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BACKGROUND: Interprofessional education (IPE) has been promoted as a breakthrough in healthcare because of the impact when professionals work as a team. However, despite its inception dating back to the 1960s, its science has taken a long time to advance. There is a need to theorize IPE to cultivate creative insights for a nuanced understanding of IPE. This study aims to propose a research agenda on social interaction by understanding the measurement scales used and guiding researchers to contribute to the discussion of social processes in IPE. METHOD: This quantitative research was undertaken in a cross-institutional IPE involving 925 healthcare students (Medicine, Nursing, Social Work, Chinese Medicine, Pharmacy, Speech Language Pathology, Clinical Psychology, Food and Nutritional Science and Physiotherapy) from two institutions in Hong Kong. Participants completed the Social Interaction Anxiety Scale (SIAS-6) and Social Phobia Scale (SPS-6). We applied a construct validation approach: within-network and between-network validation. We performed confirmatory factors analysis, t-test, analysis of variance and regression analysis. RESULTS: CFA results indicated that current data fit the a priori model providing support to within-network validity [RMSEA=.08, NFI=.959, CFI=.965, IFI=.965, TLI=.955]. The criteria for acceptable fit were met. The scales were invariant between genders, across year levels and disciplines. Results indicated that social interaction anxiety and social phobia negatively predicted behavioural engagement (F = 25.093, p<.001, R2=.065) and positively predicted behavioural disaffection (F = 22.169, p<.001, R2=.057) to IPE, suggesting between-network validity. CONCLUSIONS: Our data provided support for the validity of the scales when used among healthcare students in Hong Kong. SIAS-6 and SPS-6 have sound psychometric properties based on students' data in Hong Kong. We identified quantitative, qualitative and mixed methods research designs to guide

INTRODUCTION: Over 25% of Dutch young people are psychologically unhealthy. Individual and societal consequences that follow from having mental health complaints at this age are substantial. Young people need care which is often unavailable. ENgage YOung people earlY (ENYOY) is a moderated digital social therapy-platform that aims to help youngsters with emerging mental health complaints. Comprehensive research is being conducted into the effects and to optimize and implement the ENYOY-platform throughout the Netherlands. The aim of this study is to explore the usability and user experience of the ENYOY-platform. METHODS: A user-centered mixed-method design was chosen. 26 young people aged 16-25 with emerging mental health complaints participated. Semi-structured interviews were conducted to explore usability, user-friendliness, impact, accessibility, inclusivity, and connection (Phase 1). Phase 2 assessed usability problems using the concurrent and retrospective Think Aloud-method. User experience and perceived helpfulness were assessed using a 10-point rating scale and semi-structured interviews (Phase 3). The Health Information Technology Usability Evaluation Scale (Health-ITUES; Phase 1) and System Usability Scale (SUS; Phase 2 and 3) were administered. Qualitative data was analyzed using thematic analysis. Task completion rate and time were tracked and usability problems were categorized using the Nielsen's rating scale (Phase 2). RESULTS: Adequate to high usability was found (Phase 1 Health-ITUES 4.0(0.34); Phase 2 SUS 69,5(13,70); Phase 3 SUS 71,6(5,63)). Findings from Phase 1 (N = 10) indicated that users viewed ENYOY as a user-friendly, safe, accessible, and inclusive initiative which helped them reduce their mental health complaints and improve quality of life. Phase 2 (N = 10) uncovered 18 usability problems of which 5 of major severity (e.g. troubles accessing the platform). Findings from Phase 3 (N = 6) suggested that users perceived the coaching calls

