Engineering Sciences Research Research Articles

Mechanistic modelling of Virchows triad to assess thrombogenicity and stroke risk in atrial fibrillation patients

Abstract Background Atrial fibrillation (AF) is responsible for almost one third of all strokes, with the left atrial appendage (LAA) being the primary thromboembolic source due to localised stimulation of prothrombotic mechanisms; blood stasis, hypercoagulability and endothelial damage, known as Virchow's triad. Aim We propose an in-silico modelling pipeline that leverages clinical imaging data to mechanistically assess patient thrombogenicity for all aspects of Virchow's triad to improve the prediction and prevention of AF-related stroke. Methods Two AF patients undergoing Cine magnetic resonance imaging (sinus rhythm (SR) N=1 or AF N=1 during imaging) were selected for 3D left atrial (LA) modelling with patient-specific myocardial deformation prescribed from image-derived wall motion. Blood stasis was quantified by computational fluid dynamics (CFD) simulations of 5 cardiac cycles [1]. Generation of three key coagulation proteins; thrombin, fibrinogen and fibrin, were modelled to represent thrombus growth and hypercoagulability [2]. Regions prone to thrombogenesis by endothelial damage were identified by the oscillatory shear index (OSI), time averaged wall shear stress (TAWSS) and endothelial cell activation potential (ECAP) metrics in the LAA [3]. Results Patient-specific LA simulations enabled the assessment of differences between SR and AF conditions, quantified as numerical characteristics of each aspect of Virchow's triad. In SR, blood flow velocities were in the range 0–2.6 m/s with mean of 0.85 m/s in the LA cavity, while AF had a range between 0–1.6 m/s with mean of 0.55 m/s. The peak and mean LAA velocities in SR were 0.85 m/s and 0.14 m/s, while AF had a peak LAA velocity of 0.32 m/s and mean of 0.09 m/s, showing a 38% decrease during AF. The thrombin concentration reached its steady state at 1.26 mmol/m3 in the AF case after 4.7 seconds, while thrombin was washed away from the initial injury site in SR. After 5 cardiac cycles of thrombus growth dynamics, the peak fibrin concentration in the LAA was 1.3 mmol/m3 in SR and 3.8 mmol/m3 in AF, with the thrombus area in AF being 40% larger. Fibrinogen concentration decreased at a rate equal to fibrin generation in both SR and AF solely in the area of thrombus formation. ECAP in the LAA had peak values of 2.9 in SR and 3.7 in AF, with the location at highest risk of thrombogenesis above the LAA entrance. LAA OSI had an average value of 0.45 in AF versus 0.36 in SR, showing a 26% increase. Similarly, the TAWSS was 3.5x10–3 Pa on average over the LAA in AF compared to 1.4x10–3 Pa in SR. Conclusions Patient-specific LA models combining these three quantitative characteristics can be used to predict the higher thrombogenic risk in AF. After further validation, this novel approach for quantitative assessment of AF patient thrombogenicity based on modelling all factors in Virchow's triad can personalise and improve management of AF patients with a risk of stroke. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UK Engineering and Physical Sciences Research Council

Socioeconomic deprivation: an important largely unrecognized risk factor in primary prevention of cardiovascular disease

