Simultaneous recordings of cortical activity, recorded as the magnetoencephalogram (MEG),and the electromyogram (EMG) of the ipsilateral and contralateral first dorsal interosseous muscles (1DI) were made during maintained voluntary contractions. 2. The MEG recorded from a localized region of the sensorimotor cortex of the dominant hemisphere was coherent with the EMG from the contralateral 1DI muscle over a limited band of frequencies. The peak coherence was confined largely within the beta range of cortical activity (13-35 Hz). Significant cortical activity at 10 Hz and 40-50 Hz was not correlated with motor output. The MEG and EMG from the ipsilateral 1DI muscle were uncorrelated at all frequencies. 3. Significant coherence between the MEG and the EMG was associated with synchronous behaviour between the MEG and EMG in the time domain. 4. The results demonstrate that synchronized cortical activity contributing to MEG activity within the beta range of frequencies during maintained voluntary contractions is coupled to motor output at frequencies of motor-unit activity associated with motor-unit synchronization. This observation provides further evidence for the involvement of cortical neurones in the generation of motor-unit synchronization. 5. We suggest that the coherence between MEG and contralateral EMG observed during maintained isometric contractions may provide an example of binding within the motor system.

ObjectiveTo assess the risk of hospital admission for coronavirus disease 2019 (covid-19) among patient facing and non-patient facing healthcare workers and their household members.DesignNationwide linkage cohort study.SettingScotland, UK, 1 March to 6 June 2020.ParticipantsHealthcare workers aged 18-65 years, their households, and other members of the general population.Main outcome measureAdmission to hospital with covid-19.ResultsThe cohort comprised 158 445 healthcare workers, most of them (90 733; 57.3%) being patient facing, and 229 905 household members. Of all hospital admissions for covid-19 in the working age population (18-65 year olds), 17.2% (360/2097) were in healthcare workers or their households. After adjustment for age, sex, ethnicity, socioeconomic deprivation, and comorbidity, the risk of admission due to covid-19 in non-patient facing healthcare workers and their households was similar to the risk in the general population (hazard ratio 0.81 (95% confidence interval 0.52 to 1.26) and 0.86 (0.49 to 1.51), respectively). In models adjusting for the same covariates, however, patient facing healthcare workers, compared with non-patient facing healthcare workers, were at higher risk (hazard ratio 3.30, 2.13 to 5.13), as were household members of patient facing healthcare workers (1.79, 1.10 to 2.91). After sub-division of patient facing healthcare workers into those who worked in “front door,” intensive care, and non-intensive care aerosol generating settings and other, those in front door roles were at higher risk (hazard ratio 2.09, 1.49 to 2.94). For most patient facing healthcare workers and their households, the estimated absolute risk of hospital admission with covid-19 was less than 0.5%, but it was 1% and above in older men with comorbidity.ConclusionsHealthcare workers and their households contributed a sixth of covid-19 cases admitted to hospital. Although the absolute risk of admission was low overall, patient facing healthcare workers and their household members had threefold and twofold increased risks of admission with covid-19.

Background We aimed to ascertain the cumulative risk of fatal or critical care unit-treated COVID-19 in people with diabetes and compare it with that of people without diabetes, and to investigate risk factors for and build a crossvalidated predictive model of fatal or critical care unit-treated COVID-19 among people with diabetes. MethodsIn this cohort study, we captured the data encompassing the first wave of the pandemic in Scotland, from March 1, 2020, when the first case was identified, to July 31, 2020, when infection rates had dropped sufficiently that shielding measures were officially terminated. The participants were the total population of Scotland, including all people with diabetes who were alive 3 weeks before the start of the pandemic in Scotland (estimated Feb 7, 2020). We ascertained how many people developed fatal or critical care unit-treated COVID-19 in this period from the Electronic Communication of Surveillance in Scotland database (on virology), the RAPID database of daily hospitalisations, the Scottish Morbidity Records-01 of hospital discharges, the National Records of Scotland death registrations data, and the Scottish Intensive Care Society and Audit Group database (on critical care). Among people with fatal or critical care unit-treated COVID-19, diabetes status was ascertained by linkage to the national diabetes register, Scottish Care Information Diabetes. We compared the cumulative incidence of fatal or critical care unit-treated COVID-19 in people with and without diabetes using logistic regression. For people with diabetes, we obtained data on potential risk factors for fatal or critical care unit-treated COVID-19 from the national diabetes register and other linked health administrative databases. We tested the association of these factors with fatal or critical care unit-treated COVID-19 in people with diabetes, and constructed a prediction model using stepwise regression and 20-fold cross-validation. Findings Of the total Scottish population onMarch 1, 2020 (n=5 463 300), the population with diabetes was 319 349 (5•8%), 1082 (0•3%) of whom developed fatal or critical care unit-treated COVID-19 by July 31, 2020, of whom 972 (89•8%) were aged 60 years or older. In the population without diabetes, 4081 (0•1%) of 5 143 951 people developed fatal or critical care unit-treated COVID-19. As of July 31, the overall odds ratio (OR) for diabetes, adjusted for age and sex, was 1•395 (95% CI 1•304-1•494; p<0•0001, compared with the risk in those without diabetes. The OR was 2•396 (1•815-3•163; p<0•0001) in type 1 diabetes and 1•369 (1•276-1•468; p<0•0001) in type 2 diabetes. Among people with diabetes, adjusted for age, sex, and diabetes duration and type, those who developed fatal or critical care unit-treated COVID-19 were more likely to be male, live in residential care or a more deprived area, have a COVID-19 risk condition, retinopathy, reduced renal function, or worse glycaemic control, have had a diabetic ketoacidosis or hypoglycaemia hospitalisation in the past 5 years, be on more...

There is evidence to support the effectiveness of NSP in reducing the transmission of HIV among PWID, although it is likely that other harm reduction interventions have also contributed to the observed reduction in HIV risk. NSP should be considered as just one component of a programme of interventions to reduce both injecting risk and other types of HIV risk behaviour.

Injecting risk behaviours among people who inject drugs (PWID) and high-risk sexual practices among men who have sex with men (MSM) are important routes of hepatitis C virus (HCV) transmission. Current direct-acting antiviral treatment offers unique opportunities for reductions in HCV-related liver disease burden and epidemic control in high-risk groups, but these prospects could be counteracted by HCV reinfection due to on-going risk behaviours after successful treatment. Based on existing data from small and heterogeneous studies of interferon-based treatment, the incidence of reinfection after sustained virological response range from 2-6/100 person years among PWID to 10-15/100 person years among human immunodeficiency virus-infected MSM. These differences mainly reflect heterogeneity in study populations with regards to risk behaviours, but also reflect variations in study designs and applied virological methods. Increasing levels of reinfection are to be expected as we enter the interferon-free treatment era. Individual- and population-level efforts to address and prevent reinfection should therefore be undertaken when providing HCV care for people with on-going risk behaviour. Constructive strategies include acknowledgement, education and counselling, harm reduction optimization, scaled-up treatment including treatment of injecting networks, post-treatment screening, and rapid retreatment of reinfections.