CuxCeMgAlO mixed oxide catalysts derived from multicationic LDH precursors for methane total oxidation

International audienc

Manufacturing and Supply Chain Flexibility: Building an Integrative Conceptual Model Through Systematic Literature Review and Bibliometric Analysis

The purpose of this study is twofold: first, to establish the current themes on the topic of manufacturing and supply chain flexibility (MSCF), assess their level of maturity in relation to each other, identify the emerging ones and reflect on how they can inform each other, and second, to develop a conceptual model of MSCF that links different themes connect and highlight future research opportunities. The study builds on a sample of 222 articles published from 1996 to 2018 in international, peer-reviewed journals. The analysis of the sample involves two complementary approaches: the co-word technique to identify the thematic clusters as well as their relative standing and a critical reflection on the papers to explain the intellectual content of these thematic clusters. The results of the co-word analysis show that MSCF is a dynamic topic with a rich and complex structure that comprises five thematic clusters. The value chain, capability and volatility clusters showed research topics that were taking a central role in the discussion on MSCF but were not mature yet. The SC purchasing practices and SC planning clusters involved work that was more focused and could be considered more mature. These clusters were then integrated in a framework that built on the competence–capability perspective and identified the major structural and infrastructural elements of MSCF as well as its antecedents and consequences. This paper proposes an integrative framework helping managers keep track the various decisions they need to make to increase flexibility from the viewpoint of the entire value chain

How the Potassium Channel Response of T Lymphocytes to the Tumor Microenvironment Shapes Antitumor Immunity

Competent antitumor immune cells are fundamental for tumor surveillance and combating active cancers. Once established, tumors generate a tumor microenvironment (TME) consisting of complex cellular and metabolic elements that serve to suppress the function of antitumor immune cells. T lymphocytes are key cellular elements of the TME. In this review, we explore the role of ion channels, particularly K+ channels, in mediating the suppressive effects of the TME on T cells. First, we will review the complex network of ion channels that mediate Ca2+ influx and control effector functions in T cells. Then, we will discuss how multiple features of the TME influence the antitumor capabilities of T cells via ion channels. We will focus on hypoxia, adenosine, and ionic imbalances in the TME, as well as overexpression of programmed cell death ligand 1 by cancer cells that either suppress K+ channels in T cells and/or benefit from regulating these channels’ activity, ultimately shaping the immune response. Finally, we will review some of the cancer treatment implications related to ion channels. A better understanding of the effects of the TME on ion channels in T lymphocytes could promote the development of more effective immunotherapies, especially for resistant solid malignancies

First-line treatment with olaparib for early stage BRCA-positive ovarian cancer: may it be possible? Hypothesis potentially generating a line of research

Olaparib is currently approved in maintenance treatment of advanced ovarian cancer after response to first-line chemotherapy for breast related cancer antigens (BRCA) mutated patients. The use of this agent is based on data from SOLO1 study that observed a decreased risk of disease progression or death and a median progression-free survival about 36 months longer in case of therapy with olaparib. However, this trial recruited only patients with advanced stage ovarian cancer. The aim of this review is to retrace the available data in order to clarify the potential efficacy and feasibility of olaparib administration in newly diagnosed epithelial ovarian cancer also in early stages

Data from: Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study

Background: For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier comparison studies that have involved larger synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI. Methods: 12 different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naive Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 86 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy. Surrogate ground truth for disease extent on MRI was established by expert annotation of pre-operative MRI through spatial correlation with corresponding ex vivo whole-mount histology sections. Classifier accuracy in detecting PCa extent on MRI on a per-voxel basis was evaluated via area under the ROC curve. Results: The boosted DT classifier yielded the highest cross-validated AUC (= 0.744) for detecting PCa in the discovery cohort. However, in independent validation, the boosted QDA classifier was identified as the most accurate and robust for voxel-wise detection of PCa extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most accurate and robust classifier was the single QDA classifier, which also enjoyed the advantage of significantly lower computation times compared to any of the other methods. Conclusions: Our results therefore suggest that simpler classifiers (such as QDA and its ensemble variants) may be more robust, accurate, and efficient for prostate cancer CAD problems, especially in the context of multi-site validation