receptors. We have previously shown that a mutant form of M. genitalium lacking methionine sulfoxide reductase A (MsrA), an antioxidant enzyme which converts oxidized methionine (Met(O)) into methionine (Met), shows decreased viability in infected animals. To gain more insights into the mechanisms by which MsrA controls M. genitalium virulence, we compared the wild-type M. genitalium strain (G37) with an msrA mutant (MS5) strain for their ability to interact with target cervical epithelial cell lines (HeLa and C33A) and THP-1 monocytic cells. Infection of epithelial cell lines with both strains revealed that MS5 was less cytotoxic to HeLa and C33A cell lines than the G37 strain. Also, the MS5 strain was more susceptible to phagocytosis by THP-1 cells than wild type strain (G37). Further, MS5 was less able to induce aggregation and differentiation in THP-1 cells than the wild type strain, as determined by carboxyfluorescein diacetate succinimidyl ester (CFSE) labeling of the cells, followed by counting of cells attached to the culture dish using image analysis. Finally, MS5 was observed to induce less proinflammatory cytokine TNF-α by THP-1 cells than wild type G37 strain. These results indicate that MsrA affects the virulence properties of M. genitalium by modulating its interaction with host cells

The antiviral protein cyanovirin-N: the current state of its production and applications.

Human immunodeficiency virus (HIV)/AIDS continues to spread worldwide, and most of the HIV-infected people living in developing countries have little or no access to highly active antiretroviral therapy. The development of efficient and low-cost microbicides to prevent sexual transmission of HIV should be given high priority because there is no vaccine available yet. Cyanovirin-N (CVN) is an entry inhibitor of HIV and many other viruses, and it represents a new generation of microbicide that has specific and potent activity, a different mechanism of action, and unusual chemicophysical stability. In vitro and in vivo antiviral tests suggested that the anti-HIV effect of CVN is stronger than a well-known gp120-targeted antibody (2G12) and another microbicide candidate, PRO2000. CVN is a cyanobacteria-derived protein that has special structural features, making the artificial production of this protein very difficult. In order to develop an efficient and relatively low-cost approach for large-scale production of recombinant CVN to satisfy medical use, this protein has been expressed in many systems by trial and error. Here, to summarize the potential and remaining challenges for the development of this protein into an HIV prevention agent, the progress in the structural mechanism determination, heterologous production and pharmacological evaluation of CVN is reviewed