University of Greenwich

Nanomedicine and nano delivery systems are a relatively new but rapidly developing science where materials in the nanoscale range are employed to serve as means of diagnostic tools or to deliver therapeutic agents to specific targeted sites in a controlled manner. Nanotechnology offers multiple benefits in treating chronic human diseases by site-specific, and target-oriented delivery of precise medicines. Recently, there are a number of outstanding applications of the nanomedicine (chemotherapeutic agents, biological agents, immunotherapeutic agents etc.) in the treatment of various diseases. The current review, presents an updated summary of recent advances in the field of nanomedicines and nano based drug delivery systems through comprehensive scrutiny of the discovery and application of nanomaterials in improving both the efficacy of novel and old drugs (e.g., natural products) and selective diagnosis through disease marker molecules. The opportunities and challenges of nanomedicines in drug delivery from synthetic/natural sources to their clinical applications are also discussed. In addition, we have included information regarding the trends and perspectives in nanomedicine area.

As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.

An annotated reference sequence representing the hexaploid bread wheat genome in 21 pseudomolecules has been analyzed to identify the distribution and genomic context of coding and noncoding elements across the A, B, and D subgenomes. With an estimated coverage of 94% of the genome and containing 107,891 high-confidence gene models, this assembly enabled the discovery of tissue- and developmental stage-related coexpression networks by providing a transcriptome atlas representing major stages of wheat development. Dynamics of complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. This community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding.

Sampling is central to the practice of qualitative methods, but compared with data collection and analysis its processes have been discussed relatively little. A four-point approach to sampling in qualitative interview-based research is presented and critically discussed in this article, which integrates theory and process for the following: (1) defining a sample universe, by way of specifying inclusion and exclusion criteria for potential participation; (2) deciding upon a sample size, through the conjoint consideration of epistemological and practical concerns; (3) selecting a sampling strategy, such as random sampling, convenience sampling, stratified sampling, cell sampling, quota sampling or a single-case selection strategy; and (4) sample sourcing, which includes matters of advertising, incentivising, avoidance of bias, and ethical concerns pertaining to informed consent. The extent to which these four concerns are met and made explicit in a qualitative study has implications for its coherence, transparency, impact and trustworthiness.

This wide-ranging collection of essays by Michael Oliver discusses recent and perennial issues - such as the fundamental principles of disability, citizenship and community care, social policy and welfare, education, rehabilitation, and the politics of new social movements and the international context.

The Fifth Edition of the 'Guide to Receptors and Channels' is a compilation of the major pharmacological targets divided into seven sections: G protein-coupled receptors, ligand-gated ion channels, ion channels, catalytic receptors, nuclear receptors, transporters and enzymes. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside suggestions for further reading. Available alongside this publication is a portal at http://www.GuideToPharmacology.org which is produced in close association with NC-IUPHAR and allows free online access to the information presented in the Fifth Edition.

by Guy Standing, London, Bloomsbury, 2011, 198 pp., £19.99 (paperback), ISBN 978-1849663519, £57 (hardback), ISBN 9781849663519 Guy Standing’s challenging new book The precariat seeks to explain th...

Abstract The article outlines some of the main dimensions in which gender relations are crucial in understanding and analysing the phenomena of nations and nationalism, and the specific boundaries of inclusions and exclusions that they construct. Three major dimensions of nationalist projects that relate to citizenship, culture and origin are differentiated. In each of them gender relations play specific roles and have mobilized specific struggles. The article looks at the dualistic nature of women's citizenship, as both included and excluded from the general body of citizens. Even when there is a formal equality of women in their political rights as citizens, other modes of exclusion in the political, social and civil spheres continue to operate. The particular ways in which the entry of women into the military has been linked to struggles for women's equality as citizens are examined in this context. In relation to national cultures, both secular and religious, the article examines the ways in which wom...

Understanding Disability develops some of the main themes and issues surrounding disability that have arisen in the last twenty years, offering both a personal journey of exploration and understanding

Some of the most important impacts of global climate change will be felt among the populations, predominantly in developing countries, referred to as "subsistence" or "smallholder" farmers. Their vulnerability to climate change comes both from being predominantly located in the tropics, and from various socioeconomic, demographic, and policy trends limiting their capacity to adapt to change. However, these impacts will be difficult to model or predict because of (i) the lack of standardised definitions of these sorts of farming system, and therefore of standard data above the national level, (ii) intrinsic characteristics of these systems, particularly their complexity, their location-specificity, and their integration of agricultural and nonagricultural livelihood strategies, and (iii) their vulnerability to a range of climate-related and other stressors. Some recent work relevant to these farming systems is reviewed, a conceptual framework for understanding the diverse forms of impacts in an integrated manner is proposed, and future research needs are identified.