Abstract Background Socioeconomic deprivation is associated with higher cardiovascular morbidity and mortality. Whether deprivation status should be incorporated in more cardiovascular risk estimation scores remains unclear. Purpose This study evaluates how socioeconomic deprivation status affects the performance of three primary prevention cardiovascular risk scores. Methods The Generation Scotland Scottish Family Health Study was used to evaluate the performance of three cardiovascular risk scores with (ASSIGN) and without (SCORE2, PCE) socioeconomic deprivation as a covariate in the risk prediction model. Deprivation was defined by Scottish Index of Multiple Deprivation score. The predicted 10-year risk was evaluated against the observed event rate for the cardiovascular outcome of each risk score. The comparison was made across three groups defined by the deprivation index score consisting of group 1 defined as most deprived, group 3 defined as least deprived and group 2 which consisted of individuals in the middle deprivation categories. Results The study population consisted of 15,506 individuals (60.0% female, median age of 51). Across the population 1,808 (12%) individuals were assigned to group 1 (most deprived), 8,119 (55%) to group 2, and 4,708 (32%) to group 3 (least deprived). Risk scores based on models that did not include deprivation status significantly under predicted risk in the most deprived (6.4% observed versus 4.6% predicted for SCORE2 and 6.7% observed versus 4.7% predicted for PCE, p<0.001 for both). Both risk scores also significantly overpredicted the risk in the least deprived group (4.0% observed versus 4.7% predicted for SCORE2, p=0.007 and 4.2% observed versus 4.9% predicted for PCE, p=0.028). In contrast, no significant difference was demonstrated in the observed versus predicted risk when using the ASSIGN risk score, which included socioeconomic deprivation status in the risk model. Conclusions Socioeconomic status is a largely unrecognized risk factor in primary prevention of cardiovascular disease. Risk scores that exclude socioeconomic deprivation as a covariate under- and overestimate the risk in the most and least deprived individuals, respectively. This study highlights the importance of incorporating socioeconomic deprivation status in risk estimation systems to ultimately reduce inequalities in health care provision for cardiovascular disease. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): The British Heart Foundation.Health Data Research UK which receives its funding from HDR UK Ltd (HDR-5012) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and the Wellcome Trust.

Analisis dan Pemetaan Mata Kuliah Bersyarat Program Studi Fisika Menggunakan BackPropagation Neural Network

This study aims to analyze and map the conditional courses at the Tadris Physics Study Program, Faculty of Tarbiyah and Teacher Training, Sulthan Thaha Saifuddin State Islamic University Jambi. This research is an applied science research, data analysis using quantitative descriptive technique. The data is in the form of documenting the value of the 2019/2020 Tadris Physics Study Program students. The research sample consisted of 11 sample subjects from 19 population subjects. The data is processed using Backpropagation Neural Network with Python programming language. Validation and accuracy of prediction results using Mean Absolute Percentage Error and determinant coefficient R Square. The prediction results of conditional courses obtained are accurate and valid with MAPE values <10% (very good) and R Square values close to 1. This study shows that the mapping of prerequisite courses set by the study program is appropriate, except for Basic Physics Courses. 2 (R 0.216) and Mathematics Physics Course I (R 0.50) require additional other prerequisite courses.Keywords: mapping; conditional courses, backpropagation neural network

Built in, not bolted on: responsible innovation in UK Centres for Doctoral Training – the new normal?

PurposeThe purpose of the study detailed here was to engage with Directors of Centres for Doctoral Training (CDTs) during the first year of their new Centres to form a snapshot view of the nature and type of training that was being incorporated and how this might affect the wider institution – in this case the university. Using an organisational learning lens, this paper empirically examines the work-in-progress of the responsible innovation (RI) training in CDTs to assess how new RI understandings are being created, retained and transferred within the CDTs, questioning whether this process represents a programme of “institutionalisation”.Design/methodology/approachDuring the past decade, RI has become increasingly embedded within the EU and UK research context, appearing with greater frequency in funding calls and policy spaces. As part of this embedding, in its 2018 funding call for CDTs, the Engineering and Physical Sciences Research Council (EPSRC) required RI training to be included in the programme for all doctoral students.FindingsThe paper concludes that, at present, institutionalisation is highly variegated, with the greater organisational change required to truly embed RI mindsets.Originality/valueThe paper provides original, empirical research evidence of RI institutionalisation in UK CDTs, and, using a “learning organisation” lens, examines areas of value to both RI and learning organisation theory.

