Validation of the Mind Excessively Wandering Scale and the Relationship of Mind Wandering to Impairment in Adult ADHD.

OBJECTIVE: This study investigates excessive mind wandering (MW) in adult ADHD using a new scale: the Mind Excessively Wandering Scale (MEWS). METHOD: Data from two studies of adult ADHD was used in assessing the psychometric properties of the MEWS. Case-control differences in MW, the association with ADHD symptoms, and the contribution to functional impairment were investigated. RESULTS: The MEWS functioned well as a brief measure of excessive MW in adult ADHD, showing good internal consistency (α > .9), and high sensitivity (.9) and specificity (.9) for the ADHD diagnosis, comparable with that of existing ADHD symptom rating scales. Elevated levels of MW were found in adults with ADHD, which contributed to impairment independently of core ADHD symptom dimensions. CONCLUSION: Findings suggest excessive MW is a common co-occurring feature of adult ADHD that has specific implications for the functional impairments experienced. The MEWS has potential utility as a screening tool in clinical practice to assist diagnostic assessment

Health and socio-demographic characteristics associated with uptake of seasonal influenza vaccination amongst pregnant women: retrospective cohort study

Pregnant women are at increased risk from influenza, yet maternal influenza vaccination levels remain suboptimal. This study aimed to estimate associations between socio-demographic and health characteristics and seasonal influenza vaccination uptake among pregnant women and understand trends over time to inform interventions to improve vaccine coverage. A retrospective cohort study using linked electronic health records of women in North West London with at least one pregnancy overlapping with an influenza season between September 2010 and February 2020. We used a multivariable mixed-effects logistic regression model to identify associations between characteristics of interest and primary outcome of influenza vaccination. 451,954 pregnancies, among 260,744 women, were included. In 85,376 (18.9%) pregnancies women were vaccinated against seasonal influenza. Uptake increased from 8.4% in 2010/11 to 26.3% in 2018/19, dropping again to 21.1% in 2019/20. Uptake was lowest among women: aged 15-19 years (12%) or over 40 years (15%; OR 1.17, 95% CI 1.10 to 1.24); of Black ethnicity (14.1%; OR 0.55, 95% CI 0.53 to 0.57), or unknown ethnicity (9.9%; OR 0.42, 95% CI 0.39 to 0.46), lived in more deprived areas (OR least vs most deprived 1.16, 95% CI 1.11 to 1.21), or with no known risk factors for severe influenza. Seasonal influenza vaccine uptake in pregnant women increased in the past decade, prior to the COVID-19 pandemic, but remained suboptimal. We recommend approaches to reducing health inequalities should focus on women of Black ethnicity, younger and older women, and women living in areas of greater socio-economic deprivation

Developing Digital Tools for Remote Clinical Research:How to Evaluate the Validity and Practicality of Active Assessments in Field Settings

The ability of remote research tools to collect granular, high-frequency data on symptoms and digital biomarkers is an important strength because it circumvents many limitations of traditional clinical trials and improves the ability to capture clinically relevant data. This approach allows researchers to capture more robust baselines and derive novel phenotypes for improved precision in diagnosis and accuracy in outcomes. The process for developing these tools however is complex because data need to be collected at a frequency that is meaningful but not burdensome for the participant or patient. Furthermore, traditional techniques, which rely on fixed conditions to validate assessments, may be inappropriate for validating tools that are designed to capture data under flexible conditions. This paper discusses the process for determining whether a digital assessment is suitable for remote research and offers suggestions on how to validate these novel tools

A comparison of machine learning methods for classification using simulation with multiple real data examples from mental health studies