INTRODUCTION: To optimize treatment, it is of utmost importance to take into account the myriad of biological, social, and psychological changes that young people go through during adolescence which make them more vulnerable for developing mental health problems. Biocueing, a non-invasive method to transform physiological parameters into an observable signal, could strengthen stress- and emotion regulation by cueing physiologically unusual values in daily life. The aim of this study is to investigate the usability, feasibility, and exploratory effect of biocueing in addition to ENgage YOung people earlY (ENYOY), a moderated digital social therapy-platform, in young people with emerging mental health complaints. METHODS: A user-centered mixed-method design was used. A focus group was conducted to optimize the ENYOY-platform and biocueing intervention. Biocueing was operationalized by a smartwatch and the Sense-IT app. A within-subjects design was used; 10 days for all participants 'biofeedback off' (control), followed by 10 days 'biofeedback on' (experimental). Emotional awareness and perceived stress were measured using ecological momentary assessment. Eight individuals participated. User-friendliness, usability, and acceptance were assessed using a qualitative design. RESULTS: Findings from the focus group resulted in several adaptations of the biocueing intervention to the ENYOY-platform and vice versa. The average measurement compliance rate was 78.8%. Level-one findings showed different individual effects on perceived stress and emotional awareness. Level-two analyses showed no overall effects on perceived stress (B = -0.020, p = 0.562) and overall positive effects on emotional awareness (B = 0.030, p = 0.048) with small effect sizes (Improvement Rate Difference = 0.05-0.35). The intervention was found to be acceptable and showed moderate usability. Participants indicated they experienced improvements in reflection on feelings and changes in behavior, such as pa

BACKGROUND: The onset of mental disorders typically occurs between the ages of 12 and 25, and the burden of mental health problems is the most consequential for this group. Indicated prevention interventions to target individuals with subclinical symptoms to prevent the transition to clinical levels of disorders, even leading to suicide, have shown to be effective. However, the threshold to seek help appears to be high. Digital interventions could offer a solution, especially during the Covid-19 pandemic. This implementation study will investigate the digital indicated prevention intervention ENgage YOung people Early (ENYOY), the Dutch version of the original Moderated Online Social Therapy Platform (MOST+) from Australia. In addition, the relationship between stress biomarkers, symptoms and outcome measures of youth using the platform will be investigated in this study. METHODS: The MOST+ platform will be adapted, translated and developed for the situation in the Netherlands in collaboration with a Youth Panel. A prospective cohort of 125 young people (16-25 years) with beginning mental health complaints will be on the platform and followed for a year, of which 10 participants will have an additional smart watch and 10 participants will be asked to provide feedback about the platform. Data will be collected at baseline and after 3, 6 and 12 months. Outcome measures are Psychological Distress assessed with the Kessler Psychological Distress Scale (K10), Social and occupational functioning (measures by the SOFAS), positive mental health indicators measured by the Positive Health Instrument, stress biomarkers with a smart-watch, website journeys of visitors, and feedback of youth about the platform. It will be a mixed-method study design, containing qualitative and quantitative measures. DISCUSSION: This trial will specifically address young people with emerging mental health complaints, and offers a new approach for treatment in the Netherlands. Considering the wait

Tumours are abnormal growths of cells that reproduce by redirecting essential nutrients and resources from surrounding tissue. Changes to cell metabolism that trigger the growth of tumours are reflected in subtle differences between the chemical composition of healthy and malignant cells. We used LA-ICP-MS imaging to investigate whether these chemical differences can be used to spatially identify tumours and support detection of primary colorectal tumours in anatomical pathology. First, we generated quantitative LA-ICP-MS images of three colorectal surgical resections with case-matched normal intestinal wall tissue and used this data in a Monte Carlo optimisation experiment to develop an algorithm that can classify pixels as tumour positive or negative. Blinded testing and interrogation of LA-ICP-MS images with micrographs of haematoxylin and eosin stained and Ki67 immunolabelled sections revealed Monte Carlo optimisation accurately identified primary tumour cells, as well as returning false positive pixels in areas of high cell proliferation. We analysed an additional 11 surgical resections of primary colorectal tumours and re-developed our image processing method to include a random forest regression machine learning model to correctly identify heterogenous tumours and exclude false positive pixels in images of non-malignant tissue. Our final model used over 1.6 billion calculations to correctly discern healthy cells from various types and stages of invasive colorectal tumours. The imaging mass spectrometry and data analysis methods described, developed in partnership with clinical cancer researchers, have the potential to further support cancer detection as part of a comprehensive digital pathology approach to cancer care through validation of a new chemical biomarker of tumour cells.