A Triple Helix Model of Doctoral Education: A Case Study of an Industrial Doctorate

The knowledge economy requires a revolution in doctoral education. This article generalizes a triple helix model of doctoral education based on the existing literature. Further, it conducts a case study of a Center for Doctoral Training sponsored by the Engineering and Physical Sciences Research Council in the United Kingdom. This research adopts document and observation studies as instruments to examine the roles of university, industry, and government, and their interactions, in doctoral education. Through document and observation analyses, it finds that universities provide disciplinary, interdisciplinary, and professional training to doctoral students; industry offers research and training opportunities, research grants, and placements; and governments stimulate the cooperation between universities and industry through support policies and grants. Universities, industry, and governments benefit from these interactions, and these benefits reinforce their interactions. In the meantime, university autonomy is compromised by the involvement of industry and governments in doctoral education, although the rigor of the doctorates is not compromised because universities dominate the assessment. The curriculum, supervision, matching of research projects from industry with the research interests of doctoral students, and research outcomes, which are in the boundary spaces of the triple helix model of doctoral education, should be further developed for the development of industrial doctorates.

Lessons to learn from roadmapping in cleaning and decontamination

The UK’s Engineering and Physical Sciences Research Council supported a series of meetings in 2021 to develop a roadmap for future research in Quantitative Modelling in Cleaning and Decontamination. Quantitative modelling in this context is the development of numerical and predictive tools, based on scientific principles, which can support design, operation and decision making associated with cleaning and decontamination. The activity involved identifying past and current activities on this topic across a range of different fields, including food and drink, consumer goods, healthcare, pharmaceuticals, nuclear, civil defence and biofilms. Input was received from operators, manufacturing companies, government agencies and researchers. The exercise identified a series of common needs which span sectors, and the challenges which need to be addressed to facilitate transfer of knowledge between sectors and develop the expertise required to tackle new challenges including those posed by sustainability.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

BackgroundReal-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.MethodsWe report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.FindingsThe framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88–0·90]) and similarly predictive using only contact-network variables (0·88 [0·86–0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80–0·84]) or patient clinical (0·64 [0·62–0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82–0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82–0·86] to 0·88 [0·86–0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46–0·52] to 0·68 [0·64–0·70]).InterpretationDynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections.FundingMedical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation.

Satellite pose estimation using Earth radiation modeled by artificial neural networks

The thermal energy received by each surface of an Earth-orbiting satellite strongly depends on its position and orientation. In this sense, simultaneous orbit and attitude estimation (SOAE) using the surface temperature data has been focused in the present study. The Earth infrared (IR) radiation and the Earth’s top-of-atmosphere (TOA) albedo are two key sources of radiation affecting the satellite surface temperature rate. The Earth’s radiation information has been monitored for the past two decades by the Clouds and the Earth’s Radiant Energy System (CERES) project, producing a comprehensive set of Earth radiation budget (ERB) data for climate, weather and applied science research. The current study utilizes the ERB data to forecast the IR radiation flux (IRF) and the TOA albedo factor (AF) via an artificial neural network (ANN). In this respect, the satellite longitude, latitude and time constitute the input vector of the learning set, while the AF and IRF are considered as the output. The ANN is then employed to model the satellite surface temperature rate in the radiation-based SOAE process. The feasibility of the proposed pose estimation approach has been addressed and verified via Monte Carlo simulation. Obtained results demonstrate suitability of the algorithm in sunlight intervals with good accuracy.

Modelling Multiperiod Carbon Markets Using Singular Forward-Backward SDEs

We introduce a model for the evolution of emissions and the price of emissions allowances in a carbon market, such as the European Union Emissions Trading System (EU ETS). The model accounts for multiple trading periods, or phases, with multiple times at which compliance can occur. At the end of each trading period, the participating firms must surrender allowances for their emissions made during that period, and additional allowances can be used for compliance in the following periods. We show that the multiperiod allowance pricing problem is well-posed for various mechanisms (such as banking, borrowing, and withdrawal of allowances) linking the trading periods. The results are based on the analysis of a forward-backward stochastic differential equation with coupled forward and backward components, a discontinuous terminal condition, and a forward component that is degenerate. We also introduce an infinite-period model for a carbon market with a sequence of compliance times and with no end date. We show that, under appropriate conditions, the value function for the multiperiod pricing problem converges, as the number of periods increases, to a value function for this infinite-period model and that such functions are unique. Finally, we present a numerical example that demonstrates empirically the convergence of the multiperiod pricing problem. Funding: The Engineering and Physical Sciences Research Council and Climate-KIC funded all parts of H. Chotai’s PhD programme in the centre for doctoral training and provided many opportunities for travel to conferences and events during the programme. D. Crisan received financial support from École Polytechnique and Université Paris Diderot through visitor grants.

