SAM levels, gene expression of SAM synthetase, methionine synthase and ACC oxidase, and ethylene emission from N. suaveolens flowers

S′adenosyl-l-methionine (SAM) is a ubiquitous methyl donor and a precursor in the biosynthesis of ethylene, polyamines, biotin, and nicotianamine in plants. Only limited information is available regarding its synthesis (SAM cycle) and its concentrations in plant tissues. The SAM concentrations in flowers of Nicotiana suaveolens were determined during day/night cycles and found to fluctuate rhythmically between 10 and 50 nmol g−1 fresh weight. Troughs of SAM levels were measured in the evening and night, which corresponds to the time when the major floral scent compound, methyl benzoate, is synthesized by a SAM dependent methyltransferase (NsBSMT) and when this enzyme possesses its highest activity. The SAM synthetase (NsSAMS1) and methionine synthase (NsMS1) are enzymes, among others, which are involved in the synthesis and regeneration of SAM. Respective genes were isolated from a N. suaveolens petal cDNA library. Transcript accumulation patterns of both SAM regenerating enzymes matched perfectly those of the bifunctional NsBSMT; maximum mRNA accumulations of NsMS1 and NsSAMS1 were attained in the evening. Ethylene, which is synthesized from SAM, reached only low levels of 1–2 ppbv in N. suaveolens flowers. It is emitted in a burst at the end of the life span of the flowers, which correlates with the increased expression of the 1-aminocyclopropane-1-carboxylate oxidase (NsACO)

Cost effectiveness thresholds: the past, the present and the future

Cost-effectiveness (CE) thresholds are being discussed more frequently and there have been many new developments in this area; however, there is a lack of understanding about what thresholds mean and their implications. This paper provides an overview of the CE threshold literature. First, the meaning of a CE threshold and the key assumptions involved (perfect divisibility, marginal increments in budget, etc.) are highlighted using a hypothetical example, and the use of historic/heuristic estimates of the threshold is noted along with their limitations. Recent endeavours to estimate the empirical value of the thresholds, both from the supply side and the demand side, are then presented. The impact on CE thresholds of future directions for the field, such as thresholds across sectors and the incorporation of multiple criteria beyond quality-adjusted life-years as a measure of ‘value’, are highlighted. Finally, a number of common issues and misconceptions associated with CE thresholds are addressed

Simulation evaluation of statistical properties of methods for indirect and mixed treatment comparisons

Background: Indirect treatment comparison (ITC) and mixed treatment comparisons (MTC) have been increasingly used in network meta-analyses. This simulation study comprehensively investigated statistical properties and performances of commonly used ITC and MTC methods, including simple ITC (the Bucher method), frequentist and Bayesian MTC methods. Methods: A simple network of three sets of two-arm trials with a closed loop was simulated. Different simulation scenarios were based on different number of trials, assumed treatment effects, extent of heterogeneity, bias and inconsistency. The performance of the ITC and MTC methods was measured by the type I error, statistical power, observed bias and mean squared error (MSE). Results: When there are no biases in primary studies, all ITC and MTC methods investigated are on average unbiased. Depending on the extent and direction of biases in different sets of studies, ITC and MTC methods may be more or less biased than direct treatment comparisons (DTC). Of the methods investigated, the simple ITC method has the largest mean squared error (MSE). The DTC is superior to the ITC in terms of statistical power and MSE. Under the simulated circumstances in which there are no systematic biases and inconsistencies, the performances of MTC methods are generally better than the performance of the corresponding DTC methods. For inconsistency detection in network meta-analysis, the methods evaluated are on average unbiased. The statistical power of commonly used methods for detecting inconsistency is very low. Conclusions: The available methods for indirect and mixed treatment comparisons have different advantages and limitations, depending on whether data analysed satisfies underlying assumptions. To choose the most valid statistical methods for research synthesis, an appropriate assessment of primary studies included in evidence network is required