A Wide Dynamic Range Rectifier Based on HEMT Device with a Variable Self-Bias Voltage

This brief focuses on a highly efficient rectifier based on a high-electron-mobility transistor (HEMT) with a wide dynamic range of input power. Due to the nonlinear characteristics of HEMT, the impedance mismatch at different input power levels is a major challenge in rectifier design. Herein, a variable voltage gate self-bias network is proposed. It can dynamically generate a DC voltage according to the input power level, and continuously provide the optimal bias for the HEMT, thereby improving the RF to DC conversion efficiency in a wide input power range. This design does not require any external sensing or dynamic control circuit. The power needed by the self-bias network is provided using a weak coupling structure placed at the input port, which couples a small amount of the received RF power to operate the self-bias network. It is demonstrated that the proposed rectifier can achieve a dynamic operating power range of 24 dB (from 1 to 25 dBm) for over 60% conversion efficiency, or 16 dB for over 70% conversion efficiency in the measurement

Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol

Introduction Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks.Methods and Analysis We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km×5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices’ populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case–control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland.Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype.Ethics and dissemination The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals

Use of record linkage to evaluate treatment outcomes and trial eligibility in a 1 real-world metastatic prostate cancer population in Scotland

Purpose: New treatments are introduced into standard care based on clinical trial results. However, it is not clear if these benefits are reflected in the broader population. This study analysed the clinical outcomes of patients with metastatic castration-resistant prostate cancer, treated with abiraterone and enzalutamide, within the Scottish National Health Service. Methods: Retrospective cohort study using record linkage of routinely collected healthcare data (study period: February 2012 to February 2017). Overall survival (OS) was analysed using Kaplan-Meier methods and Cox Proportional Hazard models; a subgroup analysis comprised potentially trial-eligible patients. Results: Overall, 271 patients were included and 73.8% died during the study period. Median OS was poorer than in the pivotal trials, regardless of medication and indication: 10.8 months (95% confidence interval [CI] 8.6-15.1) and 20.9 months (95% CI 14.9-29.0) for abiraterone, and 12.6 months (95% CI 10.5-18.2) and 16.0 months (95% CI 9.8—not reached) for enzalutamide, post and pre chemotherapy, respectively. Only 46% of patients were potentially “trial eligible” and in this subgroup OS improved. Factors influencing survival included baseline performance status, and baseline prostate-specific antigen, alkaline phosphatase, and albumin levels. Conclusions: Poorer prognostic features of non-trial eligible patients impact real-world outcomes of cancer medicines. Electronic record linkage of routinely collected healthcare data offers an opportunity to report outcomes on cancer medicines at scale and describe population demographics. The availability of such observational data to supplement clinical trial results enables patients and clinicians to make more informed treatment decisions, and policymakers to contextualise trial findings

Impact on emergency and elective hospital-based care in Scotland over the first 12 months of the pandemic: interrupted time-series analysis of national lockdowns

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This analysis is part of the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II) study. EAVE II is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE – The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through the Scottish Government DG Health and Social Care. SAS and AS are also supported by the COVID-19 Longitudinal Health and Wellbeing National Core Study, funded by the Medical Research Council (MC_PC_20030). SVK acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2) and the Scottish Government Chief Scientist Office (SPHSU17). JM is partly funded by the National Institute for Health Research Applied Research Collaboration West (NIHR ARC West).Objectives COVID-19 has resulted in the greatest disruption to National Health Service (NHS) care in its over 70-year history. Building on our previous work, we assessed the ongoing impact of pandemic-related disruption on provision of emergency and elective hospital-based care across Scotland over the first year of the pandemic. Design We undertook interrupted time-series analyses to evaluate the impact of ongoing pandemic-related disruption on hospital NHS care provision at national level and across demographics and clinical specialties spanning the period 29 March 2020?28 March 2021. Setting Scotland, UK. Participants Patients receiving hospital care from NHS Scotland. Main outcome measures We used the percentage change of accident and emergency attendances, and emergency and planned hospital admissions during the pandemic compared to the average admission rate for equivalent weeks in 2018-2019. Results As restrictions were gradually lifted in Scotland after the first lockdown, hospital-based admissions increased approaching pre-pandemic levels. Subsequent tightening of restrictions in September 2020 were associated with a change in slope of relative weekly admissions rate: -1.98% (-2.38, -1.58) in accident and emergency attendance, -1.36% (-1.68, -1.04) in emergency admissions and -2.31% (-2.95, -1.66) in planned admissions. A similar pattern was seen across sex, socioeconomic status and most age groups, except children (0-14 years) where accident and emergency attendance, and emergency admissions were persistently low over the study period. Conclusions We found substantial disruption to urgent and planned inpatient healthcare provision in hospitals across NHS Scotland. There is the need for urgent policy responses to address continuing unmet health needs and to ensure resilience in the context of future pandemics.Publisher PDFPeer reviewe