Objective: Between the ages of 12 and 25 the onset of mental disorders typically occurs, and the burden of mental health problems is greatest for this group. Indicated preventive interventions to target individuals with subclinical symptoms to prevent the transition to clinical levels of disorders have gained considerable traction. However, the threshold to seek help appears to be high even when help is needed. Online interventions could offer a solution, especially during the COVID-19 pandemic. This scoping review will present an overview of the recent research of indicated online preventive interventions for youth (12-25 years) experiencing the early stages of mental health complaints with the aim of identifying the nature and extent of the research evidence. Methods: The 5-stage framework by Arksey and O'Malley was used. Academic literature published from 2013 onwards in printed or electronic format was included from Scopus, PsychINFO, and Ovid MEDLINE(R) ALL. Results: The search yielded 11,122 results, with the final selection resulting in inclusion of 30 articles for this review. In total, the articles included 4,950 participants. 26.7% of the selected articles focused on youth between 12 and 25 years. Of the articles 60% did not screen for, nor exclude participants with clinical levels of symptoms. Most studies used a common evidence-based therapy for the disorder-category targeted. More than half of the online interventions included some form of human support. Adherence levels ranged between 27.9 and 98%. The results indicate general effectiveness, usability and acceptability of online indicated preventive interventions. The most commonly used approach was CBT (n = 12 studies). Studies varied in their size, rigor of study, effectiveness and outcome measures. Online interventions with a combination of clinical and peer moderation (n = 3 studies) appear to result in the most stable and highest effect sizes. Conclusion: Online indicated preventive mental health

Tumours are abnormal growths of cells that reproduce by redirecting essential nutrients and resources from surrounding tissue. Changes to cell metabolism that trigger the growth of tumours are reflected in subtle differences between the chemical composition of healthy and malignant cells. We used LA-ICP-MS imaging to investigate whether these chemical differences can be used to spatially identify tumours and support detection of primary colorectal tumours in anatomical pathology. First, we generated quantitative LA-ICP-MS images of three colorectal surgical resections with case-matched normal intestinal wall tissue and used this data in a Monte Carlo optimisation experiment to develop an algorithm that can classify pixels as tumour positive or negative. Blinded testing and interrogation of LA-ICP-MS images with micrographs of haematoxylin and eosin stained and Ki67 immunolabelled sections revealed Monte Carlo optimisation accurately identified primary tumour cells, as well as returning false positive pixels in areas of high cell proliferation. We analysed an additional 11 surgical resections of primary colorectal tumours and re-developed our image processing method to include a random forest regression machine learning model to correctly identify heterogenous tumours and exclude false positive pixels in images of non-malignant tissue. Our final model used over 1.6 billion calculations to correctly discern healthy cells from various types and stages of invasive colorectal tumours. The imaging mass spectrometry and data analysis methods described, developed in partnership with clinical cancer researchers, have the potential to further support cancer detection as part of a comprehensive digital pathology approach to cancer care through validation of a new chemical biomarker of tumour cells.

Purpose: The eHealth self-management application ‘Oncokompas’ was developed to support cancer survivors in monitoring health-related quality of life (HRQOL) and symptoms, and obtaining personalized feedback and options for supportive care. The aim of this study was to assess the cost-utility of Oncokompas compared with care as usual (CAU) among cancer survivors. Methods: Survivors were randomly allocated to the intervention or control group. Direct (non-)medical, indirect non-medical costs, and HRQOL were measured at 3- and 6-month follow-up, using iMTA Medical Consumption and Productivity Costs and the EuroQol-5D questionnaires. Mean cumulative costs and quality-adjusted life-years (QALYs) were compared between both groups. Results: In total, 625 survivors were randomized into intervention (n = 320) or control group (n = 305). Base case analysis showed that incremental costs from a societal perspective were − €163 (95% CI, − 665 to 326), and incremental QALYs were 0.0017 (95% CI, − 0.0121 to 0.0155) in the intervention group compared with those in the control group. The probability that, compared with CAU, Oncokompas is more effective was 60%, less costly 73%, and both more effective and less costly 47%. Sensitivity analyses showed that incremental costs vary between − €40 and €69, and incremental QALYs vary between − 0.0023 and − 0.0057. Conclusion: Oncokompas is likely to be equally effective on utilities, and not more expensive than CAU, and will therefore contribute to sustainable cancer survivorship care in a (cost-)effective manner. Implications for Cancer Survivors: Oncokompas seems to improve HRQOL and reduces the burden of several tumour-specific symptoms, while costs from a societal perspective are similar to CAU.

