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Find video protocols related to scientific articles indexed in Pubmed.
HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure.
Infect. Genet. Evol.
PUBLISHED: 02-25-2013
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We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure.
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A6 peptide activates CD44 adhesive activity, induces FAK and MEK phosphorylation, and inhibits the migration and metastasis of CD44-expressing cells.
Mol. Cancer Ther.
PUBLISHED: 09-01-2011
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The A6 peptide (acetyl-KPSSPPEE-amino) has antitumor activity in the absence of significant adverse events in murine tumor models and clinical trials. A6 shares sequence homology with CD44, an adhesion receptor involved in metastasis that is also a marker of cancer stem cells and drug-resistant phenotypes. We investigated the mechanism of action of A6 by examining its effects on CD44 activity, cell migration, and metastasis. A6 inhibited the migration of a subset of ovarian and breast cancer cell lines, exhibiting IC(50) values of 5 to 110 nmol/L. The ability of A6 to inhibit migration in vitro correlated with CD44 expression. Immunopreciptation studies showed that CD44 binds A6 and that biotin-tagged A6 can be cross-linked to CD44. The binding of A6 altered the structure of CD44 such that it was no longer recognized by a monoclonal antibody to a specific epitope. Importantly, A6 potentiated the CD44-dependent adhesion of cancer cells to hyaluronic acid and activated CD44-mediated signaling, as evidenced by focal adhesion kinase and MAP/ERK kinase phosphorylation. In vivo, A6 (100 mg/kg delivered s.c. twice daily) reduced the number of lung foci generated by the i.v. injection of B16-F10 melanoma cells by 50% (P = 0.029 in an unpaired t test). We conclude that A6 potently blocks the migration of CD44-positive cells in vitro through an interaction with CD44 that alters its structure and activates CD44 to enhance ligand binding and downstream signaling. The concurrent ability of A6 to agonize the CD44 receptor suggests that CD44 activation may represent a novel strategy for inhibiting metastatic disease.
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Only slight impact of predicted replicative capacity for therapy response prediction.
PLoS ONE
PUBLISHED: 01-15-2010
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Replication capacity (RC) of specific HIV isolates is occasionally blamed for unexpected treatment responses. However, the role of viral RC in response to antiretroviral therapy is not yet fully understood.
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Comparison of HIV-1 genotypic resistance test interpretation systems in predicting virological outcomes over time.
PLoS ONE
PUBLISHED: 01-04-2010
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Several decision support systems have been developed to interpret HIV-1 drug resistance genotyping results. This study compares the ability of the most commonly used systems (ANRS, Rega, and Stanfords HIVdb) to predict virological outcome at 12, 24, and 48 weeks.
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Stochastic modelling of genotypic drug-resistance for human immunodeficiency virus towards long-term combination therapy optimization.
Bioinformatics
PUBLISHED: 05-19-2009
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Several mathematical models have been investigated for the description of viral dynamics in the human body: HIV-1 infection is a particular and interesting scenario, because the virus attacks cells of the immune system that have a role in the antibody production and its high mutation rate permits to escape both the immune response and, in some cases, the drug pressure. The viral genetic evolution is intrinsically a stochastic process, eventually driven by the drug pressure, dependent on the drug combinations and concentration: in this article the viral genotypic drug resistance onset is the main focus addressed. The theoretical basis is the modelling of HIV-1 population dynamics as a predator-prey system of differential equations with a time-dependent therapy efficacy term, while the viral genome mutation evolution follows a Poisson distribution. The instant probabilities of drug resistance are estimated by means of functions trained from in vitro phenotypes, with a roulette-wheel-based mechanisms of resistant selection. Simulations have been designed for treatments made of one and two drugs as well as for combination antiretroviral therapies. The effect of limited adherence to therapy was also analyzed. Sequential treatment change episodes were also exploited with the aim to evaluate optimal synoptic treatment scenarios.
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Advantages of predicted phenotypes and statistical learning models in inferring virological response to antiretroviral therapy from HIV genotype.
Antivir. Ther. (Lond.)
PUBLISHED: 05-12-2009
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Inferring response to antiretroviral therapy from the viral genotype alone is challenging. The utility of an intermediate step of predicting in vitro drug susceptibility is currently controversial. Here, we provide a retrospective comparison of approaches using either genotype or predicted phenotypes alone, or in combination.
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Standardized representation, visualization and searchable repository of antiretroviral treatment-change episodes.
AIDS Res Ther
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To identify the determinants of successful antiretroviral (ARV) therapy, researchers study the virological responses to treatment-change episodes (TCEs) accompanied by baseline plasma HIV-1 RNA levels, CD4+ T lymphocyte counts, and genotypic resistance data. Such studies, however, often differ in their inclusion and virological response criteria making direct comparisons of study results problematic. Moreover, the absence of a standard method for representing the data comprising a TCE makes it difficult to apply uniform criteria in the analysis of published studies of TCEs.
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Predicting response to antiretroviral treatment by machine learning: the EuResist project.
Intervirology
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For a long time, the clinical management of antiretroviral drug resistance was based on sequence analysis of the HIV genome followed by estimating drug susceptibility from the mutational pattern that was detected. The large number of anti-HIV drugs and HIV drug resistance mutations has prompted the development of computer-aided genotype interpretation systems, typically comprising rules handcrafted by experts via careful examination of in vitro and in vivo resistance data. More recently, machine learning approaches have been applied to establish data-driven engines able to indicate the most effective treatments for any patient and virus combination. Systems of this kind, currently including the Resistance Response Database Initiative and the EuResist engine, must learn from the large data sets of patient histories and can provide an objective and accurate estimate of the virological response to different antiretroviral regimens. The EuResist engine was developed by a European consortium of HIV and bioinformatics experts and compares favorably with the most commonly used genotype interpretation systems and HIV drug resistance experts. Next-generation treatment response prediction engines may valuably assist the HIV specialist in the challenging task of establishing effective regimens for patients harboring drug-resistant virus strains. The extensive collection and accurate processing of increasingly large patient data sets are eagerly awaited to further train and translate these systems from prototype engines into real-life treatment decision support tools.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.