This year marks exactly 30 years since I published a book introducing the social model of disability onto an unsuspecting world and yet, despite the impact this model has had, all we now seem to do is talk about it. While all this chatter did not matter too much when the economy was booming, now it no longer booms it is proving disastrous for many disabled people whose benefits and services are being severely cut back or removed altogether. In the article I restate my view of what the social model was and what I see as its potential for improving the lives of disabled people. Finally I focus on the unfortunate criticisms of it and the disastrous implications these have had for disabled people.

Abstract Root-associated microbes play a key role in plant performance and productivity, making them important players in agroecosystems. So far, very few studies have assessed the impact of different farming systems on the root microbiota and it is still unclear whether agricultural intensification influences the structure and complexity of microbial communities. We investigated the impact of conventional, no-till, and organic farming on wheat root fungal communities using PacBio SMRT sequencing on samples collected from 60 farmlands in Switzerland. Organic farming harbored a much more complex fungal network with significantly higher connectivity than conventional and no-till farming systems. The abundance of keystone taxa was the highest under organic farming where agricultural intensification was the lowest. We also found a strong negative association (R2 = 0.366; P < 0.0001) between agricultural intensification and root fungal network connectivity. The occurrence of keystone taxa was best explained by soil phosphorus levels, bulk density, pH, and mycorrhizal colonization. The majority of keystone taxa are known to form arbuscular mycorrhizal associations with plants and belong to the orders Glomerales, Paraglomerales, and Diversisporales. Supporting this, the abundance of mycorrhizal fungi in roots and soils was also significantly higher under organic farming. To our knowledge, this is the first study to report mycorrhizal keystone taxa for agroecosystems, and we demonstrate that agricultural intensification reduces network complexity and the abundance of keystone taxa in the root microbiome.

Metabolic syndrome (MetS) and its components are highly predictive of cardiovascular diseases. The primary aim of this systematic review and meta-analysis was to assess the prevalence of MetS and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder, comparing subjects with different disorders and taking into account demographic variables and psychotropic medication use. The secondary aim was to compare the MetS prevalence in persons with any of the selected disorders versus matched general population controls. The pooled MetS prevalence in people with severe mental illness was 32.6% (95% CI: 30.8%-34.4%; N = 198; n = 52,678). Relative risk meta-analyses established that there was no significant difference in MetS prevalence in studies directly comparing schizophrenia versus bipolar disorder, and in those directly comparing bipolar disorder versus major depressive disorder. Only two studies directly compared people with schizophrenia and major depressive disorder, precluding meta-analytic calculations. Older age and a higher body mass index were significant moderators in the final demographic regression model (z = -3.6, p = 0.0003, r(2) = 0.19). People treated with all individual antipsychotic medications had a significantly (p<0.001) higher MetS risk compared to antipsychotic-naïve participants. MetS risk was significantly higher with clozapine and olanzapine (except vs. clozapine) than other antipsychotics, and significantly lower with aripiprazole than other antipsychotics (except vs. amisulpride). Compared with matched general population controls, people with severe mental illness had a significantly increased risk for MetS (RR = 1.58; 95% CI: 1.35-1.86; p<0.001) and all its components, except for hypertension (p = 0.07). These data suggest that the risk for MetS is similarly elevated in the diagnostic subgroups of severe mental illness. Routine screening and multidisciplinary management of medical and behavioral conditions is needed in these patients. Risks of individual antipsychotics should be considered when making treatment choices.

