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Background Prostaglandin E 2 (PGE 2 ) acts via its EP4 receptor as a cytokine amplifier (e.g., interleukin [IL]-6) and induces the differentiation and expansion of inflammatory T-helper (Th) lymphocytes. These mechanisms play a key role in the onset and progression of rheumatoid arthritis (RA). We present the pharmacological characterisation of CR6086, a novel EP4 receptor antagonist, and provide evidence for its potential as a disease-modifying anti-rheumatic drug (DMARD). Methods CR6086 affinity and pharmacodynamics were studied in EP4-expressing HEK293 cells by radioligand binding and cyclic adenosine monophosphate (cAMP) production, respectively. In immune cells, IL-6 and vascular endothelial growth factor (VEGF) expression were analysed by RT-PCR, and IL-23 and IL-17 release were measured by enzyme-linked immunosorbent assay (ELISA). In collagen-induced arthritis (CIA) models, rats or mice were immunised with bovine collagen type II. Drugs were administered orally (etanercept and methotrexate intraperitoneally) starting at disease onset. Arthritis progression was evaluated by oedema, clinical score and histopathology. Anti-collagen II immunoglobulin G antibodies were measured by ELISA. Results CR6086 showed selectivity and high affinity for the human EP4 receptor ( K i = 16.6 nM) and functioned as a pure antagonist (half-maximal inhibitory concentration, 22 nM) on PGE 2 -stimulated cAMP production. In models of human immune cells in culture, CR6086 reduced key cytokine players of RA (IL-6 and VEGF expression in macrophages, IL-23 release from dendritic cells, IL-17 release from Th17 cells). In the CIA model of RA in rats and mice, CR6086 significantly improved all features of arthritis: severity, histology, inflammation and pain. In rats, CR6086 was better than the selective cyclooxygenase-2 inhibitor rofecoxib and at least as effective as the Janus kinase inhibitor tofacitinib. In mice, CR6086 and the biologic DMARD etanercept were highly effective, whereas the non-steroidal anti-inflammatory drug naproxen was ineffective. Importantly, in a study of CR6086/methotrexate, combined treatment greatly improved the effect of a fully immunosuppressive dose of methotrexate. Conclusions CR6086 is a novel, potent EP4 antagonist showing favourable immunomodulatory properties, striking DMARD effects in rodents, and anti-inflammatory activity targeted to immune-mediated inflammatory diseases and distinct from the general effects of cyclooxygenase inhibitors. These results support the clinical development of CR6086, both as a stand-alone DMARD and as a combination therapy with methotrexate. The proof-of-concept trial in patients with RA is ongoing.

CR4056, (2-phenyl-6-(1H-imidazol-1yl) quinazoline), an imidazoline-2 (I2) receptor ligand, is a promising analgesic drug that has been reported to be effective in several animal models of pain. The aim of this study was to evaluate the effects of CR4056 in two well-established rat models of osteoarthritis (OA), mimicking the painful and structural components of human OA. Knee OA was induced either by single intra-articular injection of monoiodoacetate (MIA) or by medial meniscal tear (MMT) in the right knee of male rats. In the MIA model, allodynia and hyperalgesia were measured as paw withdrawal threshold to mechanical stimulation. In the MMT model, pain behavior was analyzed as weight-bearing asymmetry (i.e. difference in hind paw weight distribution, HPWD) between the injured and the contralateral limbs. Acute oral administration of CR4056, 14 days after MIA injection, significantly and dose-dependently reduced allodynia and hyperalgesia 90 minutes after treatment, whereas acute naproxen administration significantly reduced allodynia but not hyperalgesia. After 7 days of repeated treatment, both CR4056 and naproxen showed significant anti-allodynic and anti-hyperalgesic effects in the MIA model. Rats undergoing MMT surgery developed a significant and progressive asymmetry in HPWD compared with sham-operated animals. Repeated treatment with CR4056 significantly reduced the progression of the pain behavior, whereas naproxen had no effects. The data presented here show that the I2 ligand CR4056 could be a new effective treatment for OA pain. The compound is currently under Phase II clinical evaluation for this indication.

