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Src-family protein tyrosine kinases: a promising target for treating chronic pain
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Despite growing knowledge of the mechanisms of chronic pain, it remains a major challenge facing clinical practice. Src-family protein tyrosine kinases (SFKs), a group of non-receptor protein tyrosine kinases, have been implicated in neuronal development and synaptic plasticity. SFKs are critically central to various transmembrane receptors e.g. G-protein coupled receptor (GPCR), EphB receptor (EphBR), increased intracellular calcium, epidermal growth factor (EGF) and other growth factors that regulate the phosphorylation of N-methyl-D-aspartic acid receptor (NMDAR) 2B subunit, thus contributing to the development of chronic pain. SFKs have also been regarded as an important point of convergence of intracellular signaling components that regulate microglia functions and the immune response. Additionally, intrathecal administration of SFKs inhibitors significantly alleviates mechanical allodynia in different chronic pain models. Thus, here we reviewed the current evidence of the role of SFKs in the development of chronic pain caused by complete Freund's adjuvant (CFA) injection, peripheral nerve injury (PNI), streptozotocin (STZ) injection and bone metastasis. Moreover, the role of SFKs on the development of morphine tolerance has also been discussed. Management of SFKs therefore emerged as a potential therapeutic target for the treatment of chronic pain in terms of safety and efficacy.
Key words
Chronic pain; Src-family protein tyrosine kinases; N-methyl-D-aspartic acid receptor; Microglia
Network representation learning guided by partial community structure
Get PDFNetwork Representation Learning (NRL) is an effective way to analyse large scale networks (graphs). In general, it maps network nodes, edges, subgraphs, etc. onto independent vectors in a low dimension space, thus facilitating network analysis tasks. As community structure is one of the most prominent mesoscopic structure properties of real networks, it is necessary to preserve community structure of networks during NRL. In this paper, the concept of k-step partial community structure is defined and two Partial Community structure Guided Network Embedding (PCGNE) methods, based on two popular NRL algorithms (DeepWalk and node2vec respectively), for node representation learning are proposed. The idea behind this is that it is easier and more cost-effective to find a higher quality 1-step partial community structure than a higher quality whole community structure for networks; the extracted partial community information is then used to guide random walks in DeepWalk or node2vec. As a result, the learned node representations could preserve community structure property of networks more effectively. The two proposed algorithms and six state-of-the-art NRL algorithms were examined through multi-label classification and (inner community) link prediction on eight synthesized networks: one where community structure property could be controlled, and one real world network. The results suggested that the two PCGNE methods could improve the performance of their own based algorithm significantly and were competitive for node representation learning. Especially, comparing against used baseline algorithms, PCGNE methods could capture overlapping community structure much better, and thus could achieve better performance for multi-label classification on networks that have more overlapping nodes and/or larger overlapping memberships
Using machine learning advances to unravel patterns in subject areas and performances of university students with special educational needs and disabilities (MALSEND): a conceptual approach
Get PDFUniversities and colleges in the UK welcome almost 30,000 disabled students each year. Re-search shows that the dropout from education in the EU for the disabled is at 31.5%, much higher compared to only 12.3% for non-disabled students. Supporting young students who require special educational needs in pursuing higher education is an ambitious and necessary step that needs to be adopted by tertiary education providers worldwide. We propose, MALSEND, a project aiming to develop a platform based on machine and human intelligence to understand learning disability patterns in Higher Education. The platform will analyse da-tasets from universities in the previous years and will help to discover any trends in subject areas and performance among autistic students, dyslexic students or students having attention deficit hyperactive disorder (ADHD), among others. Analysing variables such as students’ courses, modules, performances and other engagement-indices will give new insights on re-search questions, career advice and institutional policy making. This paper describes the activ-ities of the development phases of this concept
Using a multi-modal strategy to improve patient hand hygiene
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The role of healthcare worker hand hygiene in preventing healthcare associated infections (HCAI) is well established. There is less emphasis on the hand hygiene of hospitalised patients; in the context of COVID-19 mechanisms to support it are particularly important. The purpose of this study was to establish if providing patient hand wipes, and a defined protocol for encouraging their use, was effective in improving the frequency of patient hand hygiene (PHH).
Design
Before and after study
Setting
General Hospital, United Kingdom.
Participants
All adult patients admitted to six acute elderly care/rehabilitation hospital wards between July and October 2018.
Methods
Baseline audit of PHH opportunities conducted over 6 weeks. Focus group with staff and survey of the public informed the development of a PHH bundle. Effect of bundle on PHH monitored by structured observation of HH opportunities over 12 weeks.
Results
During baseline 303 opportunities for PHH were observed; compliance with PHH was 13.2% (40/303; 95%CI 9.9-7.5). In the evaluation of PHH bundle 526 PHH opportunities were observed with HH occurring in 58.9% (310/526); an increase of 45.7% vs. baseline (95%CI 39.7–51.0%; p<0.001).
