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Architecture and requirements for Transport Services
Get PDFThis document describes an architecture that exposes transport protocol features to applications for network communication. The Transport Services Application Programming Interface (API) is based on an asynchronous, event-driven interaction pattern. This API uses Messages for representing data transfer to applications and describes how a Transport Services Implementation can use multiple IP addresses, multiple protocols, and multiple paths and can provide multiple application streams. This document provides the architecture and
requirements. It defines common terminology and concepts to be used in definitions of a
Transport Services API and a Transport Services Implementation
Optimized protocols for commonly-used murine models of heart failure with preserved ejection fraction
No full textBackground:
HFpEF is a leading cause of death worldwide and clinically relevant preclinical models are required to identify new therapeutic targets. The most clinically representative murine models of heart failure with preserved ejection fraction (HFpEF) in common use include a "2-hit" model combining metabolic stress with hypertension (high-fat diet [HFD] + N(gamma)-nitro-L-arginine methyl ester [L-NAME]) and a "3-hit" model that includes age as an additional "hit" (age + HFD + deoxycorticosterone pivalate [DOCP]). However, both models have reproducibility challenges, and sub-strain and sex dependency. Here we optimize both preclinical models to overcome these challenges.
Methods:
In this study we optimized both models: (1) The 2-hit model was optimised to reproduce HFpEF (defined as the induction and maintenance of obesity, hypertension, diastolic dysfunction, left ventricular hypertrophy, lung congestion, and exercise intolerance) in both C57BL/6N and 6J mice using increasing L-NAME doses (0.5 g/L to 1.75 g/L) and protocol lengths (7 weeks to 13 weeks); and (2) The 3-hit model used 12-week-old C57BL/6N and 6J mice and two aging protocols were compared: HFD for 7 months, or healthy chow for 5 months then high fat diet for 7 months. After HFD, mice received an intraperitoneal injection of DOCP to induce hypertension via sodium retention. To enhance and prolong the effect of DOCP, mice received 1% NaCl drinking water at the time of injection until sacrifice, henceforth called "4-hit". To ensure the phenotype was maintained, a second bolus of DOCP was administered 8 weeks after the first.
Results:
For the 2-hit protocol, HFpEF was successfully induced in C57BL/6J mice when exposed to a 13-week L-NAME protocol with gradually increasing dosage from 1.0 g/L to 1.75 g/L. C57BL/6N mice showed the desired parameters after 7-weeks of 0.5 g/L L-NAME, which were not augmented by increased dosage or time administered. For the 4-hit mice, after addition of 1% NaCl drinking water following DOCP administration, a clear HFpEF phenotype was observed in C57BL/6N and 6J mice in both male and females, and maintained for up to 12 weeks.
Conclusions:
Our modifications ensure the 2-hit model is equally effective in both commonly used J and N substrains of C57BL/6 mice. Our 4-hit model overcomes the challenges of the 3-hit model, enhances reproducibility and robustness, which we demonstrate across sexes and substrains. Both of these new protocols will enhance clinically relevant mechanistic studies on HFpEF
Adaptive drive as a control strategy for fast scanning in dynamic mode atomic force microscopy
Get PDFAtomic Force Microscopy (AFM) is an advanced imaging technique which features nanoscale resolution and the ability to work under physiological conditions on soft samples. Modern AFM systems offer easy access to Dynamic Mode imaging which reduces the tip–sample interaction and increases the effective resolution. However, the intrinsic nature of this driving strategy induces a trade-off between three different aspects: the scanning speed, an accurate topography reconstruction and weak interaction forces. The impact of this inherent trade-off is especially evident when imaging samples with steep and deep valleys, and artifacts are often created in the reconstructed topography. This phenomenon, known as parachuting, rapidly worsens at faster speeds. In this paper, a new strategy is proposed for limiting parachuting artifacts, based on an adaptive driving strategy, which can be easily implemented as an add-on to commercial AFM systems. The suggested method has been tested on grid samples, and it enhances the nano-imaging quality by effectively reducing artifacts in the topography
Scoping review of Japanese encephalitis virus transmission models
Get PDFJapanese encephalitis virus (JEV) causes ~100,000 clinical cases and 25,000 deaths annually worldwide, mainly in Southeast Asia and the Western Pacific and mostly in children. JEV is transmitted to humans through the bite of mosquitoes that have fed on competent hosts. Abiotic factors, such as seasonal rainfall, influence transmission. Transmission models have an important role in understanding disease dynamics and developing prevention and control strategies to limit the impact of infectious diseases. Our goal was to investigate how transmission models capture JEV infection dynamics and their role in predicting and controlling infection. This was achieved by identifying published JEV transmission models, describing their features and identifying their limitations, to guide future modelling. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR)-guided scoping review of peer-reviewed JEV transmission models was conducted. Databases searched included PubMed, ProQuest, Scopus, Web of Science and Google Scholar. Of the 881 full-text papers available in English, 29 were eligible for data extraction. Publication year ranged from 1975 to 2023. The median number of host populations represented in each model was 3 (range: 1–8; usually humans, mosquitoes and pigs). Most (72% [n = 21]) models were deterministic, using ordinary differential equations to describe transmission. Ten models were applied (representing a real JEV transmission setting) and validated with field data, while the remaining 19 models were theoretical. In the applied models, data from only a small proportion of countries in Southeast Asia and the Western Pacific were used. Limitations included gaps in knowledge of local JEV epidemiology, vector attributes and the impact of prevention and control strategies, along with a lack of model validation with field data. The lack and limitations of models highlight that further research to understand JEV epidemiology is needed and that there is opportunity to develop and implement applied models to improve control strategies for at-risk populations of animals and humans