Two decades of investigations have failed to unequivocally clarify the functions and the molecular nature of imidazoline-2 receptors (I2R). However, there is robust pharmacological evidence for the functional modulation of monoamino oxidase (MAO) and other important enzyme activities by I2 site ligands. Some compounds of this class proved to be active experimental tools in preventing both experimental pain and opioid tolerance and dependence. Unfortunately, even though these compounds bind with high potency to central I2 sites, they fail to represent a valid clinical opportunity due to their pharmacokinetic, selectivity or side-effects profile. This paper presents the preclinical profile of a novel I2 ligand (2-phenyl-6-(1H-imidazol-1yl) quinazoline; [CR4056]) that selectively inhibits the activity of human recombinant MAO-A in a concentration-dependent manner. A sub-chronic four day oral treatment of CR4056 increased norepinephrine (NE) tissue levels both in the rat cerebral cortex (63.1% ±4.2%; P < 0.05) and lumbar spinal cord (51.3% ± 6.7%; P < 0.05). In the complete Freund's adjuvant (CFA) rat model of inflammatory pain, CR4056 was found to be orally active (ED50 = 5.8 mg/kg, by mouth [p.o.]). In the acute capsaicin model, CR4056 completely blocked mechanical hyperalgesia in the injured hind paw (ED50 = 4.1 mg/kg, p.o.; ED100 = 17.9 mg/kg, p.o.). This effect was dose-dependently antagonized by the non-selective imidazoline I2/α2 antagonist idazoxan. In rat models of neuropathic pain, oral administration of CR4056 significantly attenuated mechanical hyperalgesia and allodynia. In summary, the present study suggests a novel pharmacological opportunity for inflammatory and/or neuropathic pain treatment based on selective interaction with central imidazoline-2 receptors.

Although bortezomib (BTZ) is the frontline treatment for multiple myeloma, its clinical use is limited by the occurrence of painful peripheral neuropathy, whose treatment is still an unmet clinical need. Previous studies have shown chronic BTZ administration (0.20 mg/kg intravenously three times a week for 8 weeks) to female Wistar rats induced a peripheral neuropathy similar to that observed in humans. In this animal model of BTZ-induced neurotoxicity, the present authors evaluated the efficacy of CR4056, a novel I2 ligand endowed with a remarkable efficacy in several animal pain models. CR4056 was administered in a wide range of doses (0.6-60 mg/kg by gavage every day for 2-3 weeks) in comparison with buprenorphine (Bupre) (28.8 μg/kg subcutaneously every day for 2 weeks) and gabapentin (Gaba) (100 mg/kg by gavage every day for 3 weeks). Chronic administration of BTZ reduced nerve conduction velocity and induced allodynia. CR4056, Bupre, or Gaba did not affect the impaired nerve conduction velocity. Conversely, CR4056 dose-dependently reversed BTZ-induced allodynia (minimum effective dose 0.6 mg/kg). The optimal dose found, 6 mg/kg, provided a constant pain relief throughout the treatment period and without rebound after suspension, being effective when coadministered with BTZ, starting before or after allodynia was established, or when administered alone after BTZ cessation. A certain degree of tolerance was seen after 7 days of administration, but only at the highest doses (20 and 60 mg/kg). Bupre was effective only acutely, since tolerance was evident from the fourth day onwards. Gaba showed a significant activity only at the fourth day of treatment. CR4056, over the range of concentrations of 3-30 μM, was unable to hinder BTZ cytotoxicity on several tumor cell lines, which could indicate that this substance does not directly interfere with BTZ antitumor activity. Therefore, CR4056 could represent a new treatment option for BTZ-induced neuropathic pain.

