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Ethnopharmacological relevance: Prostate cancer originates from cells inside a gland, which begin to grow out of control. In the world, prostate cancer is the most common cancer in the male population. New therapeutic strategies are needed for this tumor which still has a high mortality. A. arborescens leaves and aerial parts have various ethnopharmacological uses such as anti-spasmodic, and their decoctions were used to resolve urticaria, neuralgia and several lung diseases. Often this species has been also used to treat different inflammatory-related diseases such as cancer. Aim of the study: In a continuation of our research on essential oils from medicinal plants, we have selected, two essential oils from Artemisia arborescens L. (Compositae), an aromatic shrub widely used in traditional medicine. We evaluated their pro-apototic effect on androgen-sensitive (LNCaP) and androgen-insensitive (DU-145) human prostate cancer cells. In this study, we also evaluated the anti-Signal transducer and transcription factor 3 (STAT-3) activity of both essential oils in the human prostate cancer cell lines, and the treatment with Tumor necrosis factor (TNF)-Related Apoptosis (TRAIL). Materials and methods: The cells were exposed to essential oils for 72h and cell viability and cell membrane integrity were evaluated. Genomic DNA and the activity of caspase-3 was tested to confirm the cell death for apoptosis. Western blot analysis was employed to evaluate the expression of Bcl-2, Bax, cleaved caspase-3, cleaved caspase-9, Hsp70, STAT-3 and SOD proteins. Assays to evaluate reactive oxygen species (ROS) and GSH levels were also performed. Results: The results showed the capacity of two essential oils to activate an apoptotic process increasing the inhibition of Hsp70 and STAT-3 protein expression. In addition, our natural products sensitize LNCaP cells to Tumor necrosis factor (TNF)-Related Apoptosis (TRAIL)-induced apoptosis. Conclusions: In summary, our study provides a further contribution to the hypothesis of the use of essential oils, from traditional medicinal plants, for the treatment of tumors, and suggests that the combination of our samples with other anti-prostate cancer therapies could be used to affect prostate cancer.

There is growing concern about the consumption of synthetic cannabinoids (SCs), one of the largest groups of new psychoactive substances, its consequence on human health (general population and workers), and the continuous placing of new SCs on the market. Although drug-induced alterations in neuronal function remain an essential component for theories of drug addiction, accumulating evidence indicates the important role of activated astrocytes, whose essential and pleiotropic role in brain physiology and pathology is well recognized. The study aims to clarify the mechanisms of neurotoxicity induced by one of the most potent SCs, named MAM-2201 (a naphthoyl-indole derivative), by applying a novel three-dimensional (3D) cell culture model, mimicking the physiological and biochemical properties of brain tissues better than traditional two-dimensional in vitro systems. Specifically, human astrocyte spheroids, generated from the D384 astrocyte cell line, were treated with different MAM-2201 concentrations (1–30 µM) and exposure times (24–48 h). MAM-2201 affected, in a concentration- and time-dependent manner, the cell growth and viability, size and morphological structure, E-cadherin and extracellular matrix, CB1-receptors, glial fibrillary acidic protein, and caspase-3/7 activity. The findings demonstrate MAM-2201-induced cytotoxicity to astrocyte spheroids, and support the use of this human 3D cell-based model as species-specific in vitro tool suitable for the evaluation of neurotoxicity induced by other SCs.

Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To address this limitation, we propose a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FFNSL), that integrates a logic-based machine learning system capable of learning from noisy examples, with neural networks, in order to learn interpretable knowledge from labelled unstructured data. We demonstrate the generality of FFNSL on four neural-symbolic classification problems, where different pre-trained neural network models and logic-based machine learning systems are integrated to learn interpretable knowledge from sequences of images. We evaluate the robustness of our framework by using images subject to distributional shifts, for which the pre-trained neural networks may predict incorrectly and with high confidence. We analyse the impact that these shifts have on the accuracy of the learned knowledge and run-time performance, comparing FFNSL to tree-based and pure neural approaches. Our experimental results show that FFNSL outperforms the baselines by learning more accurate and interpretable knowledge with fewer examples.

This article sets the scene for the Special Issue ‘Reaching for allies?’ by setting out the research questions and structure of the Special Issue. Specifically, this introduction reviews the state of the art of dialectics interweaving International Relations and Area Studies. Specifically, it focuses on tracing the genealogy of these debates, identifying the actors engaged with them, as well as, mapping those sites where such transdisciplinary knowledge is produced and circulated. We also provide an assessment of the interaction between the two disciplinary traditions as scholarly disciplines by reviewing the field as it had developed in the last decade since 2013. In order to do so, we present data on the brokers of this dialogue by analysing top-ranked Journals across regions, dedicated Special Issues on the matter as well as main international conferences and participants. Overall, this article provides a threefold contribution: first, we provide an account of the globalization of knowledge production and circulation that has also increasingly decentred, valuing local peculiarities and epistemological traditions beyond the Western academia(s). Second, we assess and discuss how Western and non-Western academics have contoured concepts which demand and entail site-intensive techniques of enquiry, exposure to complexities on the grounds, ethnographic sensitivity, and, at the same time, comparative endeavours going beyond area specialisms. Third, by looking at international and regional policy-making milieus with attention to context-specificity, we believe critical policy-relevant implications can be discussed, specifically in relation to local ownership and bottom-up approaches.

This article aims at reviewing and mapping the actors and sites involved in knowledge production on radicalisation, focusing on the EU context. We did so by collecting information on EU-funded research projects, which cover subjects of radicalisation and violent extremism, and are sponsored through either the Seventh Framework Programme or Horizon 2020; and analysing them from the point of view of the actors involved in the project implementation. Complementarily, we have focused on the actors involved in the design of the calls for project proposals and the strategic documents framing and embedding them. By premising on the assumption that experts' knowledge and scientific knowledge are sources of leverage and contention in security-related policy areas, we intend to have a well-rounded grasp of the actors that are involved in the production and utilisation of such knowledge in policy-making, their organisation, their gate-keeping capacity and the instruments at their disposal.