Integrating AI in engineering education: a comprehensive review and student-informed module design for UK students
Get PDFContribution: The integration of artificial intelligence (AI) in engineering higher education is becoming increasingly important nowadays. This article contributes to the Scholarship of Integration by providing a comprehensive review of current research on AI integration in engineering higher education and presenting a pilot AI introductory module designed to teach engineering students AI fundamentals. Background: With the rapid development of AI, it is crucial to integrate AI into engineering curricula to prepare students for the workforce. However, there is a lack of comprehensive research on the strategies to integrate AI into engineering higher education. Research Questions (RQs): This article addresses the following RQs: What is the current state of AI integration in engineering higher education? What are the key considerations for integrating AI education into undergraduate engineering programs? What are the challenges and lessons learned when delivering an AI module to undergraduate students majoring in electronics? Methodology: A comprehensive review was conducted to identify current research on pedagogical methods for integrating AI in engineering curricula. A pilot AI introductory module was also developed and implemented based on this comprehensive review. To customize module design for U.K. students, data was collected from a program review of 29 universities in the U.K. to understand the platforms used to deliver these programs. Finally, surveys were used to evaluate the impact of this module and to identify any challenges and lessons learned. Findings: Our comprehensive review revealed a lack of comprehensive research on AI integration in engineering higher education. The program review results showed that 29 universities in the U.K. offer AI and engineering-related knowledge in the same curriculum, among which London leads the trend. Following the review, an AI module was developed and delivered to 150 U.K. first-year electronics and electrical engineering students. The module was evaluated via entry and exit surveys that were completed by 114 and 104 students, respectively. The results suggested that the pilot AI module aids in teaching AI fundamentals to undergraduate engineering students, with 97% of students agreeing that the module can increase their future job competencies. The review and developed module can serve as valuable references for introducing AI into existing engineering programs at the undergraduate level
Using natural experiments to evaluate population health interventions: a framework for producers, funders, publishers and users of evidence
Get PDFBackground:
There has been a substantial increase in the conduct of natural experimental evaluations in the last 10 years. This has been driven by advances in methodology, greater availability of large routinely collected datasets, and a rise in demand for evidence about the impacts of upstream population health interventions. It is important that researchers, practitioners, commissioners, and users of intervention research are aware of the recent developments. This new framework updates and extends existing Medical Research Council guidance for using natural experiments to evaluate population health interventions.
Methods:
The framework was developed with input from three international workshops and an online consultation with researchers, journal editors, funding representatives, and individuals with experience of using and commissioning natural experimental evaluations. The project team comprised researchers with expertise in natural experimental evaluations. The project had a funder-assigned oversight group and an advisory group of independent experts.
Results:
The framework defines key concepts and provides an overview of recent advances in designing and planning evaluations of natural experiments, including the relevance of a systems perspective, mixed methods and stakeholder involvement throughout the process. It provides an overview of the strengths, weaknesses, applicability and limitations of the range of methods now available, identifies issues of infrastructure and data governance, and provides good practice considerations.
Limitations:
The framework does not provide detailed information for the substantial volume of themes and material covered, rather an overview of key issues to help the conduct and use of natural experimental evaluations.
Conclusion:
This updated and extended framework provides an integrated guide to the use of natural experimental methods to evaluate population health interventions. The framework provides a range of tools to support its use and detailed, evidence-informed recommendations for researchers, funders, publishers, and users of evidence
Effectiveness of a personalised self-management intervention for people living with Long Covid: the LISTEN randomised controlled trial
Get PDFObjective: To evaluate the effectiveness of Listen, a self-management support intervention, for people living with long covid who were not in hospital.
Design: Pragmatic, multicentre, parallel group, randomised controlled trial.
Setting: Twenty four sites in England and Wales.
