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Objective The objectives were to (1) compare satisfaction with social roles and activities in a large multinational systemic sclerosis (SSc) cohort to general population normative data and (2) identify sociodemographic, lifestyle and SSc disease factors associated with satisfaction with social roles and activities.Methods Participants in the Scleroderma Patient-centered Intervention Network Cohort completed the Patient Reported Outcomes Information System Version 2 satisfaction with social roles and activities domain questionnaire. Multivariable regression was used to assess associations with sociodemographic, lifestyle and disease factors.Results Among 2385 participants, mean satisfaction with social roles and activities T-score (48.1, SD=9.9) was slightly lower than the US general population (mean=50, SD=10). Factors independently associated with satisfaction were years of education (0.54 per SD, 95% CI 0.14 to 0.93); non-White race or ethnicity (−1.13, 95% CI −2.18 to –0.08); living in Canada (−1.33, 95% CI −2.40 to –0.26 (reference USA)) or the UK (−2.49, 95% CI −3.92 to –1.06); body mass index (−1.08 per SD, 95% CI −1.47 to –0.69); gastrointestinal involvement (−3.16, 95% CI −4.27 to –2.05); digital ulcers (−1.90, 95% CI −3.05 to –0.76); moderate (−1.62, 95% CI −2.78 to –0.45) or severe (−2.26, 95% CI −3.99 to –0.52) small joint contractures; interstitial lung disease (−1.11, 95% CI −1.97 to –0.25); pulmonary arterial hypertension (−2.69, 95% CI −4.08 to –1.30); rheumatoid arthritis (−2.51, 95% CI −4.28 to –0.73); and Sjogren’s syndrome (−2.42, 95% CI −3.96 to –0.88).Conclusion Mean satisfaction with social roles and activities is slightly lower in SSc than the general population and associated with multiple sociodemographic and disease factors.

Summary: Background: Systemic sclerosis is a heterogenous disease in which little is known about patterns of patient-reported symptom clusters. We aimed to identify classes of individuals with similar anxiety, depression, fatigue, sleep disturbance, and pain symptoms and to evaluate associated sociodemographic and disease-related characteristics. Methods: This multi-centre cross-sectional study used baseline data from Scleroderma Patient-centered Intervention Network Cohort participants enrolled from 2014 to 2020. Eligible participants completed the PROMIS-29 v2.0 measure. Latent profile analysis was used to identify homogeneous classes of participants based on patterns of anxiety, depression, fatigue, sleep disturbance, and pain scores. Sociodemographic and disease-related characteristics were compared across classes. Findings: Among 2212 participants, we identified five classes, including four classes with “Low” (565 participants, 26%), “Normal” (651 participants, 29%), “High” (569 participants, 26%), or “Very High” (193 participants, 9%) symptom levels across all symptoms. Participants in a fifth class, “High Fatigue/Sleep/Pain and Low Anxiety/Depression” (234 participants, 11%) had similar levels of fatigue, sleep disturbance, and pain as in the “High” class but low anxiety and depression symptoms. There were significant and substantive trends in sociodemographic characteristics (age, education, race or ethnicity, marital or partner status) and increasing disease severity (diffuse disease, tendon friction rubs, joint contractures, gastrointestinal symptoms) across severity-based classes. Disease severity and sociodemographic characteristics of “High Fatigue/Sleep/Pain and Low Anxiety/Depression” class participants were similar to the “High” severity class. Interpretation: Most people with systemic sclerosis can be classified by levels of patient-reported symptoms, which are consistent across symptoms and highly associated with sociodemographic and disease-related variables, except for one group which reports low mental health symptoms despite high levels of other symptoms and substantial disease burden. Studies are needed to better understand resilience in systemic sclerosis and to identify and facilitate implementation of cognitive and behavioural strategies to improve coping and overall quality of life. Funding: National Institute of Nursing Research (F31NR019007), Canadian Institutes of Health Research, Arthritis Society Canada, the Lady Davis Institute for Medical Research, the Jewish General Hospital Foundation, McGill University, Scleroderma Society of Ontario, Scleroderma Canada, Sclérodermie Québec, Scleroderma Manitoba, Scleroderma Atlantic, Scleroderma Association of BC, Scleroderma SASK, Scleroderma Australia, Scleroderma New South Wales, Scleroderma Victoria, and Scleroderma Queensland.