BACKGROUND AND REVIEW CONTEXT: Evidence to support the proposition that learning together will help practitioners and agencies work better together remains limited and thinly spread. This review identified, collated, analysed and synthesised the best available contemporary evidence from 21 of the strongest evaluations of IPE to inform the above proposition. In this way we sought to help shape future interprofessional education and maximize the potential for interprofessional learning to contribute to collaborative practice and better care. OBJECTIVES OF THE REVIEW: To identify and review the strongest evaluations of IPE. To classify the outcomes of IPE and note the influence of context on particular outcomes. To develop a narrative about the mechanisms that underpin and inform positive and negative outcomes of IPE. SEARCH STRATEGY: Bibliographic database searches as follows: Medline 1966-2003, CINAHL 1982-2001, BEI 1964-2001, ASSIA 1990-2003 which produced 10,495 abstracts. Subsequently, 884 full papers were obtained and scrutinized. In addition, hand searching (2003-5 issues) of 21 journals known to have published two or more higher quality studies from a previous review. TOPIC DEFINITION AND INCLUSION CRITERIA: Peer-reviewed papers and reports included in the review had to be formal educational initiatives attended by at least two of the many professional groups from health and social care, with the objective of improving care; and learning with, from and about each other. DATA COLLECTION, ANALYSIS AND SYNTHESIS: Standard systematic review procedures were applied for sifting abstracts, scrutinizing full papers and abstracting data. Two members of the team checked each abstract to decide whether the full paper should be read. A third member was consulted over any discrepancies. Similarly, each full paper was read by at least two members of the team and agreement sought before passing it to one member of the team (SR) for data abstraction. Other members of the team checked 10% of the abstraction records. Coding into a Statistical Package for Social Scientists (SPSS) data base led to collection of different outcome measures used in the primary studies via the common metric of an adapted Kirkpatrick's four-level model of educational outcomes. Additionally, a narrative synthesis was built after analysis of primary data with the 3-P model (presage-process-product) of education development and delivery. HEADLINE RESULTS: Government calls for enhanced collaboration amongst practitioners frequently leads to IPE that is then developed and delivered by educators, practitioners or service managers. Staff development is a key influence on the effectiveness of IPE for learners who all have unique values about themselves and others. Authenticity and customization of IPE are important mechanisms for positive outcomes of IPE. Interprofessional education is generally well received, enabling knowledge and skills necessary for collaborative working to be learnt; it is less able to positively influence attitudes and perceptions towards others in the service delivery team. In the context of quality improvement initiatives interprofessional education is frequently used as a mechanism to enhance the development of practice and improvement of services.

OBJECTIVE: To investigate whether people with subjective memory complaints (SMC) but no objective deficits are at increased risk of developing mild cognitive impairment (MCI) and dementia. METHOD: Major electronic databases were searched till 03/2014, and a meta-analysis was conducted using inception cohort studies. RESULTS: Across 28 studies, there were 29,723 unique individuals (14,714 with SMC and 15,009 without SMC) (mean 71.6 years) followed on average for 4.8 years through to dementia. The annual conversion rate (ACR) of SMC to dementia was 2.33% (95% CI = 1.93%-2.78%) a relative risk (RR) of 2.07 (95% CI = 1.76-2.44) compared with those without SMC (n = 15,009). From 11 studies the ACR of developing MCI was 6.67% (95% CI = 4.70-8.95%). In long-term studies over 4 years, 14.1% (9.67-19.1%) of people with SMC developed dementia and 26.6% (95% CI = 5.3-39.7) went on to develop MCI. The ACR from SMC to dementia and MCI were comparable in community and non-community settings. CONCLUSION: Older people with SMC but no objective complaints are twice as likely to develop dementia as individuals without SMC. Approximately 2.3% and 6.6% of older people with SMC will progress to dementia and MCI per year.

Human land use threatens global biodiversity and compromises multiple ecosystem functions critical to food production. Whether crop yield-related ecosystem services can be maintained by a few dominant species or rely on high richness remains unclear. Using a global database from 89 studies (with 1475 locations), we partition the relative importance of species richness, abundance, and dominance for pollination; biological pest control; and final yields in the context of ongoing land-use change. Pollinator and enemy richness directly supported ecosystem services in addition to and independent of abundance and dominance. Up to 50% of the negative effects of landscape simplification on ecosystem services was due to richness losses of service-providing organisms, with negative consequences for crop yields. Maintaining the biodiversity of ecosystem service providers is therefore vital to sustain the flow of key agroecosystem benefits to society.

In electronics manufacturing, the required quality of electronic modules (e.g.packaged electronic devices) are evaluated through qualification testing using standards and user-defined requirements.The challenge for the electronics industry is that product qualification testing is time-consuming and costly.This paper focuses on the development and demonstration of a novel approach for smarter qualification using test data from the production line along with integrated computational techniques for data mining/analytics and data-driven forecasting (i.e.prognostics) modelling.The most common type of testing in the electronics industry-sequentially run electrical multi-parameter tests on the Device-under-Test (DUT), is considered.The proposed data mining (DM) framework can identify the tests that have strong correlation to pending failure of the device in the qualification (tests sensitive to pending failure) as well as to evaluate the similarity in test measurements, thus generating knowledge on potentially redundant tests.Mining the data in this context and with the proposed approach represents a major new contribution because it uncovers embedded knowledge and information in the production test data that can enable intelligent optimisation of the tests' sequence and reduce the number of tests.The intelligent manufacturing concept behind the development of data-driven prognostics models using machine learning techniques is to use data only from a small number of tests from the full qualification specification as training data in the process of model construction.This model can then forecast the overall qualification outcome for a DUT-Pass or Fail-without performing all other remaining tests.The novelty in the context of machine learning is in the selection of the data features for the training dataset using results from tests sensitive to pending failure.Support Vector Machine (SVM) binary classifiers SVM models built with data from tests sensitive to the outcome that the module will fail are shown to have superior performance compared with models trained with other datasets of tests.Case studies based on the use of real industrial production test data for an electronic module are included in the paper to demonstrate and validate the computational approach.This work is both novel and original because at present, to the best knowledge of the authors, such predictive analytics methodology applied to qualification testing and providing benefits of test time and hence cost reduction are non-existent in the electronics industry.The integrated data analytics-prognostics approach, deployable for both off-line and in-line optimisation of production test procedures, has the potential to transform current practices by exploiting in a smarter way information and knowledge available with large datasets of qualification test data.

