Drug costs and benefits of medical treatments in high-unmet need solid tumours in the Nordic countries

Introduction: Regional and hospital decision-makers increasingly require analyses assessing the cost-benefit profile of new cancer drugs. This analysis evaluates the cost-benefit profile of nano albumin-bound paclitaxel (nab-paclitaxel) in pancreatic cancer, versus other drugs indicated in high-unmet need solid tumour indications in Nordic countries (Sweden, Denmark, Finland, Norway and Sweden). Methods: For a selected number of cancer dugs, approved for metastatic cancer or non-curable treatment intention patients by the European Medicine Agency (EMA) after 2000, and indicated in high-unmet need solid tumours (defined as OS in first line for trial comparator ≤12 months), a regression analysis was conducted. Overall treatment costs of cancer drugs, divided by OS and PFS months, were related to the clinical improvement offered versus trial comparator. Results: Eleven of 42 drugs (26.2%) with at least one indication in solid tumours met inclusion criteria. On average, a good (R 2 = 0.5359) fit between costs per OS month and OS relative benefit versus trial comparator was observed. Nab-paclitaxel offered an OS improvement of +27% versus trial comparator (average improvement: +31%), at a cost per OS month of €1,684 (average cost: €2,247). Correlation between costs per PFS month and relative PFS benefit versus trial comparator was still observed, but the goodness of fit was lower (R 2 = 0.1853) than for the OS analysis. Conclusion: Treatment costs of new cancer therapies should reflect their clinical value, consistently among different indications with comparable characteristics. Nab-paclitaxel, recently approved in pancreatic cancer, showed a similar cost per OS or PFS month ratio compared to other drugs for high-unmet need solid tumours. </p

Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program

Light-ion production in the interaction of 96 MeV neutrons with silicon

Double-differential cross sections for light-ion (p, d, t, He-3 and alpha) production in silicon, induced by 96 MeV neutrons are reported. Energy spectra are measured at eight laboratory angles, ranging from 20 degrees to 160 degrees in steps of 20 degrees. Procedures for data taking and data reduction are presented. Deduced energy-differential, angle-differential and production cross sections are reported. Experimental cross sections are compared to theoretical reaction model calculations and experimental data in the literature.Comment: 23 pages, 10 figures, added wrap-around correction (see section 4.3) leading to changed cross-sections and figures, accepted Phys. Rev.

Implication of the overlap representation for modelling generalized parton distributions

Based on a field theoretically inspired model of light-cone wave functions, we derive valence-like generalized parton distributions and their double distributions from the wave function overlap in the parton number conserved s-channel. The parton number changing contributions in the t-channel are restored from duality. In our construction constraints of positivity and polynomiality are simultaneously satisfied and it also implies a model dependent relation between generalized parton distributions and transverse momentum dependent parton distribution functions. The model predicts that the t-behavior of resulting hadronic amplitudes depends on the Bjorken variable x_Bj. We also propose an improved ansatz for double distributions that embeds this property.Comment: 15 pages, 8 eps figure

Comparative analysis of the lambda-interferons IL-28A and IL-29 regarding their transcriptome and their antiviral properties against hepatitis C virus.

Specific differences in signaling and antiviral properties between the different Lambda-interferons, a novel group of interferons composed of IL-28A, IL-28B and IL-29, are currently unknown. This is the first study comparatively investigating the transcriptome and the antiviral properties of the Lambda-interferons IL-28A and IL-29. Expression studies were performed by microarray analysis, quantitative PCR (qPCR), reporter gene assays and immunoluminometric assays. Signaling was analyzed by Western blot. HCV replication was measured in Huh-7 cells expressing subgenomic HCV replicon. All hepatic cell lines investigated as well as primary hepatocytes expressed both IFN-λ receptor subunits IL-10R2 and IFN-λR1. Both, IL-28A and IL-29 activated STAT1 signaling. As revealed by microarray analysis, similar genes were induced by both cytokines in Huh-7 cells (IL-28A: 117 genes; IL-29: 111 genes), many of them playing a role in antiviral immunity. However, only IL-28A was able to significantly down-regulate gene expression (n = 272 down-regulated genes). Both cytokines significantly decreased HCV replication in Huh-7 cells. In comparison to liver biopsies of patients with non-viral liver disease, liver biopsies of patients with HCV showed significantly increased mRNA expression of IL-28A and IL-29. Moreover, IL-28A serum protein levels were elevated in HCV patients. In a murine model of viral hepatitis, IL-28 expression was significantly increased. IL-28A and IL-29 are up-regulated in HCV patients and are similarly effective in inducing antiviral genes and inhibiting HCV replication. In contrast to IL-29, IL-28A is a potent gene repressor. Both IFN-λs may have therapeutic potential in the treatment of chronic HCV