Participants: Identified from long covid clinic waiting lists, word of mouth, and adverts/social media self-referred to the trial, 554 adults with long covid were randomised to receive either the Listen trial intervention or NHS usual care.
Interventions: The Listen intervention involved up to six one-to-one personalised sessions with trained healthcare practitioners and an accompanying handbook co-designed by people with lived experience and health professionals. Usual NHS care was variable, ranging from no access, access to mobile applications and resources, and to specialist long covid clinics.
Main outcome measures: The primary outcome was the Oxford participation and activities questionnaire (Ox-PAQ) routine activities scale score at three months assessed in the intention-to-treat population. Secondary outcomes included Ox-PAQ emotional wellbeing and social engagement scale scores, the Short Form-12 (SF-12) health survey, the fatigue impact scale, and the generalised self-efficacy scale at three months. The EuroQol five-dimension five-level (EQ-5D-5L) assessed health utility. Serious adverse events were recorded.
Results: Between 27 May 2022 and 15 September 2023, 554 people with long covid (mean age 50 (standard deviation 12.3) years; 394 (72.4%) women) were randomly assigned. At three months, participants assigned to the intervention group reported small non-significant improvements in the primary outcome of capacity for daily activities as assessed by Ox-PAQ routine activities scale score (adjusted mean difference −2.68 (95% confidence interval (CI) −5.38 to 0.02), P=0.052) compared with usual NHS care. For the secondary outcomes, people receiving the intervention also reported significant improvements in mental health (Ox-PAQ emotional wellbeing −5.29 (95% CI −8.37 to −2.20), P=0.001; SF-12 2.36 (95% CI 0.77 to 3.96), P=0.004), reductions in fatigue (fatigue impact score −7.93 (95% CI −11.97 to −3.88), P<0.001), and increases in self-efficacy (generalised self-efficacy scale 2.63 (95% CI 1.50 to 3.75), P<0.001). No differences were found in social engagement (−2.07 (95% CI −5.36 to 1.22), P=0.218) or SF-12 physical health (0.32 (95% CI −0.93 to 1.57), P=0.612). No intervention related serious adverse events were reported.
Conclusions: The personalised self-management support intervention of the Listen trial resulted in non-significant short term improvements in routine activities when compared with usual care. Improvements in emotional wellbeing, fatigue, quality of life, and self-efficacy for people living with long covid were also reported. Physical health and social engagement were not affected by the trial intervention. The limited understanding of how much change is clinically meaningful in this population along with the unblinded design, the use of self-referral as a recruitment method and variable usual care may have introduced unintended bias and thus limits robust conclusions about this intervention. Further research is required to fully establish the impact of the intervention.
Trial registration number: ISRCTN36407216, ISRCTN registry, registered 27 January 2022
Investigating causal effects of income on health using two-sample Mendelian randomisation
Get PDFBackground:
Income is associated with many health outcomes, but it is unclear how far this reflects a causal relationship. Mendelian randomisation (MR) uses genetic variation between individuals to investigate causal effects and may overcome some of the confounding issues inherent in many observational study designs.
Methods:
We used two-sample MR using data from unrelated individuals to estimate the effect of log occupational income on indicators of mental health, physical health, and health-related behaviours. We investigated pleiotropy (direct effects of genotype on the outcome) using robust MR estimators, CAUSE, and multivariable MR including education as a co-exposure. We also investigated demographic factors and dynastic effects using within-family analyses, and misspecification of the primary phenotype using bidirectional MR and Steiger filtering.
Results:
We found that a 10% increase in income lowered the odds of depression (OR 0.92 [95% CI 0.86–0.98]), death (0.91 [0.86–0.96]), and ever-smoking (OR 0.91 [0.86–0.96]), and reduced BMI (− 0.06 SD [− 0.11, − 0.003]). We found little evidence of an effect on alcohol consumption (− 0.02 SD [− 0.01, 0.05]) or subjective wellbeing (0.02 SD [− 0.003, 0.04]), or on two negative control outcomes, childhood asthma (OR 0.99 [0.87, 1.13]) and birth weight (− 0.02 SD, [− 0.01, 0.05]). Within-family analysis and multivariable MR including education and income were imprecise, and there was substantial overlap between the genotypes associated with income and education: out of 36 genetic variants significantly associated with income, 29 were also significantly associated with education.
Conclusions:
MR evidence provides some limited support for causal effects of income on some mental health outcomes and health behaviours, but the lack of reliable evidence from approaches accounting for family-level confounding and potential pleiotropic effects of education places considerable caveats on this conclusion. MR may nevertheless be a useful complement to other observational study designs since its assumptions and limitations are radically different. Further research is needed using larger family-based genetic cohorts, and investigating the overlap between income and other socioeconomic measures
Data sources:
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