When outcome is a balance: methods to measure combined utility for the choice between induction of labour and expectant management in mild risk pregnancy at term

Background: When the primary and secondary outcomes of clinical studies yield ambiguous or conflicting recommendations, preference or valuation studies may help to overcome the decision problem. The present preference study is attached to two clinical studies (DIGTAT, ISRCT10363217; HYPITAT, ISRCT08132825) that evaluate induction of labour versus expectant management in term pregnancies with a mild risk profile. The purpose of the present study is to compare four methods of valuation/preference measurement. Met

Reflecting the real value of health care resources in modelling and cost-effectiveness studies-The example of viral load informed differentiated care

BACKGROUND: The WHO HIV Treatment Guidelines suggest routine viral-load monitoring can be used to differentiate antiretroviral therapy (ART) delivery and reduce the frequency of clinic visits for patients stable on ART. This recommendation was informed by economic analysis that showed the approach is very likely to be cost-effective, even in the most resource constrained of settings. The health benefits were shown to be modest but the costs of introducing and scaling up viral load monitoring can be offset by anticipated reductions in the costs of clinic visits, due to these being less frequent for many patients. KEY ISSUES FOR ECONOMIC EVALUATION: The cost-effectiveness of introducing viral-load informed differentiated care depends upon whether cost reductions are possible if the number of clinic visits is reduced and/or how freed clinic capacity is used for alternative priorities. Where freed resources, either physical or financial, generate large health gains (e.g. if committed to patients failing ART or to other high value health care interventions), the benefits of differentiated care are expected to be high; if however these freed physical resources are already under-utilized or financial resources are used less efficiently and would not be put to as beneficial an alternative use, the policy may not be cost-effective. The implication is that the use of conventional unit costs to value resources may not well reflect the latter's value in contributing to health improvement. Analyses intended to inform resource allocated decisions in a number of settings may therefore have to be interpreted with due consideration to local context. In this paper we present methods of how economic analyses can reflect the real value of health care resources rather than simply applying their unit costs. The analyses informing the WHO Guidelines are re-estimated by implementing scenarios using this framework, informing how differentiated care can be prioritized to generate greatest gains in population health. IMPLICATIONS: The findings have important implications for how economic analyses should be undertaken and reported in HIV and other disease areas. Results provide guidance on conditions under which viral load informed differentiated care will more likely prove to be cost effective when implemented

Discriminative structural approaches for enzyme active-site prediction

<p>Abstract</p> <p>Background</p> <p>Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far.</p> <p>Results</p> <p>This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis.</p> <p>Conclusions</p> <p>This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.</p

Age-related difference in susceptibility of ApcMin/+ mice towards the chemopreventive efficacy of dietary aspirin and curcumin

The nonsteroidal anti-inflammatory drug aspirin and the spice curcumin retard adenoma formation when administered long-term to ApcMin/+ mice, a model of human familial adenomatous polyposis coli. Both agents interfere with cyclooxygenase activity. When aspirin is administered to ApcMin/+ mice only postweaning, but not before, it is inefficacious, while curcumin given postweaning is active. Here the hypothesis was tested that dietary aspirin (0.05%) or curcumin (0.2%) prevent or delay adenoma formation in offsprings when administered to ApcMin/+ mothers and up to the end of weaning, but not afterwards. Whereas curcumin was without effect when administered in this way, aspirin reduced numbers of intestinal adenomas by 21%. When aspirin given up to the end of weaning was combined with curcumin administered from the end of weaning for the rest of the animals' lifetime, intestinal adenoma numbers were reduced by 38%. The combination was not superior to intervention postweaning with curcumin alone. These results show that aspirin exerts chemopreventive activity in the ApcMin/+ mouse during tumour initiation/early promotion, while curcumin is efficacious when given at a later stage of carcinogenic progression. Thus, the results suggest that in this mouse model aspirin and curcumin act during different ‘windows’ of neoplastic development

CMASA: an accurate algorithm for detecting local protein structural similarity and its application to enzyme catalytic site annotation

<p>Abstract</p> <p>Background</p> <p>The rapid development of structural genomics has resulted in many "unknown function" proteins being deposited in Protein Data Bank (PDB), thus, the functional prediction of these proteins has become a challenge for structural bioinformatics. Several sequence-based and structure-based methods have been developed to predict protein function, but these methods need to be improved further, such as, enhancing the accuracy, sensitivity, and the computational speed. Here, an accurate algorithm, the CMASA (Contact MAtrix based local Structural Alignment algorithm), has been developed to predict unknown functions of proteins based on the local protein structural similarity. This algorithm has been evaluated by building a test set including 164 enzyme families, and also been compared to other methods.</p> <p>Results</p> <p>The evaluation of CMASA shows that the CMASA is highly accurate (0.96), sensitive (0.86), and fast enough to be used in the large-scale functional annotation. Comparing to both sequence-based and global structure-based methods, not only the CMASA can find remote homologous proteins, but also can find the active site convergence. Comparing to other local structure comparison-based methods, the CMASA can obtain the better performance than both FFF (a method using geometry to predict protein function) and SPASM (a local structure alignment method); and the CMASA is more sensitive than PINTS and is more accurate than JESS (both are local structure alignment methods). The CMASA was applied to annotate the enzyme catalytic sites of the non-redundant PDB, and at least 166 putative catalytic sites have been suggested, these sites can not be observed by the Catalytic Site Atlas (CSA).</p> <p>Conclusions</p> <p>The CMASA is an accurate algorithm for detecting local protein structural similarity, and it holds several advantages in predicting enzyme active sites. The CMASA can be used in large-scale enzyme active site annotation. The CMASA can be available by the mail-based server (<url>http://159.226.149.45/other1/CMASA/CMASA.htm</url>).</p