Prostaglandin E (PGE ) acts via its EP4 receptor as a cytokine amplifier (e.g., interleukin [IL]-6) and induces the differentiation and expansion of inflammatory T-helper (Th) lymphocytes. These mechanisms play a key role in the onset and progression of rheumatoid arthritis (RA). We present the pharmacological characterisation of CR6086, a novel EP4 receptor antagonist, and provide evidence for its potential as a disease-modifying anti-rheumatic drug (DMARD). CR6086 affinity and pharmacodynamics were studied in EP4-expressing HEK293 cells by radioligand binding and cyclic adenosine monophosphate (cAMP) production, respectively. In immune cells, IL-6 and vascular endothelial growth factor (VEGF) expression were analysed by RT-PCR, and IL-23 and IL-17 release were measured by enzyme-linked immunosorbent assay (ELISA). In collagen-induced arthritis (CIA) models, rats or mice were immunised with bovine collagen type II. Drugs were administered orally (etanercept and methotrexate intraperitoneally) starting at disease onset. Arthritis progression was evaluated by oedema, clinical score and histopathology. Anti-collagen II immunoglobulin G antibodies were measured by ELISA. CR6086 showed selectivity and high affinity for the human EP4 receptor (K = 16.6 nM) and functioned as a pure antagonist (half-maximal inhibitory concentration, 22 nM) on PGE -stimulated cAMP production. In models of human immune cells in culture, CR6086 reduced key cytokine players of RA (IL-6 and VEGF expression in macrophages, IL-23 release from dendritic cells, IL-17 release from Th17 cells). In the CIA model of RA in rats and mice, CR6086 significantly improved all features of arthritis: severity, histology, inflammation and pain. In rats, CR6086 was better than the selective cyclooxygenase-2 inhibitor rofecoxib and at least as effective as the Janus kinase inhibitor tofacitinib. In mice, CR6086 and the biologic DMARD etanercept were highly effective, whereas the non-steroidal anti-inflammatory drug naproxen was ineffective. Importantly, in a study of CR6086/methotrexate, combined treatment greatly improved the effect of a fully immunosuppressive dose of methotrexate. CR6086 is a novel, potent EP4 antagonist showing favourable immunomodulatory properties, striking DMARD effects in rodents, and anti-inflammatory activity targeted to immune-mediated inflammatory diseases and distinct from the general effects of cyclooxygenase inhibitors. These results support the clinical development of CR6086, both as a stand-alone DMARD and as a combination therapy with methotrexate. The proof-of-concept trial in patients with RA is ongoing.

Reliably predicting software defects is one of the holy grails of software engineering. Researchers have devised and implemented a plethora of defect/bug prediction approaches varying in terms of accuracy, complexity and the input data they require. However, the absence of an established benchmark makes it hard, if not impossible, to compare approaches. We present a benchmark for defect prediction, in the form of a publicly available dataset consisting of several software systems, and provide an extensive comparison of well-known bug prediction approaches, together with novel approaches we devised. We evaluate the performance of the approaches using different performance indicators: classification of entities as defect-prone or not, ranking of the entities, with and without taking into account the effort to review an entity. We performed three sets of experiments aimed at (1) comparing the approaches across different systems, (2) testing whether the differences in performance are statistically significant, and (3) investigating the stability of approaches across different learners. Our results indicate that, while some approaches perform better than others in a statistically significant manner, external validity in defect prediction is still an open problem, as generalizing results to different contexts/learners proved to be a partially unsuccessful endeavor.

Exploiting the excellent electronic properties of two-dimensional (2D) materials to fabricate advanced electronic circuits is a major goal for the semiconductor industry 1 , 2 . However, most studies in this field have been limited to the fabrication and characterization of isolated large (more than 1 µm 2 ) devices on unfunctional SiO 2 –Si substrates. Some studies have integrated monolayer graphene on silicon microchips as a large-area (more than 500 µm 2 ) interconnection 3 and as a channel of large transistors (roughly 16.5 µm 2 ) (refs.  4 , 5 ), but in all cases the integration density was low, no computation was demonstrated and manipulating monolayer 2D materials was challenging because native pinholes and cracks during transfer increase variability and reduce yield. Here, we present the fabrication of high-integration-density 2D–CMOS hybrid microchips for memristive applications—CMOS stands for complementary metal–oxide–semiconductor. We transfer a sheet of multilayer hexagonal boron nitride onto the back-end-of-line interconnections of silicon microchips containing CMOS transistors of the 180 nm node, and finalize the circuits by patterning the top electrodes and interconnections. The CMOS transistors provide outstanding control over the currents across the hexagonal boron nitride memristors, which allows us to achieve endurances of roughly 5 million cycles in memristors as small as 0.053 µm 2 . We demonstrate in-memory computation by constructing logic gates, and measure spike-timing dependent plasticity signals that are suitable for the implementation of spiking neural networks. The high performance and the relatively-high technology readiness level achieved represent a notable advance towards the integration of 2D materials in microelectronic products and memristive applications. High-integration-density 2D–CMOS hybrid microchips for memristive applications are made demonstrating in-memory computation and electrical response suitable for the implementation of spiking neural networks representing an advance towards integration of 2D materials in microelectronic products and memristive applications.

Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL) techniques, which rely on networks of connected simple computing units operating in parallel. The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigms, such as high parallelization and near-memory computing, help alleviate the data communication bottleneck to some extent, but paradigm- shifting concepts are required. Memristors, a novel beyond-complementary metal-oxide-semiconductor (CMOS) technology, are a promising choice for memory devices due to their unique intrinsic device-level properties, enabling both storing and computing with a small, massively-parallel footprint at low power. Theoretically, this directly translates to a major boost in energy efficiency and computational throughput, but various practical challenges remain. In this work we review the latest efforts for achieving hardware-based memristive artificial neural networks (ANNs), describing with detail the working principia of each block and the different design alternatives with their own advantages and disadvantages, as well as the tools required for accurate estimation of performance metrics. Ultimately, we aim to provide a comprehensive protocol of the materials and methods involved in memristive neural networks to those aiming to start working in this field and the experts looking for a holistic approach. Memristors hold promise for massively-parallel computing at low power. Aguirre et al. provide a comprehensive protocol of the materials and methods for designing memristive artificial neural networks with the detailed working principles of each building block and the tools for performance evaluation.

Metabolomics approaches, such as direct analysis in real time-high resolution mass spectrometry (DART-HRMS), allow characterising many polar and non-polar compounds useful as authentication biomarkers of dairy chains. By using both a partial least squares discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA), this study aimed to assess the capability of DART-HRMS, coupled with a low-level data fusion, discriminate among milk samples from lowland (silages vs. hay) and Alpine (grazing; APS) systems and identify the most informative biomarkers associated with the main dietary forage. As confirmed also by the LDA performed against the test set, DART-HRMS analysis provided an accurate discrimination of Alpine samples; meanwhile, there was a limited capacity to correctly recognise silage- vs. hay-milks. Supervised multivariate statistics followed by metabolomics hierarchical cluster analysis allowed extrapolating the most significant metabolites. Lowland milk was characterised by a pool of energetic compounds, ketoacid derivates, amines and organic acids. Seven informative DART-HRMS molecular features, mainly monoacylglycerols, could strongly explain the metabolomic variation of Alpine grazing milk and contributed to its classification. The misclassification between the two lowland groups confirmed that the intensive dairy systems would be characterised by a small variation in milk composition.

Remanufacturing includes disassembly and reassembly of used products to save natural resources and reduce emissions. While assembly is widely understood in the field of operations management, disassembly is a rather new problem in production planning and control. The latter faces the challenge of high uncertainty of type, quantity and quality conditions of returned products, leading to high volatility in remanufacturing production systems. Traditionally, disassembly is a manual labor-intensive production step that, thanks to advances in robotics and artificial intelligence, starts to be automated with autonomous workstations. Due to the diverging material flow, the application of production systems with loosely linked stations is particularly suitable and, owing to the risk of condition induced operational failures, the rise of hybrid disassembly systems that combine manual and autonomous workstations can be expected. In contrast to traditional workstations, autonomous workstations can expand their capabilities but suffer from unknown failure rates. For such adverse conditions a condition-based control for hybrid disassembly systems, based on reinforcement learning, alongside a comprehensive modeling approach is presented in this work. The method is applied to a real-world production system. By comparison with a heuristic control approach, the potential of the RL approach can be proven simulatively using two different test cases.