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Background\ud \ud Oral lichen planus (OLP) is a chronic inflammatory disorder of the oral mucosa. Currently there is no approved treatment for oral lichen planus (OLP). We report on the efficacy and safety of a novel mucoadhesive clobetasol patch (Rivelin®-CLO) for the treatment of OLP.\ud \ud \ud \ud Methods\ud \ud Patients with confirmed OLP and measurable symptomatic ulcer(s) participated in a randomized, double-blind, placebo-controlled, multicenter clinical trial testing a novel mucoadhesive clobetasol patch (Rivelin®-CLO) in OLP across Europe, Canada and USA. Patients were randomized to placebo (non-medicated), 1, 5, 20 µg Clobetasol/patch, twice daily, for 4 weeks. The primary endpoint was change in total ulcer area compared to baseline. Secondary endpoints included improvement from baseline in pain, disease activity, and quality of life.\ud \ud \ud \ud Results\ud \ud Data were analyzed and expressed as mean [SD]. One hundred thirty-eight (138) patients were included in the study; 99 females and 39 males, mean age was 61.1 [11.6] years. Statistical analyses revealed that treatment with 20-μg Rivelin®-CLO patches demonstrated significant improvement with ulcer area (P=0.047), symptom severity (P=0.001), disease activity (P=0.022), pain (P=0.012), and quality of life (P=0.003) as compared with placebo. Improvement in OLP symptoms from beginning to the end of the study was reported as very much better (best rating) in the 20-µg group (25/32) patients compared to the placebo group (11/30), (P=0.012). Adverse events were mild/moderate. Candidiasis incidence was low (2%).\ud \ud \ud \ud Conclusions\ud \ud Rivelin®-CLO patches were superior to placebo demonstrating statistically significant, clinically relevant efficacy in objective and subjective improvement and, with a favorable safety profile.

X-ray nanotomography has proven to be a powerful tool for the characterization of nanoscale materials and structures, such as energy storage devices and next-generation integrated circuits (ICs). In a nanotomography measurement the X-ray spot size must be kept at the nanoscale, which significantly limits the total amount of X-ray flux achievable. Due to this photon limit, nanotomography has been generally restricted to synchrotron facilities, where higher flux can be maintained even at nanoscale spot sizes. Here, we present a laboratory-scale nanotomography instrument, deemed TOMCAT (TOMographic Circuit Analysis Tool). TOMCAT combines the electron beam of a scanning electron microscope (SEM) with the precise, broadband X-ray detection of a superconducting transition-edge sensor (TES) spectrometer. The electron beam generates a highly focused X-ray spot in a metal target, while the TES spectrometer isolates target photons with high signal-to-noise. TOMCAT design is discussed, including target optimization, system geometry, and scan procedures. Features which are 160 nm wide are resolved in three-dimensional images on a Cu-SiO$_2$ IC sample, demonstrating the capabilities of laboratory-scale nanotomography based on a focused electron beam and TES spectrometer., Comment: The following article has been submitted to Review of Scientific Instruments

Total Worker Health® (TWH) interventions that utilize integrated approaches to advance worker safety, health, and well-being can be challenging to design and implement in practice. This may be especially true for the food service industry, characterized by high levels of injury and turnover. This paper illustrates how we used TWH Implementation Guidelines to develop and implement an organizational intervention to improve pain, injury, and well-being among low-wage food service workers. We used the Guidelines to develop the intervention in two main ways: first, we used the six key characteristics of an integrated approach (leadership commitment; participation; positive working conditions; collaborative strategies; adherence; data-driven change) to create the foundation of the intervention; second, we used the four stages to guide integrated intervention planning. For each stage (engaging collaborators; planning; implementing; evaluating for improvement), the Guidelines provided a flexible and iterative process to plan the intervention to improve safety and ergonomics, work intensity, and job enrichment. This paper provides a real-world example of how the Guidelines can be used to develop a complex TWH intervention for food service workers that is responsive to organizational context and addresses targeted working conditions. Application of the Guidelines is likely transferable to other industries.

Background: Depressed individuals use language differently than healthy controls and it has been proposed that social media posts could therefore be used to identify depression. But much of the evidence behind this claim relies on indirect measures of mental health that are sometimes circular, such as statements of self-diagnosis (“Got an OCD diagnosis today”) on social media or membership in disorder-specific online forums. Relatedly, few studies have tested if these language features are specific to depression versus other aspects of mental health. Methods: We analyzed the Tweets of 1,006 participants who completed questionnaires assessing symptoms of depression and 8 other mental health conditions. Daily Tweets were subjected to textual analysis and the resulting linguistic features were used to train an Elastic Net model on depression severity, using nested cross validation. We then tested performance in a held-out test set (30%), comparing predictions of depression versus 8 other aspects of mental health. Results: The depression trained model had only modest predictive performance when tested out of sample, explaining just 2.5% of variance in depression symptoms (R2 = 0.025, r = 0.16). The performance of this model was as-good or superior when used to identify other aspects of mental health: schizotypy (R2 = 0.035, r = 0.19), social anxiety (R2 = 0.025, r = 0.16), eating disorders (R2 = 0.011, r = 0.12), generalized anxiety (R2 = 0.041, r = 0.21), above chance for obsessive-compulsive disorder (R2 = 0.011, r = 0.12), apathy (R2 = 0.008, r = 0.11), but not significant for alcohol abuse (R2 = -0.012, r = 0.04) or impulsivity (R2 = -0.001, r = 0.08).Conclusions: Machine learning analysis of social media data, when trained on well-validated clinical instruments, could not make meaningful individualized out-of-sample predictions regarding mental health status of users. For the small effects observed, language use associated with depression was non-specific, having similar performance in predicting other mental health problems.

The Learning Together methodology at Liverpool John Moores University attempts to open up higher education for people with criminal convictions. Applied in a criminal justice course, Learning Together aims to create a safe space for criminal justice academics, students, service users and practitioners to come together and form a unique community of practice whereby scholarly activity, life events and professional experience are recognised, applied and practiced within and beyond the classroom. As the initiative has grown and developed, course co-creators have recognised how community engagement as a pedagogical framework holds the ability to reduce cultural distance between academic researchers and the communities in which they work whilst at the same time enriching learning and strengthening communities. This paper provides an insight into the initiative and reflects on how belonging can be embedded via a connected curriculum framework., Innovations in Practice, Vol 14 No 1 (2020)