367 research outputs found
Reproducibility of 24h energy expenditure measurements using a human whole body indirect calorimeter
IFN stimulated gene expression in the liver is a better predictor of treatment response in chronic hepatitis c than the IL28b (IFN lambda 3) genotype
Background: Therapy of chronic hepatitis C (CHC) with pegIFNa/ribavirin achieves sustained virologic response (SVR) in ~55%. Pre-activation of the endogenous interferon system in the liver is associated non-response (NR). Recently, genome-wide association studies described associations of allelic variants near the IL28B (IFNλ3) gene with treatment response and with spontaneous clearance of the virus. We investigated if the IL28B genotype determines the constitutive expression of IFN stimulated genes (ISGs) in the liver of patients with CHC.
Methods: We genotyped 93 patients with CHC for 3 IL28B single nucleotide polymorphisms (SNPs, rs12979860, rs8099917, rs12980275), extracted RNA from their liver biopsies and quantified the expression of IL28B and of 8 previously identified classifier genes which discriminate between SVR and NR (IFI44L, RSAD2, ISG15, IFI22, LAMP3, OAS3, LGALS3BP and HTATIP2). Decision tree ensembles in the form of a random forest classifier were used to calculate the relative predictive power of these different variables in a multivariate analysis.
Results: The minor IL28B allele (bad risk for treatment response) was significantly associated with increased expression of ISGs, and, unexpectedly, with decreased expression of IL28B. Stratification of the patients into SVR and NR revealed that ISG expression was conditionally independent from the IL28B genotype, i.e. there was an increased expression of ISGs in NR compared to SVR irrespective of the IL28B genotype. The random forest feature score (RFFS) identified IFI27 (RFFS = 2.93), RSAD2 (1.88) and HTATIP2 (1.50) expression and the HCV genotype (1.62) as the strongest predictors of treatment response. ROC curves of the IL28B SNPs showed an AUC of 0.66 with an error rate (ERR) of 0.38. A classifier with the 3 best classifying genes showed an excellent test performance with an AUC of 0.94 and ERR of 0.15. The addition of IL28B genotype information did not improve the predictive power of the 3-gene classifier.
Conclusions: IL28B genotype and hepatic ISG expression are conditionally independent predictors of treatment response in CHC. There is no direct link between altered IFNλ3 expression and pre-activation of the endogenous system in the liver. Hepatic ISG expression is by far the better predictor for treatment response than IL28B genotype
Energy balances of eight volunteers fed on diets supplemented with either lactitol or saccharose
Machine learning algorithms evaluate immune response to novel Mycobacterium tuberculosis antigens for diagnosis of tuberculosis
RationaleTuberculosis diagnosis in children remains challenging. Microbiological confirmation of tuberculosis disease is often lacking, and standard immunodiagnostic including the tuberculin skin test and interferon-gamma release assay for tuberculosis infection has limited sensitivity. Recent research suggests that inclusion of novel Mycobacterium tuberculosis antigens has the potential to improve standard immunodiagnostic tests for tuberculosis.ObjectiveTo identify optimal antigen-cytokine combinations using novel Mycobacterium tuberculosis antigens and cytokine read-outs by machine learning algorithms to improve immunodiagnostic assays for tuberculosis.MethodsA total of 80 children undergoing investigation of tuberculosis were included (15 confirmed tuberculosis disease, five unconfirmed tuberculosis disease, 28 tuberculosis infection and 32 unlikely tuberculosis). Whole blood was stimulated with 10 novel Mycobacterium tuberculosis antigens and a fusion protein of early secretory antigenic target (ESAT)-6 and culture filtrate protein (CFP) 10. Cytokines were measured using xMAP multiplex assays. Machine learning algorithms defined a discriminative classifier with performance measured using area under the receiver operating characteristics.Measurements and main resultsWe found the following four antigen-cytokine pairs had a higher weight in the discriminative classifier compared to the standard ESAT-6/CFP-10-induced interferon-gamma: Rv2346/47c- and Rv3614/15c-induced interferon-gamma inducible protein-10; Rv2031c-induced granulocyte-macrophage colony-stimulating factor and ESAT-6/CFP-10-induced tumor necrosis factor-alpha. A combination of the 10 best antigen-cytokine pairs resulted in area under the curve of 0.92 +/- 0.04.ConclusionWe exploited the use of machine learning algorithms as a key tool to evaluate large immunological datasets. This identified several antigen-cytokine pairs with the potential to improve immunodiagnostic tests for tuberculosis in children.Immunogenetics and cellular immunology of bacterial infectious disease
Beyond the randomized clinical trial: innovative data science to close the pediatric evidence gap
Despite the application of advanced statistical and pharmacometric approaches to pediatric trial data, a large pediatric evidence gap still remains. Here, we discuss how to collect more data from children by using real-world data from electronic health records, mobile applications, wearables, and social media. The large datasets collected with these approaches enable, and may demand, the use of artificial intelligence and machine learning to allow the data to be analyzed for decision-making. Applications of this approach are presented, which include the prediction of future clinical complications, medical image analysis, identification of new pediatric endpoints and biomarkers, the prediction of treatment non-responders and the prediction of placebo-responders for trial enrichment. Finally, we discuss how to bring machine learning from science to pediatric clinical practice. We conclude that advantage should be taken of the current opportunities offered by innovations in data science and machine learning to close the pediatric evidence gap.Pharmacolog