(Invited, Digital Presentation) Tuning Gas-Diffusion-Layer Surface Wettability for Polymer Electrolyte Fuel Cells

In the present scenario of a global initiative toward securing global net-zero by mid-century and keeping 1.5 degrees within reach, polymer-electrolyte fuel cells (PEFCs) are considered to play an important role in the energy transition, particularly for the decarbonization of transit buses, trucks, rail transport, ships and ferries, and the residential heating sector. However, PEFCs are not economically competitive with the internal combustion engine powertrains [1]. Moreover, their durability standards in widely varying conditions have yet to be established and water management remains a critical issue for performance degradation and durability [1-3]. Thus, the mission of my research team is to conduct original research to make PEFCs economically viable and optimize their performance and durability [4,5].In this talk, I will highlight our research on PEFC’s gas diffusion layer (GDL), as its interfaces with the flow channel and microporous layer play a significant role in water management. This research was aimed at selectively modifying GDL surfaces with a hydrophobic pattern to improve water transport and water removal from flow channels; thus, improving the durability and performance of PEFCs. Sigracet® GDLs were used as a base substrate and two different monomers, polydimethylsiloxane (PDMS) added with fumed silica (Si) and fluorinated ethylene propylene (FEP) were used to print a selective pattern on the GDL surfaces [6]. Both the additive manufacturing and spray coating techniques were utilized for creating the hydrophobic pattern on the GDL surfaces. The results of this study demonstrated a novel but simple approach to tune GDL surfaces with selective wetting properties and superhydrophobic interfaces that would enhance water transport. I will discuss some of these results and highlight how these results will benefit the water management of next-generation high-power PEFCs.This work was funded by the Engineering and Physical Sciences Research Council (EP/P03098X/1) and the STFC Batteries Network (ST/R006873/1) and was supported by SGL Carbon SE (www.sglcarbon.com).

A Classical Search Game in Discrete Locations

Consider a two-person zero-sum search game between a hider and a searcher. The hider hides among n discrete locations, and the searcher successively visits individual locations until finding the hider. Known to both players, a search at location i takes ti time units and detects the hider—if hidden there—independently with probability [Formula: see text] for [Formula: see text]. The hider aims to maximize the expected time until detection, whereas the searcher aims to minimize it. We prove the existence of an optimal strategy for each player. In particular, any optimal mixed hiding strategy hides in each location with a nonzero probability, and there exists an optimal mixed search strategy that can be constructed with up to n simple search sequences. Funding: This work was supported by the Engineering and Physical Sciences Research Council [Grant EP/L015692/1] STOR-i Centre for Doctoral Training.

An exploratory analysis of the response to ChAdOx1 nCoV-19 (AZD1222) vaccine in males and females.