Abstract Effective testing is essential to control the coronavirus disease 2019 (COVID-19) transmission. Here we report a-proof-of-concept study on hyperspectral image analysis in the visible and near-infrared range for primary screening at the point-of-care of SARS-CoV-2. We apply spectral feature descriptors, partial least square-discriminant analysis, and artificial intelligence to extract information from optical diffuse reflectance measurements from 5 µL fluid samples at pixel, droplet, and patient levels. We discern preparations of engineered lentiviral particles pseudotyped with the spike protein of the SARS-CoV-2 from those with the G protein of the vesicular stomatitis virus in saline solution and artificial saliva. We report a quantitative analysis of 72 samples of nasopharyngeal exudate in a range of SARS-CoV-2 viral loads, and a descriptive study of another 32 fresh human saliva samples. Sensitivity for classification of exudates was 100% with peak specificity of 87.5% for discernment from PCR-negative but symptomatic cases. Proposed technology is reagent-free, fast, and scalable, and could substantially reduce the number of molecular tests currently required for COVID-19 mass screening strategies even in resource-limited settings.

Abstract Optical spectroscopic techniques have been commonly used to detect the presence of biofilm-forming pathogens (bacteria and fungi) in the agro-food industry. Recently, near-infrared (NIR) spectroscopy revealed that it is also possible to detect the presence of viruses in animal and vegetal tissues. Here we report a platform based on visible and NIR (VNIR) hyperspectral imaging for non-contact, reagent free detection and quantification of laboratory-engineered viral particles in fluid samples (liquid droplets and dry residue) using both partial least square-discriminant analysis and artificial feed-forward neural networks. The detection was successfully achieved in preparations of phosphate buffered solution and artificial saliva, with an equivalent pixel volume of 4 nL and lowest concentration of 800 TU· $$\upmu$$ μ L−1. This method constitutes an innovative approach that could be potentially used at point of care for rapid mass screening of viral infectious diseases and monitoring of the SARS-CoV-2 pandemic.

Spinal cord injury (SCI) is a devastating condition of the central nervous system that strongly reduces the patient’s quality of life and has large financial costs for the healthcare system. Cell therapy has shown considerable therapeutic potential for SCI treatment in different animal models. Although many different cell types have been investigated with the goal of promoting repair and recovery from injury, stem cells appear to be the most promising. Here, we review the experimental approaches that have been carried out with pluripotent stem cells, a cell type that, due to its inherent plasticity, self-renewal, and differentiation potential, represents an attractive source for the development of new cell therapies for SCI. We will focus on several key observations that illustrate the potential of cell therapy for SCI, and we will attempt to draw some conclusions from the studies performed to date.

IntroductionTo decrease psychological risk for content moderators, the study initiated the first steps of developing a robust employment screening tool, namely, the Cognitive Adaptability and Resiliency Employment Screener.MethodThe study consisted of three phases with 4,839 total participants.ResultsIn Phase 1, a set of 75 items were developed and tested via exploratory factor analysis, yielding three factors (i.e., Psychological Perseverance & Agility, Rumination & Emotional Lingering, and Expressiveness & Sociability) and also reducing the scale to 67 items. In Phase 2 through confirmatory factor analysis, the three-factor structure showed good fit (CFI = .93, RMSEA = .05) and demonstrated sufficient overall reliability. In Phase 3, the convergent validity and divergent validity of the tool were established relative to constructs such as resilience, cognitive control and flexibility, emotion regulation, and optimism.DiscussionAltogether, the findings revealed that the scale demonstrated good psychometric properties that, pending future studies, may serve as a promising employment screener for content moderators.