Optimal search strategies for identifying sound clinical prediction studies in EMBASE

BACKGROUND: Clinical prediction guides assist clinicians by pointing to specific elements of the patient's clinical presentation that should be considered when forming a diagnosis, prognosis or judgment regarding treatment outcome. The numbers of validated clinical prediction guides are growing in the medical literature, but their retrieval from large biomedical databases remains problematic and this presents a barrier to their uptake in medical practice. We undertook the systematic development of search strategies ("hedges") for retrieval of empirically tested clinical prediction guides from EMBASE. METHODS: An analytic survey was conducted, testing the retrieval performance of search strategies run in EMBASE against the gold standard of hand searching, using a sample of all 27,769 articles identified in 55 journals for the 2000 publishing year. All articles were categorized as original studies, review articles, general papers, or case reports. The original and review articles were then tagged as 'pass' or 'fail' for methodologic rigor in the areas of clinical prediction guides and other clinical topics. Search terms that depicted clinical prediction guides were selected from a pool of index terms and text words gathered in house and through request to clinicians, librarians and professional searchers. A total of 36,232 search strategies composed of single and multiple term phrases were trialed for retrieval of clinical prediction studies. The sensitivity, specificity, precision, and accuracy of search strategies were calculated to identify which were the best. RESULTS: 163 clinical prediction studies were identified, of which 69 (42.3%) passed criteria for scientific merit. A 3-term strategy optimized sensitivity at 91.3% and specificity at 90.2%. Higher sensitivity (97.1%) was reached with a different 3-term strategy, but with a 16% drop in specificity. The best measure of specificity (98.8%) was found in a 2-term strategy, but with a considerable fall in sensitivity to 60.9%. All single term strategies performed less well than 2- and 3-term strategies. CONCLUSION: The retrieval of sound clinical prediction studies from EMBASE is supported by several search strategies

Rapid cryopreservation of five mammalian and one mosquito cell line at -80°C while attached to flasks in a serum free cryopreservative

Cell culturing, and the requisite storage of cell lines at ultra-low temperatures, is used in most laboratories studying or using eukaryotic proteomics, genomics, microarray, and RNA technologies. In this study we have observed that A72(dog), CRFK(cat), NB324K(human), MCF7(human), WI38(human), and C636(mosquito) cells were effectively cryopreserved at -80°C while attached to the substratum of 25cm(2 )tissue culture flasks. This was accomplished using a serum free crypreservative recently developed by Corsini and co-workers. The technique allows for significant savings of time and money in laboratories that rapidly process numerous cell lines

Poloxomer 188 Has a Deleterious Effect on Dystrophic Skeletal Muscle Function

Duchenne muscular dystrophy (DMD) is an X-linked, fatal muscle wasting disease for which there is currently no cure and limited palliative treatments. Poloxomer 188 (P188) is a tri-block copolymer that has been proposed as a potential treatment for cardiomyopathy in DMD patients. Despite the reported beneficial effects of P188 on dystrophic cardiac muscle function, the effects of P188 on dystrophic skeletal muscle function are relatively unknown. Mdx mice were injected intraperitoneally with 460 mg/kg or 30 mg/kg P188 dissolved in saline, or saline alone (control). The effect of single-dose and 2-week daily treatment was assessed using a muscle function test on the Tibialis Anterior (TA) muscle in situ in anaesthetised mice. The test comprises a warm up, measurement of the force-frequency relationship and a series of eccentric contractions with a 10% stretch that have previously been shown to cause a drop in maximum force in mdx mice. After 2 weeks of P188 treatment at either 30 or 460 mg/kg/day the drop in maximum force produced following eccentric contractions was significantly greater than that seen in saline treated control mice (P = 0.0001). Two week P188 treatment at either dose did not significantly change the force-frequency relationship or maximum isometric specific force produced by the TA muscle. In conclusion P188 treatment increases susceptibility to contraction-induced injury following eccentric contractions in dystrophic skeletal muscle and hence its suitability as a potential therapeutic for DMD should be reconsidered

A biophysical model of cell adhesion mediated by immunoadhesin drugs and antibodies

A promising direction in drug development is to exploit the ability of natural killer cells to kill antibody-labeled target cells. Monoclonal antibodies and drugs designed to elicit this effect typically bind cell-surface epitopes that are overexpressed on target cells but also present on other cells. Thus it is important to understand adhesion of cells by antibodies and similar molecules. We present an equilibrium model of such adhesion, incorporating heterogeneity in target cell epitope density and epitope immobility. We compare with experiments on the adhesion of Jurkat T cells to bilayers containing the relevant natural killer cell receptor, with adhesion mediated by the drug alefacept. We show that a model in which all target cell epitopes are mobile and available is inconsistent with the data, suggesting that more complex mechanisms are at work. We hypothesize that the immobile epitope fraction may change with cell adhesion, and we find that such a model is more consistent with the data. We also quantitatively describe the parameter space in which binding occurs. Our results point toward mechanisms relating epitope immobility to cell adhesion and offer insight into the activity of an important class of drugs.Comment: 13 pages, 5 figure

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.Fil: Goncearenco, Alexander. National Institutes of Health; Estados UnidosFil: Li, Minghui. Soochow University; China. National Institutes of Health; Estados UnidosFil: Simonetti, Franco Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Shoemaker, Benjamin A. National Institutes of Health; Estados UnidosFil: Panchenko, Anna R. National Institutes of Health; Estados Unido

OMERACT Filter 2.1: Elaboration of the Conceptual Framework for Outcome Measurement in Health Intervention Studies