Grain yields of eight representative semidwarf spring wheat ( Triticum aestivum L.) cultivars released in northwest Mexico between 1962 and 1988 have increased linearly across years as measured in this region during 6 yr under favorable management and irrigation. To understand the physiological basis of this progress and possibly assist future selection for grain yield, leaf traits were determined during 3 yr in the same study. Stomatal conductance ( g s ), maximum photosynthetic rate (A max , and canopy temperature depression (CTD), averaged over the 3 yr, were closely and positively correlated with progress in the 6‐yr mean yield. The correlation was greatest with g s ( r = 0.94, P &lt; 0.01). Compared with the overall yield increase of 27%, g s increased 63%, A max increased 23%, and canopies were 0.6°C cooler. Carbon‐13 isotope discrimination was also positively associated with yield progress ( r = 0.71, P &lt; 0.05), but other leaf traits such as flag leaf area, specific leaf weight, percentage N and greeness were not, nor was crop growth rate around anthesis. The causal basis of the leaf activity interrelationships is reasonably clear, with both increased intercellular CO 2 concentration and increased mesophyll activity contributing to the increase in A max . However, causal links to the yield progress, and the accompanying increase in kernels per square meter, are not clear. It is concluded that g s and CTD should be further investigated as potential indirect selection criteria for yield.

Abstract The serotonin hypothesis of depression is still influential. We aimed to synthesise and evaluate evidence on whether depression is associated with lowered serotonin concentration or activity in a systematic umbrella review of the principal relevant areas of research. PubMed, EMBASE and PsycINFO were searched using terms appropriate to each area of research, from their inception until December 2020. Systematic reviews, meta-analyses and large data-set analyses in the following areas were identified: serotonin and serotonin metabolite, 5-HIAA, concentrations in body fluids; serotonin 5-HT 1A receptor binding; serotonin transporter (SERT) levels measured by imaging or at post-mortem; tryptophan depletion studies; SERT gene associations and SERT gene-environment interactions. Studies of depression associated with physical conditions and specific subtypes of depression (e.g. bipolar depression) were excluded. Two independent reviewers extracted the data and assessed the quality of included studies using the AMSTAR-2, an adapted AMSTAR-2, or the STREGA for a large genetic study. The certainty of study results was assessed using a modified version of the GRADE. We did not synthesise results of individual meta-analyses because they included overlapping studies. The review was registered with PROSPERO (CRD42020207203). 17 studies were included: 12 systematic reviews and meta-analyses, 1 collaborative meta-analysis, 1 meta-analysis of large cohort studies, 1 systematic review and narrative synthesis, 1 genetic association study and 1 umbrella review. Quality of reviews was variable with some genetic studies of high quality. Two meta-analyses of overlapping studies examining the serotonin metabolite, 5-HIAA, showed no association with depression (largest n = 1002). One meta-analysis of cohort studies of plasma serotonin showed no relationship with depression, and evidence that lowered serotonin concentration was associated with antidepressant use ( n = 1869). Two meta-analyses of overlapping studies examining the 5-HT 1A receptor (largest n = 561), and three meta-analyses of overlapping studies examining SERT binding (largest n = 1845) showed weak and inconsistent evidence of reduced binding in some areas, which would be consistent with increased synaptic availability of serotonin in people with depression, if this was the original, causal abnormaly. However, effects of prior antidepressant use were not reliably excluded. One meta-analysis of tryptophan depletion studies found no effect in most healthy volunteers ( n = 566), but weak evidence of an effect in those with a family history of depression ( n = 75). Another systematic review ( n = 342) and a sample of ten subsequent studies ( n = 407) found no effect in volunteers. No systematic review of tryptophan depletion studies has been performed since 2007. The two largest and highest quality studies of the SERT gene, one genetic association study ( n = 115,257) and one collaborative meta-analysis ( n = 43,165), revealed no evidence of an association with depression, or of an interaction between genotype, stress and depression. The main areas of serotonin research provide no consistent evidence of there being an association between serotonin and depression, and no support for the hypothesis that depression is caused by lowered serotonin activity or concentrations. Some evidence was consistent with the possibility that long-term antidepressant use reduces serotonin concentration.