The 3D Structure of N132D in the LMC: A Late-Stage Young Supernova Remnant
We have used the Wide Field Spectrograph (WiFeS) on the 2.3m telescope at
Siding Spring Observatory to map the [O III] 5007{\AA} dynamics of the young
oxygen-rich supernova remnant N132D in the Large Magellanic Cloud. From the
resultant data cube, we have been able to reconstruct the full 3D structure of
the system of [O III] filaments. The majority of the ejecta form a ring of
~12pc in diameter inclined at an angle of 25 degrees to the line of sight. We
conclude that SNR N132D is approaching the end of the reverse shock phase
before entering the fully thermalized Sedov phase of evolution. We speculate
that the ring of oxygen-rich material comes from ejecta in the equatorial plane
of a bipolar explosion, and that the overall shape of the SNR is strongly
influenced by the pre-supernova mass loss from the progenitor star. We find
tantalizing evidence of a polar jet associated with a very fast oxygen-rich
knot, and clear evidence that the central star has interacted with one or more
dense clouds in the surrounding ISM.Comment: Accepted for Publication in Astrophysics & Space Science, 18pp, 8
figure
Low Complexity Regularization of Linear Inverse Problems
Inverse problems and regularization theory is a central theme in contemporary
signal processing, where the goal is to reconstruct an unknown signal from
partial indirect, and possibly noisy, measurements of it. A now standard method
for recovering the unknown signal is to solve a convex optimization problem
that enforces some prior knowledge about its structure. This has proved
efficient in many problems routinely encountered in imaging sciences,
statistics and machine learning. This chapter delivers a review of recent
advances in the field where the regularization prior promotes solutions
conforming to some notion of simplicity/low-complexity. These priors encompass
as popular examples sparsity and group sparsity (to capture the compressibility
of natural signals and images), total variation and analysis sparsity (to
promote piecewise regularity), and low-rank (as natural extension of sparsity
to matrix-valued data). Our aim is to provide a unified treatment of all these
regularizations under a single umbrella, namely the theory of partial
smoothness. This framework is very general and accommodates all low-complexity
regularizers just mentioned, as well as many others. Partial smoothness turns
out to be the canonical way to encode low-dimensional models that can be linear
spaces or more general smooth manifolds. This review is intended to serve as a
one stop shop toward the understanding of the theoretical properties of the
so-regularized solutions. It covers a large spectrum including: (i) recovery
guarantees and stability to noise, both in terms of -stability and
model (manifold) identification; (ii) sensitivity analysis to perturbations of
the parameters involved (in particular the observations), with applications to
unbiased risk estimation ; (iii) convergence properties of the forward-backward
proximal splitting scheme, that is particularly well suited to solve the
corresponding large-scale regularized optimization problem
Heavy quarkonium: progress, puzzles, and opportunities
A golden age for heavy quarkonium physics dawned a decade ago, initiated by
the confluence of exciting advances in quantum chromodynamics (QCD) and an
explosion of related experimental activity. The early years of this period were
chronicled in the Quarkonium Working Group (QWG) CERN Yellow Report (YR) in
2004, which presented a comprehensive review of the status of the field at that
time and provided specific recommendations for further progress. However, the
broad spectrum of subsequent breakthroughs, surprises, and continuing puzzles
could only be partially anticipated. Since the release of the YR, the BESII
program concluded only to give birth to BESIII; the -factories and CLEO-c
flourished; quarkonium production and polarization measurements at HERA and the
Tevatron matured; and heavy-ion collisions at RHIC have opened a window on the
deconfinement regime. All these experiments leave legacies of quality,
precision, and unsolved mysteries for quarkonium physics, and therefore beg for
continuing investigations. The plethora of newly-found quarkonium-like states
unleashed a flood of theoretical investigations into new forms of matter such
as quark-gluon hybrids, mesonic molecules, and tetraquarks. Measurements of the
spectroscopy, decays, production, and in-medium behavior of c\bar{c}, b\bar{b},
and b\bar{c} bound states have been shown to validate some theoretical
approaches to QCD and highlight lack of quantitative success for others. The
intriguing details of quarkonium suppression in heavy-ion collisions that have
emerged from RHIC have elevated the importance of separating hot- and
cold-nuclear-matter effects in quark-gluon plasma studies. This review
systematically addresses all these matters and concludes by prioritizing
directions for ongoing and future efforts.Comment: 182 pages, 112 figures. Editors: N. Brambilla, S. Eidelman, B. K.
Heltsley, R. Vogt. Section Coordinators: G. T. Bodwin, E. Eichten, A. D.
Frawley, A. B. Meyer, R. E. Mitchell, V. Papadimitriou, P. Petreczky, A. A.
Petrov, P. Robbe, A. Vair
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