SummaryBackgroundThere are known differences in vaccine reactogenicity and immunogenicity by sex. Females have been shown to report greater reactogenicity and generate higher humoral and cellular immune responses than males following vaccination with several different vaccines. Whether this is also the case for COVID-19 vaccines is currently unknown, as COVID-19 vaccine study data disaggregated by sex are not routinely reported. Therefore, we have assessed the influence of sex on reactogenicity, immunogenicity and efficacy of COVID-19 vaccine ChAdOx1 nCoV-19.MethodsVaccine efficacy was assessed in 15169 volunteers enrolled into single-blind randomised controlled trials of ChAdOx1 nCoV-19 in Brazil and the UK, with the primary endpoint defined as nucleic acid amplification test (NAAT)-positive symptomatic SARS-CoV-2 infection. All participants were electronically randomised to receive two standard doses of vaccine or the control product. Logistic regression models were fitted to explore the effect of age and sex on reactogenicity, and linear models fitted to log-transformed values for immunogenicity data. Reactogenicity data were taken from self-reported diaries of 788 trial participants. Pseudovirus neutralisation assay data were available from 748 participants and anti-SARS-CoV-2 spike IgG assay data from 1543 participants.Findings7619 participants received ChAdOx1 nCoV-19 and 7550 received the control. Vaccine efficacy in participants after two doses of ChAdOx1 nCoV-19 (4243 females and 3376 males) was 66.1% (95% CI 55.9-73.9%) in males and 59.9% (95% CI 49.8-67.9%) in females; with no evidence of a difference in efficacy between the sexes (vaccine by sex interaction term P=0.3359). A small, statistically significant difference in anti-spike IgG was observed (adjusted GMR 1.14; 95% CI 1.04-1.26), with higher titres in females than males, but there were no statistically significant differences in other immunological endpoints. Whilst the majority of individuals reported at least one systemic reaction following a first dose of ChAdOx1 nCoV-19, females were twice as likely as males to report any systemic reaction after a first dose (OR 1.95; 95% CI 1.37-2.77). Measured fever of 38°C or above was reported in 5% of females and 1% of males following first doses. Headache and fatigue were the most commonly reported reactions in both sexes.InterpretationOur results show that there is no evidence of difference in efficacy of the COVID-19 vaccine ChAdOx1 nCoV-19 in males and females. Greater reactogenicity in females was not associated with any difference in vaccine efficacy.FundingStudies were registered with ISRCTN 90906759 (COV002) and ISRCTN 89951424 (COV003) and follow-up is ongoing. Funding was received from the UK Research and Innovation, Engineering and Physical Sciences Research Council, National Institute for Health Research, Coalition for Epidemic Preparedness Innovations, National Institute for Health Research Oxford Biomedical Research Centre, Chinese Academy of Medical Sciences Innovation Fund for Medical Science, Thames Valley and South Midlands NIHR Clinical Research Network, the Lemann Foundation, Rede D'Or, the Brava and Telles Foundation, the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior, Brazil, and AstraZeneca.

Engineering, Technology & Applied Science Research (ETASR), Vol. 12, No. 3, pp. 8482-8776

Engineering, Technology & Applied Science Research (ETASR), Vol. 12, No. 3, pp. 8482-8776

Design and Fabrication of Electric Foldable Bike

Abstract: In the current lifestyle, man isn't able to dedicate specific time for his health. This importance towards exercise and body fitness thanks to time shortage and stressful life is extremely low. To address the time deficit, we are able to utilize the time spent on commuting efficiently to exercise by using bielectric bikes, thereby also contributing to pollution control. But regular bielectric bikes occupy sufficient space to park and aren't easy to hold around additionally because of the possibility of theft. Transport has been one of the foremost important issues to be restricted within the present day situation, as commuting from place to position within the town has become a tedious and a chic task. it's very difficult to succeed in the closest transport facility and in many cases the destination is very far away from most roads where the general public transport won't be able to commute or it'd be very expensive. To overcome a typical problem faced by the society, a plan is conceptualized International Journal for Research in Applied Science & Engineering Technology (IJRASET) ISSN: 2321-9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue V May 2022- Available at www.ijraset.com ©IJRASET: All Rights are Reserved | SJ Impact Factor 7.538 | ISRA Journal Impact Factor 7.894 | 5330 to style and fabricate a foldable bielectric bike. We have already seen many foldable bielectric bikes within the global market but the main idea of this project is to supply a foldable bielectric bike which is light & sleek yet rigid & safe. It's easy to handle and straightforward to take care of. Unlike the standard electric bikes, this bielectric bike will occupy very less space and is also very easy to hold around. Also it utilises the regenerative braking system. The most objective is to style and develop a foldable bielectric bike which is comfortable to ride and economical

A Bibliometric Analysis of Computational Mapping on Publishing Teaching Science Engineering Using VOSviewer Application and Correlation

