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Abstract Fragile X-associated tremor/ataxia syndrome (FXTAS) is an age-related neurodegenerative disorder caused by a premutation of the FMR1 gene on the X chromosome. Despite the pervasive physical and cognitive effects of FXTAS, no studies have examined language in symptomatic males and females, limiting utility as an outcome measure in clinical trials of FXTAS. The goal of this work is to determine (a) the extent to which male and female FMR1 premutation carriers with FXTAS symptoms differ in their language use and (b) whether language production predicts FXTAS symptoms. Thirty-one individuals with the FMR1 premutation (21M, 10F), ages 58–85 years with some symptoms of FXTAS, were recruited from a larger cross-sectional study. Participants completed a five-minute monologic language sample. Language transcripts were assessed for rate of dysfluencies, lexical-semantics, syntax, and speech rate. Multivariable linear and ordinal regressions were used to predict FXTAS-associated symptoms, cognitive functioning, and executive functioning. Males and females did not differ in their language use. Language production predicted FXTAS symptom severity, cognitive functioning, and executive functioning. Language production difficulties may co-occur with FXTAS-associated symptoms and may be a viable outcome measure in future clinical trials, with future research needed.

An attempt is made to simulate both bioexponential and isotopic scrambling kinetics data with a multiple-well master-equation simulation. These constrain key properties of the short-lived HOONO intermediate, observations of both conformers of the HOONO intermediate itself, isotopic scrambling data for [sup.18.O]H+N[O.sub.2] and observations of HON[O.sub.2] production from the H[O.sub.2] + N reaction.

INTRODUCTION Eleven days after the terrorist attacks of September 11, 2001, Congress passed the Air Transportation Safety and System Stabilization Act (ATSSSA). (1) Title 1V of the Act created the [...]

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BackgroundPragmatic language, or the use of language in social contexts, is a critical skill in daily life, supporting social interactions and the development of meaningful social relationships. Pragmatic language is universally impacted in autism spectrum disorder (ASD) and pragmatic deficits are also common in other neurodevelopmental conditions, particularly those related to ASD, such as fragile X syndrome (FXS). This study used a multi-method, longitudinal approach to characterize potentially unique pragmatic profiles across different neurodevelopmental disabilities, and across contexts that varied in degree of social demand. The utility of computational linguistic analyses, as an efficient tool for capturing pragmatic abilities, was also explored.MethodsPragmatic skills of boys with idiopathic ASD (ASD-O, n = 43), FXS with and without ASD (FXS-ASD, n = 57; FXS-O, n = 14), Down syndrome (DS, n = 22), and typical development (TD, n = 24) were compared using variables obtained from a standardized measure, narrative, and semi-naturalistic conversation at up to three time points.ResultsPragmatic language was most significantly impacted among males with ASD-O and FXS-ASD across all three contexts, with more difficulties in the least structured context (conversation), and also some differences based on FXS comorbidity. Patterns of group differences were more nuanced for boys with FXS-O and DS, with context having less of an impact. Clinical groups demonstrated minimal changes in pragmatic skills with age, with some exceptions. Computational language measurement tools showed some utility for measuring pragmatic skills, but were not as successful as traditional methods at capturing differences between clinical groups.ConclusionOverlap and differences between ASD and other forms of neurodevelopmental disability in general, and between idiopathic and syndromic ASD in particular, have important implications for developing precisely tailored assessment and intervention approaches, consistent with a personalized medicine approach to clinical study and care in ASD.

BackgroundFatigue is a subjective, complex and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. It is a well-recognized manifestation in chronic inflammatory rheumatic diseases, such as Sjögren’s Syndrome and Systemic Lupus Erythematosus and an important predictor of patient’s health-related quality of life (HRQoL). Patient reported outcome questions are the key instruments to assess fatigue. To date, there is no consensus about reliable quantitative assessments of fatigue.MethodObservational data for a period of one month were collected from 296 participants in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity and sleep features, and app-based daily and weekly questions covering various HRQoL factors including pain, mood, general physical activity and fatigue. Descriptive statistics and hierarchical clustering of digital data were used to describe behavioural phenotypes. Gradient boosting classifiers were trained to classify participant-reported weekly fatigue and daily tiredness from multi-sensor and other participant-reported data, and extract a set of key predictive features.ResultsCluster analysis of Fitbit parameters highlighted multiple digital phenotypes, including sleep-affected, fatigued and healthy phenotypes. Features from participant-reported data and Fitbit data both contributed as key predictive features of weekly physical and mental fatigue and daily tiredness. Participant answers to pain and depressed mood-related daily questions contributed the most as top features for predicting physical and mental fatigue, respectively. To classify daily tiredness, participant answers to questions on pain, mood and ability to perform daily activities contributed the most. Features related to daily resting heart rate and step counts and bouts were overall the most important Fitbit features for the classification models.ConclusionThese results demonstrate that multimodal digital data can be used to quantitatively and more frequently augment pathological and non-pathological participant-reported fatigue.

Open dialogue (OD) is a person-centred social network model of crisis and continuing mental healthcare, which promotes agency and long-term recovery in mental illness. Peer support workers who have lived experience of mental illness play a key role in OD in the UK, as they enhance shared understanding of mental health crisis as part of the OD model and provide a sense of belonging and social inclusion. These elements are in alignment with the shared decision making (SDM) approach in mental health, which focuses on person-centred communication in treatment decision-making. The previously documented benefits of peer-led SDM include increased engagement with services, symptom reduction, increased employment opportunities, and reduced utilization of mental and general health services. While the contribution of peer support and SDM principles to OD has been acknowledged, there is only a small body of literature surrounding this development, and little guidance on how peer support can enhance treatment decision-making and other aspects of OD. This viewpoint, which was co-authored by people with lived experience of mental illness, clinicians, and researchers, discusses practical implications and recommendations for research and training for the provision of a co-produced OD model grounded in peer support and SDM.

Abstract Background COVID-19 has resulted in significant morbidity and mortality worldwide. Lateral flow assays can detect anti-Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) antibodies to monitor transmission. However, standardized evaluation of their accuracy and tools to aid in interpreting results are needed. Methods We evaluated 20 IgG and IgM assays selected from available tests in April 2020. We evaluated the assays’ performance using 56 pre-pandemic negative and 56 SARS-CoV-2-positive plasma samples, collected 10–40 days after symptom onset, confirmed by a molecular test and analyzed by an ultra-sensitive immunoassay. Finally, we developed a user-friendly web app to extrapolate the positive predictive values based on their accuracy and local prevalence. Results Combined IgG + IgM sensitivities ranged from 33.9 to 94.6%, while combined specificities ranged from 92.6 to 100%. The highest sensitivities were detected in Lumiquick for IgG (98.2%), BioHit for both IgM (96.4%), and combined IgG + IgM sensitivity (94.6%). Furthermore, 11 LFAs and 8 LFAs showed perfect specificity for IgG and IgM, respectively, with 15 LFAs showing perfect combined IgG + IgM specificity. Lumiquick had the lowest estimated limit-of-detection (LOD) (0.1 μg/mL), followed by a similar LOD of 1.5 μg/mL for CareHealth, Cellex, KHB, and Vivachek. Conclusion We provide a public resource of the accuracy of select lateral flow assays with potential for home testing. The cost-effectiveness, scalable manufacturing process, and suitability for self-testing makes LFAs an attractive option for monitoring disease prevalence and assessing vaccine responsiveness. Our web tool provides an easy-to-use interface to demonstrate the impact of prevalence and test accuracy on the positive predictive values.