Background: Recent literature on the comparison of machine learning methods has raised questions about the neutrality, unbiasedness and utility of many comparative studies. Reporting of results on favourable datasets and sampling error in the estimated performance measures based on single samples are thought to be the major sources of bias in such comparisons. Better performance in one or a few instances does not necessarily imply so on an average or on a population level and simulation studies may be a better alternative for objectively comparing the performances of machine learning algorithms. Methods: We compare the classification performance of a number of important and widely used machine learning algorithms, namely the Random Forests (RF), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA) and k-Nearest Neighbour (kNN). Using massively parallel processing on high-performance supercomputers, we compare the generalisation errors at various combinations of levels of several factors: number of features, training sample size, biological variation, experimental variation, effect size, replication and correlation between features. Results: For smaller number of correlated features, number of features not exceeding approximately half the sample size, LDA was found to be the method of choice in terms of average generalisation errors as well as stability (precision) of error estimates. SVM (with RBF kernel) outperforms LDA as well as RF and kNN by a clear margin as the feature set gets larger provided the sample size is not too small (at least 20). The performance of kNN also improves as the number of features grows and outplays that of LDA and RF unless the data variability is too high and/or effect sizes are too small. RF was found to outperform only kNN in some instances where the data are more variable and have smaller effect sizes, in which cases it also provide more stable error estimates than kNN and LDA. Applications to a number of real datasets supported the findings from the simulation study

Age-related changes in muscle architecture and metabolism in humans: The likely contribution of physical inactivity to age-related functional decline

In the United Kingdom (UK), it is projected that by 2035 people aged >65 years will make up 23 % of the population, with those aged >85 years accounting for 5% of the total population. Ageing is associated with progressive changes in muscle metabolism and a decline in functional capacity, leading to a loss of independence. Muscle metabolic changes associated with ageing have been linked to alterations in muscle architecture and declines in muscle mass and insulin sensitivity. However, the biological features often attributed to muscle ageing are also seen in controlled studies of physical inactivity (e.g. reduced step-count and bed-rest), and it is currently unclear how many of these ageing features are due to ageing per se or sedentarism. This is particularly relevant at a time of home confinements reducing physical activity levels during the Covid-19 pandemic. Current knowledge gaps include the relative contribution that physical inactivity plays in the development of many of the negative features associated with muscle decline in older age. Similarly, data demonstrating positive effects of government recommended physical activity guidelines on muscle health are largely non-existent. It is imperative therefore that research examining interactions between ageing, physical activity and muscle mass and metabolic health is prioritised so that it can inform on the “normal” muscle ageing process and on strategies for improving health span and well-being. This review will focus on important changes in muscle architecture and metabolism that accompany ageing and highlight the likely contribution of physical inactivity to these changes

Testing the association between tobacco and cannabis use and cognitive functioning: findings from an observational and Mendelian randomization study

Background Although studies have examined the association between tobacco and cannabis use in adolescence with subsequent cognitive functioning, study designs are usually not able to distinguish correlation from causation. Methods Separate patterns of tobacco and cannabis use were derived using longitudinal latent class analysis based on measures assessed on five occasions from age 13–18 in a large UK population cohort (Avon Longitudinal Study of Parents and Children). Cognitive functioning measures comprised of working memory, response inhibition, and emotion recognition assessed at 24 years of age. Mendelian randomization was used to examine the possible causal relationship between smoking initiation, lifetime cannabis use and cognitive functioning. Results We found evidence of a relationship between tobacco and cannabis use and diminished cognitive functioning for each of the outcomes in the observational analyses. There was evidence to suggest that late-onset regular tobacco smokers (b=-0.29, 95 %CI=-0.45 to -0.13), early-onset regular tobacco smokers (b=-0.45, 95 %CI=-0.84 to -0.05), and early-onset regular cannabis users (b=-0.62, 95 %CI=-0.93 to -0.31) showed poorer working memory. Early-onset regular tobacco smokers (b = 0.18, 95 %CI = 0.07 to 0.28), and early-onset regular cannabis users (b = 0.30, 95 %CI = 0.08 to 0.52) displayed poorer ability to inhibit responses. Late-onset regular (b=-0.02, 95 %CI=-0.03 to - 0.00), and early-onset regular tobacco smokers (b=-0.04, 95 %CI=-0.08 to -0.01) showed poorer ability to recognise emotions. Mendelian randomization analyses were imprecise and did not provide additional support for the observational results. Conclusion There was some evidence to suggest that adolescent tobacco and cannabis use were associated with deficits in working memory, response inhibition and emotion recognition. Better powered genetic studies are required to determine whether these associations are causal