Objective: The Outcome Measures in Rheumatology (OMERACT) Filter 2.0 framework was developed in 2014 to aid core outcome set development by describing the full universe of “measurable aspects of health conditions” from which core domains can be selected. This paper provides elaborations and updated concepts (OMERACT Filter 2.1). Methods: At OMERACT 2018, we discussed challenges in the framework application caused by unclear or ambiguous wording and terms and incompletely developed concepts. Results: The updated OMERACT Filter 2.1 framework makes benefits and harms explicit, clarifies concepts, and improves naming of various terms. Conclusion: We expect that the Filter 2.1 framework will improve the process of core set development

Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures

Computer-based computed tomography (CT) analysis can provide objective quantitation of disease in idiopathic pulmonary fibrosis (IPF). A computer algorithm, CALIPER, was compared with conventional CT and pulmonary function measures of disease severity for mortality prediction.CT and pulmonary function variables (forced expiratory volume in 1 s, forced vital capacity, diffusion capacity of the lung for carbon monoxide, transfer coefficient of the lung for carbon monoxide and composite physiologic index (CPI)) of 283 consecutive patients with a multidisciplinary diagnosis of IPF were evaluated against mortality. Visual and CALIPER CT features included total extent of interstitial lung disease, honeycombing, reticular pattern, ground glass opacities and emphysema. In addition, CALIPER scored pulmonary vessel volume (PVV) while traction bronchiectasis and consolidation were only scored visually. A combination of mortality predictors was compared with the Gender, Age, Physiology model.On univariate analyses, all visual and CALIPER-derived interstitial features and functional indices were predictive of mortality to a 0.01 level of significance. On multivariate analysis, visual CT parameters were discarded. Independent predictors of mortality were CPI (hazard ratio (95% CI) 1.05 (1.02-1.07), p<0.001) and two CALIPER parameters: PVV (1.23 (1.08-1.40), p=0.001) and honeycombing (1.18 (1.06-1.32), p=0.002). A three-group staging system derived from this model was powerfully predictive of mortality (2.23 (1.85-2.69), p<0.0001).CALIPER-derived parameters, in particular PVV, are more accurate prognostically than traditional visual CT scores. Quantitative tools such as CALIPER have the potential to improve staging systems in IPF

Evaluation of computer-based computer tomography stratification against outcome models in connective tissue disease-related interstitial lung disease: a patient outcome study

Table S1. Lobar visual scores were adjusted using scintigraphic and gas dilution measures of the physiological contribution of each lobe to the total lung volume in health (top row). The figure was divided by the proportion of each lung representing a lobe (16.7%), or in the case of the left upper lobe, which included the lingula, two lobes (33.3%). Table S2. Single determination standard deviation values of visual CT scores for connective tissue disease-related interstitial lung disease cases. Table S3. Patient age, gender, smoking status and measures of pulmonary function indices, CALIPER and visually scored CT parameters and echocardiography data for the four groups of the ILD-GAP index. Data represent mean values with standard deviations. CTD, connective tissue disease; FEV1, forced expiratory volume in one second; FVC, forced vital capacity; DLco, diffusing capacity for carbon monoxide; Kco, carbon monoxide transfer coefficient; TLC, total lung capacity; CPI, composite physiologic index; ILD, interstitial lung disease; GGO, ground glass opacity; PVV, pulmonary vessel volume; TxBx, traction bronchiectasis; PA, pulmonary artery; AAo, ascending aorta; RVSP, right ventricular systolic pressure. Table S4. P values demonstrating differences between automated stratified groups calculated using one-way ANOVA with Bonferroni correction for continuous variables and t-test with Bonferroni correction for categorical variables. ILD, interstitial lung disease; PA, pulmonary artery; Ao, ascending aorta; HC, honeycombing; DLco, diffusing capacity for carbon monoxide; Kco, carbon monoxide transfer coefficient; CPI, composite physiologic index; RVSP, right ventricular systolic pressure. * not significant. Figure S1. CONSORT diagram illustrating the selection of patients for the final study population. ILD, interstitial lung disease; CTD, connective tissue disease; IPAF, interstitial pneumonia with autoimmune features; LCH, Langerhans cell histiocytosis; LAM, lymphangioleiomyomatosis; CT, computed tomography. (DOCX 67 kb

Structural Basis for the Mechanism of ATP-Dependent Acetone Carboxylation

Microorganisms use carboxylase enzymes to form new carbon-carbon bonds by introducing carbon dioxide gas (CO2) or its hydrated form, bicarbonate (HCO3−), into target molecules. Acetone carboxylases (ACs) catalyze the conversion of substrates acetone and HCO3− to form the product acetoacetate. Many bicarbonate-incorporating carboxylases rely on the organic cofactor biotin for the activation of bicarbonate. ACs contain metal ions but not organic cofactors, and use ATP to activate substrates through phosphorylation. How the enzyme coordinates these phosphorylation events and new C-C bond formation in the absence of biotin has remained a mystery since these enzymes were discovered. The first structural rationale for acetone carboxylation is presented here, focusing on the 360 kDa (αβγ)2 heterohexameric AC from Xanthobacter autotrophicus in the ligand-free, AMP-bound, and acetate coordinated states. These structures suggest successive steps in a catalytic cycle revealing that AC undergoes large conformational changes coupled to substrate activation by ATP to perform C-C bond ligation at a distant Mn center. These results illustrate a new chemical strategy for the conversion of CO2 into biomass, a process of great significance to the global carbon cycle