Examining chemical and structural characteristics of micro-features in complex tissue matrices is essential for understanding biological systems. Advances in multimodal chemical and structural imaging using synchrotron radiation have overcome many issues in correlative imaging, enabling the characterization of distinct microfeatures at nanoscale resolution in ex vivo tissues. We present a nanoscale imaging method that pairs X-ray ptychography and X-ray fluorescence microscopy (XFM) to simultaneously examine structural features and quantify elemental content of microfeatures in complex ex vivo tissues. We examined the neuropathological microfeatures Lewy bodies, aggregations of superoxide dismutase 1 (SOD1) and neuromelanin in human post-mortem Parkinson's disease tissue. Although biometals play essential roles in normal neuronal biochemistry, their dyshomeostasis is implicated in Parkinson's disease aetiology. Here we show that Lewy bodies and SOD1 aggregates have distinct elemental fingerprints yet are similar in structure, whilst neuromelanin exhibits different elemental composition and a distinct, disordered structure. The unique approach we describe is applicable to the structural and chemical characterization of a wide range of complex biological tissues at previously unprecedented levels of detail.

Machine learning and data analysis are becoming an essential part of the decision-making process in modern organizations. Even though new and improved analytics algorithms are developed frequently, organizations are struggling to develop analytics applications that can stay up-To-date with changing business requirements and technology innovations The rapid development of ad-hoc programs to conduct machine learning tasks at hand has resulted in creating more expenses and efforts in the long term, a phenomenon referred to as technical debt in literature. This paper addresses the technical debt associated with data analytics applications by proposing a knowledge repository that captures analytics-related knowledge, which can be developed and maintained separately from the organization's IT infrastructure and used to design analytics applications with visual interfaces. This way, organizations can develop dynamic and adaptable analytics applications with easy-To-follow front-ends and can accommodate new data sources or machine learning models. We evaluate the proposed approach by conducting a case study that develops an application for the acquisition and management of high-frequency financial market data.

Structural and chemical characterisation of microfeatures in unadulterated Parkinson's disease brain tissue using synchrotron nanoscale XFM and ptychography.

Structural and chemical characterisation of microfeatures in unadulterated Parkinson's disease brain tissue using synchrotron nanoscale XFM and ptychography.

BACKGROUND:Recognition of pain in people with dementia is challenging. Observational scales have been developed, but there is a need to harmonize and improve the assessment process. In EU initiative COST-Action TD1005, 36 promising items were selected from existing scales to be tested further. We aimed to study the observer agreement of each item, and to analyse the factor structure of the complete set. METHODS:One hundred and ninety older persons with dementia were recruited in four different countries (Italy, Serbia, Spain and The Netherlands) from different types of healthcare facilities. Patients represented a convenience sample, with no pre-selection on presence of (suspected) pain. The Pain Assessment in Impaired Cognition (PAIC, research version) item pool includes facial expressions of pain (15 items), body movements (10 items) and vocalizations (11 items). Participants were observed by health professionals in two situations, at rest and during movement. Intrarater and interrater reliability was analysed by percentage agreement. The factor structure was examined with principal component analysis with orthogonal rotation. RESULTS:Health professionals performed observations in 40-57 patients in each country. Intrarater and interrater agreement was generally high (≥70%). However, for some facial expression items, agreement was sometimes below 70%. Factor analyses showed a six-component solution, which were named as follows: Vocal pain expression, Face anatomical descriptors, Protective body movements, Vocal defence, Tension and Lack of affect. CONCLUSIONS:Observation of PAIC items can be done reliably in healthcare settings. Observer agreement is quite promising already without extensive training. SIGNIFICANCE:In this international project, promising items from existing observational pain scales were identified and evaluated regarding their reliability as an alternative to pain self-report in people with dementia. Analysis on factor structure helped to understa