The purpose of this study was to describe the development of research in the field of teaching science engineering using VOSviewer. This research was conducted by creating a journal database from the Publish or Perish application and managed with the VOSviewer application to classify and visualize the database using the keyword of “Teaching Science Engineering” in the last 10 years (form 2012-2021). A total of 998 articles related to teaching science engineering were analysed and mapped. Based on the mapping results, three terms were obtained, namely “Mathematics” which was associated with 51 links and 335 link strengths; “Engineering Education” associated with 49 links and 146 link strengths; and “Science Technology Engineering Mathematics (STEM)” associated with 48 links and 270 link strengths, respectively. In addition, an analysis of the findings showed that teaching science engineering research in 2012-2016 experienced fluctuating developments. Meanwhile, from 2017 to 2021, teaching science engineering research continues to experience a significant decline. The significant decline in 2019-2021 occurred due to pandemic conditions that impacted the teaching of science engineering related to direct practice in the field. Therefore, it is hoped that the study can help and become a reference for researchers in conducting and determining the research themes to be taken.

Trainable segmentation for transmission electron microscope images of inorganic nanoparticles.

We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user-labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and neural networks, when used on transmission electron microscope images of nanoparticles. We also quantitatively investigate the effectiveness of the components of trainable segmentation, its filter kernels and classifiers, in order to demonstrate the use cases for the different filter kernels in ParticleSpy and the most accurate classifiers for different data types. A set of filter kernels is identified that are effective in distinguishing particles from background but that retain dissimilar features. In terms of classifiers, we find that different classifiers perform optimally for different image contrast; specifically, a random forest classifier performs best for high-contrast ADF images, but that QDA and Gaussian Naïve Bayes classifiers perform better for low-contrast TEM images.

Complex interlinkages, key objectives, and nexuses among the Sustainable Development Goals and climate change: a network analysis

Global sustainability is an enmeshed system of complex socioeconomic, climatological, and ecological interactions. The numerous objectives of the UN's Sustainable Development Goals (SDGs) and the Paris Agreement have various levels of interdependence, making it difficult to ascertain the influence of changes to particular indicators across the whole system. In this analysis, we aimed to detect and rank the complex interlinkages between objectives of sustainability agendas. We developed a method to find interlinkages among the 17 SDGs and climate change, including non-linear and non-monotonic dependences. We used time series of indicators defined by the World Bank, consisting of 400 indicators that measure progress towards the 17 SDGs and an 18th variable (annual average temperatures), representing progress in the response to the climate crisis, from 2000 to 2019. This method detects significant dependencies among the time evolution of the objectives by using partial distance correlations, a non-linear measure of conditional dependence that also discounts spurious correlations originating from lurking variables. We then used a network representation to identify the most important objectives (using network centrality) and to obtain nexuses of objectives (defined as highly interconnected clusters in the network). Using temporal data from 181 countries spanning 20 years, we analysed dependencies among SDGs and climate for 35 country groupings based on region, development, and income level. The observed significant interlinkages, central objectives, and nexuses identified varied greatly across country groupings; however, SDG 17 (partnerships for the goals) and climate change ranked as highly important across many country groupings. Temperature rise was strongly linked to urbanisation, air pollution, and slum expansion (SDG 11), especially in country groupings likely to be worst affected by climate breakdown, such as Africa. In several country groupings composed of developing nations, we observed a consistent nexus of strongly interconnected objectives formed by SDG 1 (poverty reduction), SDG 4 (education), and SDG 8 (economic growth), sometimes incorporating SDG 5 (gender equality), and SDG 16 (peace and justice). The differences across groupings emphasise the need to define goals in accordance with local circumstances and priorities. Our analysis highlights global partnerships (SDG 17) as a pivot in global sustainability efforts, which have been strongly linked to economic growth (SDG 8). However, if economic growth and trade expansion were repositioned as a means instead of an end goal of development, our analysis showed that education (SDG 4) and poverty reduction (SDG 1) become more central, thus suggesting that these could be prioritised in global partnerships. Urban livelihoods (SDG 11) were also flagged as important to avoid replicating unsustainable patterns of the past. Engineering and Physical Sciences Research Council, UK Research and Innovation.