A comprehensive evaluation of colonic mucosal isolates of Sutterella wadsworthensis from inflammatory bowel disease

Peer reviewedPublisher PD

Sialylation of campylobacter jejuni lipo-oligosaccharides: impact on phagocytosis and cytokine production in mice

<p>Background: Guillain-Barré syndrome (GBS) is a post-infectious polyradiculoneuropathy, frequently associated with antecedent Campylobacter jejuni (C. jejuni) infection. The presence of sialic acid on C. jejuni lipo-oligosaccharide (LOS) is considered a risk factor for development of GBS as it crucially determines the structural homology between LOS and gangliosides, explaining the induction of cross-reactive neurotoxic antibodies. Sialylated C. jejuni are recognised by TLR4 and sialoadhesin; however, the functional implications of these interactions in vivo are unknown.</p> <p>Methodology/Principal Findings: In this study we investigated the effects of bacterial sialylation on phagocytosis and cytokine secretion by mouse myeloid cells in vitro and in vivo. Using fluorescently labelled GM1a/GD1a ganglioside-mimicking C. jejuni strains and corresponding (Cst-II-mutant) control strains lacking sialic acid, we show that sialylated C. jejuni was more efficiently phagocytosed in vitro by BM-MΦ, but not by BM-DC. In addition, LOS sialylation increased the production of IL-10, IL-6 and IFN-β by both BM-MΦ and BM-DC. Subsequent in vivo experiments revealed that sialylation augmented the deposition of fluorescent bacteria in splenic DC, but not macrophages. In addition, sialylation significantly amplified the production of type I interferons, which was independent of pDC.</p> <p>Conclusions/Significance: These results identify novel immune stimulatory effects of C. jejuni sialylation, which may be important in inducing cross-reactive humoral responses that cause GBS</p&gt

'Unable to have a proper conversation over the phone about my concerns': a multimethods evaluation of the impact of COVID-19 on routine childhood vaccination services in London, UK

Objectives Investigating the completion rate of 12-month vaccinations and parental perspectives on vaccine services during COVID-19. Study-design Service evaluation including parental questionnaire. Methods Uptake of 12-month vaccinations in three London general practices during three periods: pre-COVID (1/3/2018–28/2/2019, n = 826), during COVID (1/3/2019–28/2/2020, n = 775) and post-COVID first wave (1/8/2020–31/1/2021, n = 419). Questionnaire of parents whose children were registered at the practices (1/4/2019–1/22/2021, n = 1350). Results Comparing pre-COVID and both COVID cohorts, the completion rates of 12-month vaccines were lower. Haemophilus influenzae type B/meningococcal group C (Hib/MenC) vaccination uptake was 5.6% lower (89.0% vs 83.4%, P=<0.001), meningococcal group B (MenB) booster uptake was 4.4% lower (87.3% vs 82.9%, P = 0.006), pneumococcal conjugate vaccine (PCV) booster uptake was 6% lower (88.0% vs 82.0%, P < 0.001) and measles, mumps and rubella (MMR) vaccine uptake was 5.2% lower (89.1% vs 83.9%, P = 0.003). Black/Black-British ethnicity children had increased odds of missing their 12-month vaccinations compared to White ethnicity children (adjusted odds ratio 0.43 [95% confidence interval 0.24–0.79, P = 0.005; 0.36 [0.20–0.65], P < 0.001; 0.48 [0.27–0.87], P = 0.01; 0.40 [0.22–0.73], P = 0.002; for Hib/MenC, MenB booster, PCV booster and MMR. Comparing pre-COVID and COVID periods, vaccinations coded as not booked increased for MMR (10%), MenB (7%) and PCV booster (8%). Parents reported changes to vaccination services during COVID-19, including difficulties booking and attending appointments and lack of vaccination reminders. Conclusion A sustained decrease in 12-month childhood vaccination uptake disproportionally affected Black/Black British ethnicity infants during the first wave of the pandemic. Vaccination reminders and availability of healthcare professionals to discuss parental vaccine queries are vital to maintaining uptake