BACKGROUND:Recognition of pain in people with dementia is challenging. Observational scales have been developed, but there is a need to harmonize and improve the assessment process. In EU initiative COST-Action TD1005, 36 promising items were selected from existing scales to be tested further. We aimed to study the observer agreement of each item, and to analyse the factor structure of the complete set. METHODS:One hundred and ninety older persons with dementia were recruited in four different countries (Italy, Serbia, Spain and The Netherlands) from different types of healthcare facilities. Patients represented a convenience sample, with no pre-selection on presence of (suspected) pain. The Pain Assessment in Impaired Cognition (PAIC, research version) item pool includes facial expressions of pain (15 items), body movements (10 items) and vocalizations (11 items). Participants were observed by health professionals in two situations, at rest and during movement. Intrarater and interrater reliability was analysed by percentage agreement. The factor structure was examined with principal component analysis with orthogonal rotation. RESULTS:Health professionals performed observations in 40-57 patients in each country. Intrarater and interrater agreement was generally high (≥70%). However, for some facial expression items, agreement was sometimes below 70%. Factor analyses showed a six-component solution, which were named as follows: Vocal pain expression, Face anatomical descriptors, Protective body movements, Vocal defence, Tension and Lack of affect. CONCLUSIONS:Observation of PAIC items can be done reliably in healthcare settings. Observer agreement is quite promising already without extensive training. SIGNIFICANCE:In this international project, promising items from existing observational pain scales were identified and evaluated regarding their reliability as an alternative to pain self-report in people with dementia. Analysis on factor structure helped to understa

© 2019 The Royal Society of Chemistry. Biological X-ray fluorescence microscopy (XFM) is an important tool for determining quantitative distributions of bioinorganics and essential trace elements. Here we present a new analysis approach for rapid nanoscale ptychographic imaging and simultaneous chemical mapping of large radiation sensitive specimens without image degradation associated with probe evolution.

© 2019 The Royal Society of Chemistry. Biological X-ray fluorescence microscopy (XFM) is an important tool for determining quantitative distributions of bioinorganics and essential trace elements. Here we present a new analysis approach for rapid nanoscale ptychographic imaging and simultaneous chemical mapping of large radiation sensitive specimens without image degradation associated with probe evolution.

Al71.25Cu16.15Mg7.6Ni5 amorphous alloy ribbons were prepared using melt-spinning technique. The structure related to the phase transformation of the amorphous alloy ribbon was studied by X-ray diffraction and differential scanning calorimetry (DSC). It was found that the ribbon exhibits two separate diffuse shoulders with peak positions. Formation of cubic-Al was observed when the ribbon was heated beyond the primary crystallization peak. Upon secondary crystallization peak, cubic-Al, Al3Ni and Al2CuMg were observed. The evolution of the lattice constant, a, for the cubic-Al and Al2CuMg, were determined by the Rietveld procedure. The lattice constant for cubic-Al remains almost constant between the primary and fully crystallization temperature after sudden change at secondary crystallization temperature. The lattice constant for Al2CuMg increases when the temperature increases. Both crystallite size of cubic-Al and Al2CuMg were increased as temperature increases.

BACKGROUND: Many persons with stroke experience physical, cognitive, and emotional problems that contribute to restrictions in social participation. There is, however, a lack of knowledge on the long-term course of participation over time post-stroke. OBJECTIVE: To describe the time course of participation up to 2 years post-stroke and to identify which demographic and stroke-related factors are associated with this time course. METHODS: This was a multicenter, prospective cohort study following 390 persons with stroke from hospital admission up to 2 years (at 2, 6, 12, and 24 months). Multilevel modeling with linear and quadratic time effects was used to examine the course of the frequency of vocational and social/leisure activities, experienced restrictions, and satisfaction with participation. RESULTS: The frequency of vocational activities increased up to 1 year post-stroke and leveled off thereafter. Older and lower-educated persons showed less favorable courses of participation than younger and higher-educated persons, respectively. The frequency of social/leisure activities decreased post-stroke. Participation restrictions declined up to 1 year post-stroke and leveled off thereafter. Persons dependent in activities of daily living (ADL) kept experiencing more restrictions throughout time than independent persons. Satisfaction with participation increased slightly over time. CONCLUSIONS: Changes in participation occurred mostly in the first year post-stroke. Particularly older and lower-educated persons, and those dependent in ADL showed less favorable courses of participation up to 2 years post-stroke. Clinicians can apply these findings in identifying persons most at risk of long-term unfavorable participation outcome and, thus, target rehabilitation programs accordingly.

© The Author(s) 2016. Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human PhenotypeOntology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.