Early presentation of primary glioblastoma

Background Clinical and neuroimaging findings of glioblastomas (GBM) at an early stage have rarely been described and those tumors are most probably under-diagnosed. Furthermore, their genetic alterations, to our knowledge, have never been previously reported. Methods We report the clinical as well as neuroimaging findings of four early cases of patients with GBM. Results In our series, early stage GBM occurred at a mean age of 57 years. All patients had seizures as their first symptom. In all early stages, MRI showed a hyperintense signal on T2-weighted sequences and an enhancement on GdE-T1WI sequences. A hyperintense signal on diffusion sequences with a low ADC value was also found. These early observed occurrences of GBM developed rapidly and presented the MRI characteristics of classic GBM within a few weeks. The GBM size was multiplied by 32 in one month. Immunohistochemical analysis indicated the de novo nature of these tumors, i.e. absence of mutant IDH1 R132H protein expression, which is a diagnostic marker of low-grade diffuse glioma and secondary GBM. Conclusions A better knowledge of early GBM presentation would allow a more suitable management of the patients and may improve their prognosis

Stochastic and epistemic uncertainty propagation in LCA

Purpose: When performing uncertainty propagation, most LCA practitioners choose to represent uncertainties by single probability distributions and to propagate them using stochastic methods. However the selection of single probability distributions appears often arbitrary when faced with scarce information or expert judgement (epistemic uncertainty). Possibility theory has been developed over the last decades to address this problem. The objective of this study is to present a methodology that combines probability and possibility theories to represent stochastic and epistemic uncertainties in a consistent manner and apply it to LCA. A case study is used to show the uncertainty propagation performed with the proposed method and compare it to propagation performed using probability and possibility theories alone. Methods: Basic knowledge on the probability theory is first recalled, followed by a detailed description of hal-00811827, version 1- 11 Apr 2013 epistemic uncertainty representation using fuzzy intervals. The propagation methods used are the Monte Carlo analysis for probability distribution and an optimisation on alpha-cuts for fuzzy intervals. The proposed method (noted IRS) generalizes the process of random sampling to probability distributions as well as fuzzy intervals, thus making the simultaneous use of both representations possible

Three dimensional in vitro models of cancer: Bioprinting multilineage glioblastoma models

International audienceThree dimensional (3D) bioprinting of multiple cell types within optimised extracellular matrices has the potential to more closely model the 3D environment of human physiology and disease than current alternatives. In this study, we used a multi-nozzle extrusion bioprinter to establish models of glioblastoma made up of cancer and stromal cells printed within matrices comprised of alginate modified with RGDS cell adhesion peptides, hyaluronic acid and collagen-1. Methods were developed using U87MG glioblastoma cells and MM6 monocyte/macrophages, whilst more disease relevant constructs contained glioblastoma stem cells (GSCs), co-printed with glioma associated stromal cells (GASCs) and microglia. Printing parameters were optimised to promote cell-cell interaction, avoiding the 'caging in' of cells due to overly dense cross-linking. Such printing had a negligible effect on cell viability, and cells retained robust metabolic activity and proliferation. Alginate gels allowed the rapid recovery of printed cell protein and RNA, and fluorescent reporters provided analysis of protein kinase activation at the single cell level within printed constructs. GSCs showed more resistance to chemotherapeutic drugs in 3D printed tumour constructs compared to 2D monolayer cultures, reflecting the clinical situation. In summary, a novel 3D bioprinting strategy is developed which allows control over the spatial organisation of tumour constructs for pre-clinical drug sensitivity testing and studies of the tumour microenvironment

The challenges of applying planetary boundaries as a basis for strategic decision-making in companies with global supply chains

The Planetary Boundaries (PB) framework represents a significant advance in specifying the ecological constraints on human development. However, to enable decision-makers in business and public policy to respect these constraints in strategic planning, the PB framework needs to be developed to generate practical tools. With this objective in mind, we analyse the recent literature and highlight three major scientific and technical challenges in operationalizing the PB approach in decision-making: first, identification of thresholds or boundaries with associated metrics for different geographical scales; second, the need to frame approaches to allocate fair shares in the 'safe operating space' bounded by the PBs across the value chain and; third, the need for international bodies to co-ordinate the implementation of the measures needed to respect the Planetary Boundaries. For the first two of these challenges, we consider how they might be addressed for four PBs: climate change, freshwater use, biosphere integrity and chemical pollution and other novel entities. Four key opportunities are identified: (1) development of a common system of metrics that can be applied consistently at and across different scales; (2) setting 'distance from boundary' measures that can be applied at different scales; (3) development of global, preferably open-source, databases and models; and (4) advancing understanding of the interactions between the different PBs. Addressing the scientific and technical challenges in operationalizing the planetary boundaries needs be complemented with progress in addressing the equity and ethical issues in allocating the safe operating space between companies and sectors

An environmental evaluation of food waste downstream management options: a hybrid LCA approach

Food waste treatment methods have been typically analysed using current energy generation conditions. To correctly evaluate treatment methods, they must be compared under existing and potential decarbonisation scenarios. This paper holistically quantifies the environmental impacts of three food waste downstream management options—incineration, composting, and anaerobic digestion (AD). Methods The assessment was carried out using a novel hybrid input–output-based life cycle assessment method (LCA), for 2014, and in a future decarbonised economy. The method introduces expanded system boundaries which reduced the level of incompleteness, a previous limitation of process-based LCA. Results Using the 2014 UK energy mix, composting achieved the best score for seven of 14 environmental impacts, while AD scored second best for ten. Incineration had the highest environmental burdens in six impacts. Uncertainties in the LCA data made it difficult determine best treatment option. There was significant environmental impact from capital goods, meaning current treatment facilities should be used for their full lifespan. Hybrid IO LCA’s included additional processes and reduced truncation error increasing overall captured environmental impacts of composting, AD, and incineration by 26, 10 and 26%, respectively. Sensitivity and Monte Carlo analysis evaluate the methods robustness and illustrate the uncertainty of current LCA methods. Major implication: hybrid IO-LCA approaches must become the new norm for LCA. Conclusion This study provided a deeper insight of the overall environmental performance of downstream food waste treatment options including ecological burdens associated with capital goods. Keywords Anaerobic digestion Incineration Composting Food waste Hybrid life cycle assessment Capital good