Pharmacogenomics: Relevance and opportunities for clinical pharmacology

Pharmacogenomics: Relevance and opportunities for clinical pharmacology

Escherichia coli Harboring Mcr-1 on a Novel Plasmid in a Mink in Eastern China

Research in Medical & Engineering Sciences Escherichia coli Harboring Mcr-1 on a Novel Plasmid in a Mink in Eastern China Xinxing Wang1, Lin Qi2* and Qiaoyun Wang2 1School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, China 2Therapeutic Center for Liver Disease, 960th Hospital of PLA, China *Corresponding author: Lin Qi, Therapeutic Center for Liver Disease, 960th Hospital of PLA, China Submission: March 22, 2022Published: March 30, 2022 DOI: 10.31031/RMES.2022.09.000720 ISSN: 2576-8816Volume9 Issue4

Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study

SummaryBackgroundThe omicron variant (B.1.1.529) of SARS-CoV-2 has demonstrated partial vaccine escape and high transmissibility, with early studies indicating lower severity of infection than that of the delta variant (B.1.617.2). We aimed to better characterise omicron severity relative to delta by assessing the relative risk of hospital attendance, hospital admission, or death in a large national cohort.MethodsIndividual-level data on laboratory-confirmed COVID-19 cases resident in England between Nov 29, 2021, and Jan 9, 2022, were linked to routine datasets on vaccination status, hospital attendance and admission, and mortality. The relative risk of hospital attendance or admission within 14 days, or death within 28 days after confirmed infection, was estimated using proportional hazards regression. Analyses were stratified by test date, 10-year age band, ethnicity, residential region, and vaccination status, and were further adjusted for sex, index of multiple deprivation decile, evidence of a previous infection, and year of age within each age band. A secondary analysis estimated variant-specific and vaccine-specific vaccine effectiveness and the intrinsic relative severity of omicron infection compared with delta (ie, the relative risk in unvaccinated cases).FindingsThe adjusted hazard ratio (HR) of hospital attendance (not necessarily resulting in admission) with omicron compared with delta was 0·56 (95% CI 0·54–0·58); for hospital admission and death, HR estimates were 0·41 (0·39–0·43) and 0·31 (0·26–0·37), respectively. Omicron versus delta HR estimates varied with age for all endpoints examined. The adjusted HR for hospital admission was 1·10 (0·85–1·42) in those younger than 10 years, decreasing to 0·25 (0·21–0·30) in 60–69-year-olds, and then increasing to 0·47 (0·40–0·56) in those aged at least 80 years. For both variants, past infection gave some protection against death both in vaccinated (HR 0·47 [0·32–0·68]) and unvaccinated (0·18 [0·06–0·57]) cases. In vaccinated cases, past infection offered no additional protection against hospital admission beyond that provided by vaccination (HR 0·96 [0·88–1·04]); however, for unvaccinated cases, past infection gave moderate protection (HR 0·55 [0·48–0·63]). Omicron versus delta HR estimates were lower for hospital admission (0·30 [0·28–0·32]) in unvaccinated cases than the corresponding HR estimated for all cases in the primary analysis. Booster vaccination with an mRNA vaccine was highly protective against hospitalisation and death in omicron cases (HR for hospital admission 8–11 weeks post-booster vs unvaccinated: 0·22 [0·20–0·24]), with the protection afforded after a booster not being affected by the vaccine used for doses 1 and 2.InterpretationThe risk of severe outcomes following SARS-CoV-2 infection is substantially lower for omicron than for delta, with higher reductions for more severe endpoints and significant variation with age. Underlying the observed risks is a larger reduction in intrinsic severity (in unvaccinated individuals) counterbalanced by a reduction in vaccine effectiveness. Documented previous SARS-CoV-2 infection offered some protection against hospitalisation and high protection against death in unvaccinated individuals, but only offered additional protection in vaccinated individuals for the death endpoint. Booster vaccination with mRNA vaccines maintains over 70% protection against hospitalisation and death in breakthrough confirmed omicron infections.FundingMedical Research Council, UK Research and Innovation, Department of Health and Social Care, National Institute for Health Research, Community Jameel, and Engineering and Physical Sciences Research Council.