Author Correction:SARS-CoV-2 infection and COVID-19 vaccination rates in pregnant women in Scotland

Population-level data on COVID-19 vaccine uptake in pregnancy and SARS-CoV-2 infection outcomes are lacking. We describe COVID-19 vaccine uptake and SARS-CoV-2 infection in pregnant women in Scotland, using whole-population data from a national, prospective cohort. Between the start of a COVID-19 vaccine program in Scotland, on 8 December 2020 and 31 October 2021, 25,917 COVID-19 vaccinations were given to 18,457 pregnant women. Vaccine coverage was substantially lower in pregnant women than in the general female population of 18−44 years; 32.3% of women giving birth in October 2021 had two doses of vaccine compared to 77.4% in all women. The extended perinatal mortality rate for women who gave birth within 28 d of a COVID-19 diagnosis was 22.6 per 1,000 births (95% CI 12.9−38.5; pandemic background rate 5.6 per 1,000 births; 452 out of 80,456; 95% CI 5.1−6.2). Overall, 77.4% (3,833 out of 4,950; 95% CI 76.2−78.6) of SARS-CoV-2 infections, 90.9% (748 out of 823; 95% CI 88.7−92.7) of SARS-CoV-2 associated with hospital admission and 98% (102 out of 104; 95% CI 92.5−99.7) of SARS-CoV-2 associated with critical care admission, as well as all baby deaths, occurred in pregnant women who were unvaccinated at the time of COVID-19 diagnosis. Addressing low vaccine uptake rates in pregnant women is imperative to protect the health of women and babies in the ongoing pandemic

Performance of fast-ion loss diagnostic on EAST

The scintillator-based detector for fast-ion loss measurements has been installed on EAST. To obtain high temporal resolution for fast-ion loss diagnostics, fast photomultiplier tube systems have been developed which can supply the complementary measurements to the previous image system with good energy and pitch resolution by using a CCD camera. By applying the rotatable platform, the prompt losses of beam-ions can be measured in normal and reverse magnetic field. The thick-target bremsstrahlung occurring in the stainless steel shield with energetic electrons can produce X-rays, which will strike on the scintillator based detector. To understand this interference on fast-ion loss signals, the effects of energetic electrons on the scintillator-based detector are studied, including runaway electrons in the plasma ramping-up phase and fast electrons accelerated by the lower hybrid wave

SARS-CoV-2 infection and COVID-19 vaccination rates in pregnant women in Scotland

Population-level data on COVID-19 vaccine uptake in pregnancy and SARS-CoV-2 infection outcomes are lacking. We describe COVID-19 vaccine uptake and SARS-CoV-2 infection in pregnant women in Scotland, using whole-population data from a national, prospective cohort. Between the start of a COVID-19 vaccine program in Scotland, on 8 December 2020 and 31 October 2021, 25,917 COVID-19 vaccinations were given to 18,457 pregnant women. Vaccine coverage was substantially lower in pregnant women than in the general female population of 18-44 years; 32.3% of women giving birth in October 2021 had two doses of vaccine compared to 77.4% in all women. The extended perinatal mortality rate for women who gave birth within 28 d of a COVID-19 diagnosis was 22.6 per 1,000 births (95% CI 12.9-38.5; pandemic background rate 5.6 per 1,000 births; 452 out of 80,456; 95% CI 5.1-6.2). Overall, 77.4% (3,833 out of 4,950; 95% CI 76.2-78.6) of SARS-CoV-2 infections, 90.9% (748 out of 823; 95% CI 88.7-92.7) of SARS-CoV-2 associated with hospital admission and 98% (102 out of 104; 95% CI 92.5-99.7) of SARS-CoV-2 associated with critical care admission, as well as all baby deaths, occurred in pregnant women who were unvaccinated at the time of COVID-19 diagnosis. Addressing low vaccine uptake rates in pregnant women is imperative to protect the health of women and babies in the ongoing pandemic. [Abstract copyright: © 2022. The Author(s).