The HY5-PIF regulatory module coordinates light and temperature control of photosynthetic gene transcription

The ability to interpret daily and seasonal alterations in light and temperature signals is essential for plant survival. This is particularly important during seedling establishment when the phytochrome photoreceptors activate photosynthetic pigment production for photoautotrophic growth. Phytochromes accomplish this partly through the suppression of phytochrome interacting factors (PIFs), negative regulators of chlorophyll and carotenoid biosynthesis. While the bZIP transcription factor long hypocotyl 5 (HY5), a potent PIF antagonist, promotes photosynthetic pigment accumulation in response to light. Here we demonstrate that by directly targeting a common promoter cis-element (G-box), HY5 and PIFs form a dynamic activation-suppression transcriptional module responsive to light and temperature cues. This antagonistic regulatory module provides a simple, direct mechanism through which environmental change can redirect transcriptional control of genes required for photosynthesis and photoprotection. In the regulation of photopigment biosynthesis genes, HY5 and PIFs do not operate alone, but with the circadian clock. However, sudden changes in light or temperature conditions can trigger changes in HY5 and PIFs abundance that adjust the expression of common target genes to optimise photosynthetic performance and growth

Ab initio atomistic thermodynamics and statistical mechanics of surface properties and functions

Previous and present "academic" research aiming at atomic scale understanding is mainly concerned with the study of individual molecular processes possibly underlying materials science applications. Appealing properties of an individual process are then frequently discussed in terms of their direct importance for the envisioned material function, or reciprocally, the function of materials is somehow believed to be understandable by essentially one prominent elementary process only. What is often overlooked in this approach is that in macroscopic systems of technological relevance typically a large number of distinct atomic scale processes take place. Which of them are decisive for observable system properties and functions is then not only determined by the detailed individual properties of each process alone, but in many, if not most cases also the interplay of all processes, i.e. how they act together, plays a crucial role. For a "predictive materials science modeling with microscopic understanding", a description that treats the statistical interplay of a large number of microscopically well-described elementary processes must therefore be applied. Modern electronic structure theory methods such as DFT have become a standard tool for the accurate description of individual molecular processes. Here, we discuss the present status of emerging methodologies which attempt to achieve a (hopefully seamless) match of DFT with concepts from statistical mechanics or thermodynamics, in order to also address the interplay of the various molecular processes. The new quality of, and the novel insights that can be gained by, such techniques is illustrated by how they allow the description of crystal surfaces in contact with realistic gas-phase environments.Comment: 24 pages including 17 figures, related publications can be found at http://www.fhi-berlin.mpg.de/th/paper.htm

Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks.

<div><p>Genome-scale metabolic networks provide a comprehensive structural framework for modeling genotype-phenotype relationships through flux simulations. The solution space for the metabolic flux state of the cell is typically very large and optimization-based approaches are often necessary for predicting the active metabolic state under specific environmental conditions. The objective function to be used in such optimization algorithms is directly linked with the biological hypothesis underlying the model and therefore it is one of the most relevant parameters for successful modeling. Although linear combination of selected fluxes is widely used for formulating metabolic objective functions, we show that the resulting optimization problem is sensitive towards stoichiometry representation of the metabolic network. This undesirable sensitivity leads to different simulation results when using numerically different but biochemically equivalent stoichiometry representations and thereby makes biological interpretation intrinsically subjective and ambiguous. We hereby propose a new method, Minimization of Metabolites Balance (MiMBl), which decouples the artifacts of stoichiometry representation from the formulation of the desired objective functions, by casting objective functions using metabolite turnovers rather than fluxes. By simulating perturbed metabolic networks, we demonstrate that the use of stoichiometry representation independent algorithms is fundamental for unambiguously linking modeling results with biological interpretation. For example, MiMBl allowed us to expand the scope of metabolic modeling in elucidating the mechanistic basis of several genetic interactions in <em>Saccharomyces cerevisiae</em>.</p> </div