Conclusion
Providing patients with multi-wipe packs of handwipes is a simple, cost-effective approach to increasing patient hand hygiene and reducing the risk of HCAI in hospital. Healthcare workers play an essential role in encouraging PHH
Antonioni and the aesthetics of impurity: remaking the image in the 1960s
No full textMichelangelo Antonioni’s 1960s films are widely recognized as both exemplars of cinema and key texts in ushering in cinema’s ‘modern’ incarnation. Reconnecting Antonioni’s aesthetically audacious films of the 1960s to the ferment of their historical time, Antonioni and the Aesthetics of Impurity addresses these works’ crucial, yet overlooked, affinity with the new ‘impure’ art practices that emerged in the period. At the same time, the book also offers a novel reading of the films’ dialogue with postwar pictorial abstraction. Revealing an Antonioni who embraced both mixed and mass media and reflected on them via his cinema, the book replaces auteuristic accounts of the director’s work with a new understanding of its critical significance in late-twentieth century cinema and visual culture
New music inspired by old Hispanic chant
Get PDFCountless composers have turned to the Middle Ages for inspiration, a practice described by Pugh and Weisl as ‘medievalism.’ Multiple strategies have been developed, ranging from use of existing medieval melodies to being inspired by medieval art or architecture. In what is perhaps the most familiar approach, composers use identifiable musical signposts that signify –to modern audiences –an imagined medieval sound. For example, in much music by Tavener, the medieval past is arguably evoked through recognisable modern signifiers rather than building on concrete aspects of medieval Greek chant. Use of such signifiers relies on listeners recognising stereotyped ideas of medieval music. Many of the prevailing strategies adopted by composers since the 19th century are explored in detail in the forthcoming Oxford Handbook of Music and Medievalism. In the present article, we discuss an example of recent practice-based research falling under the umbrella of ‘creative medievalism.’ The works discussed here were generated through collaborative research undertaken by a composer and a group of medievalists
Who feels it knows it! Alterity, identity and ‘epistemological privilege’: challenging white privilege from a black perspective within the academy
Get PDFThe paper considers some of the contemporary issues faced by black academics, denoting our constant struggles for equal and fair treatment, which are not based on what we bring to the table but the skin we are in. To do so I will utilise ‘Interpretative Phenomenological Analysis’ (IPA) because ‘[W]hen people are engaged with an ‘experience’ of something major in their lives, they begin to reflect on the significance of what is happening’ (Smith et al 2009: 3). IPA therefore enables us to consider a form of reasoning as that which firmly locates the ‘nebulous’ concepts of whiteness and white privilege in a socio-cultural and historical context. Moreover, by interrogating and exposing the centrality of whiteness, we make known that any discussion of the value of human life, in white racist societies, requires black voices to be at the forefront of discussions regarding, race, representation and belonging; especially within the hallowed halls of academia
Classification of malware attacks using machine learning in decision tree
Get PDFPredicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends
Waterborne protozoan pathogens in environmental aquatic biofilms: implications for water quality assessment strategies
Get PDFBiofilms containing pathogenic organisms from the water supply are a potential source of protozoan parasite outbreaks and a general public health concern. The aim of the present study was to demonstrate the simultaneous and multispatial occurrence of waterborne protozoan pathogens (WBPP) in substrate-associated biofilms (SAB) and compare it to surface water (SW) and sediments with bottom water (BW) counterparts using manual filtration and elution from low-volume samples. For scenario purposes, simulated environmental biofilm contamination was created from in-situ grown one-month-old SAB (OM-SAB) that were spiked with Cryptosporidium parvum oocysts. Samples were collected from the largest freshwater reservoirs in Luzon, Philippines and a University Lake in Thailand. A total of 69 samples (23 SAB, 23 SW, and 23 BW) were evaluated using traditional staining techniques for Cryptosporidium, and immunofluorescence staining for the simultaneous detection of Cryptosporidium and Giardia. In the present study, WBPP was found in 43% SAB, 39% SW, and 39% BW samples tested with SAB results reflecting SW and BW results. Further, the potential and advantages of using low-volume sampling for the detection of parasite (oo)cysts in aquatic matrices were also demonstrated. Scanning electron microscopy of OM-SAB revealed a naturally-associated testate amoeba shell, while Cryptosporidium oocysts spiked samples provided a visual profile of what can be expected from naturally contaminated biofilms. This study provides the first evidence for the simultaneous and multi-spatial occurrence of waterborne protozoan pathogens in low-volume environmental aquatic matrices and warrants SAB testing along with SW and BW matrices for improved water quality assessment strategies (iWQAS)