Pregabalin, celecoxib, and their combination for treatment of chronic low-back pain

Background - The efficacy and safety of the association of celecoxib [a selective cyclooxygenase-2 (COX-2) inhibitor] and pregabalin (commonly used to control neuropathic pain), compared with monotherapy of each, were evaluated for the treatment of chronic low-back pain, a condition known to be due to neuropathic as well as nociceptive pain mechanisms. Materials and methods - In this prospective randomized trial, 36 patients received three consecutive 4-week treatment regimes, randomly assigned: celecoxib plus placebo, pregabalin plus placebo, and celecoxib plus pregabalin. All patients were assessed by using a visual analogue scale (VAS, 0\u2013100 mm) and the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS) pain scale by an investigator blinded to the administered pharmacological treatment. Results - Celecoxib and pregabalin were effective in reducing low-back pain when patients were pooled according to LANSS score. The association of celecoxib and pregabalin was more effective than either monotherapy in a mixed population of patients with chronic low-back pain and when data were pooled according to LANSS score. Adverse effects of drug association and monotherapies were similar, with reduced drug consumption in the combined therapy. Conclusions - Combination of celecoxib and pregabalin is more effective than monotherapy for chronic low-back pain, with similar adverse effects

Combinatorial Clustering of Residue Position Subsets Predicts Inhibitor Affinity across the Human Kinome

The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (CCORPS) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, CCORPS is applied to the problem of identifying structural features of the kinase ATP binding site that are informative of inhibitor binding. CCORPS is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, CCORPS is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors

The Hubbard model within the equations of motion approach

The Hubbard model has a special role in Condensed Matter Theory as it is considered as the simplest Hamiltonian model one can write in order to describe anomalous physical properties of some class of real materials. Unfortunately, this model is not exactly solved except for some limits and therefore one should resort to analytical methods, like the Equations of Motion Approach, or to numerical techniques in order to attain a description of its relevant features in the whole range of physical parameters (interaction, filling and temperature). In this manuscript, the Composite Operator Method, which exploits the above mentioned analytical technique, is presented and systematically applied in order to get information about the behavior of all relevant properties of the model (local, thermodynamic, single- and two- particle ones) in comparison with many other analytical techniques, the above cited known limits and numerical simulations. Within this approach, the Hubbard model is shown to be also capable to describe some anomalous behaviors of the cuprate superconductors.Comment: 232 pages, more than 300 figures, more than 500 reference

Using structural motif descriptors for sequence-based binding site prediction

All authors are with the Biotechnological Center, TU Dresden, Tatzberg 47-51, 01307 Dresden, Germany and -- Wan Kyu Kim is with the Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USABackground: Many protein sequences are still poorly annotated. Functional characterization of a protein is often improved by the identification of its interaction partners. Here, we aim to predict protein-protein interactions (PPI) and protein-ligand interactions (PLI) on sequence level using 3D information. To this end, we use machine learning to compile sequential segments that constitute structural features of an interaction site into one profile Hidden Markov Model descriptor. The resulting collection of descriptors can be used to screen sequence databases in order to predict functional sites. -- Results: We generate descriptors for 740 classified types of protein-protein binding sites and for more than 3,000 protein-ligand binding sites. Cross validation reveals that two thirds of the PPI descriptors are sufficiently conserved and significant enough to be used for binding site recognition. We further validate 230 PPIs that were extracted from the literature, where we additionally identify the interface residues. Finally we test ligand-binding descriptors for the case of ATP. From sequences with Swiss-Prot annotation "ATP-binding", we achieve a recall of 25% with a precision of 89%, whereas Prosite's P-loop motif recognizes an equal amount of hits at the expense of a much higher number of false positives (precision: 57%). Our method yields 771 hits with a precision of 96% that were not previously picked up by any Prosite-pattern. -- Conclusion: The automatically generated descriptors are a useful complement to known Prosite/InterPro motifs. They serve to predict protein-protein as well as protein-ligand interactions along with their binding site residues for proteins where merely sequence information is available.Institute for Cellular and Molecular [email protected]

Regulation of Bestrophins by Ca2+: A Theoretical and Experimental Study

Bestrophins are a recently discovered family of Cl− channels, for which no structural information is available. Some family members are activated by increased intracellular Ca2+ concentration. Bestrophins feature a well conserved Asp-rich tract in their COOH terminus (Asp-rich domain), which is homologous to Ca2+-binding motifs in human thrombospondins and in human big-conductance Ca2+- and voltage-gated K+ channels (BKCa). Consequently, the Asp-rich domain is also a candidate for Ca2+ binding in bestrophins. Based on these considerations, we constructed homology models of human bestrophin-1 (Best1) Asp-rich domain using human thrombospondin-1 X-ray structure as a template. Molecular dynamics simulations were used to identify Asp and Glu residues binding Ca2+ and to predict the effects of their mutations to alanine. We then proceeded to test selected mutations in the Asp-rich domain of the highly homologous mouse bestrophin-2. The mutants expressed in HEK-293 cells were investigated by electrophysiological experiments using the whole-cell voltage-clamp technique. Based on our molecular modeling results, we predicted that Asp-rich domain has two defined binding sites and that D301A and D304A mutations may impact the binding of the metal ions. The experiments confirmed that these mutations do actually affect the function of the protein causing a large decrease in the Ca2+-activated Cl− current, fully consistent with our predictions. In addition, other studied mutations (E306A, D312A) did not decrease Ca2+-activated Cl− current in agreement with modeling results