Although bipolar disorder (BD) has been understood classically as a cyclic disease with full recovery between manic and depressive mood episodes, the long-term outcome has been associated with cognitive deficits, impaired psychosocial functioning, and premature death.1 Due to ageing of the population the absolute number of older persons with BD will rise in the next decades1 with substantial burden for their caregivers. 2. Acknowledging that recovery is defined more broadly than the absence of mood symptoms,3 insights regarding perspectives of recovery and expectations of mental health care (MHC) are urgently warranted to meet the needs of this growing complex patient group.
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Bipolar disorder is a severe mental illness with serious consequences for daily living of patients and their caregivers. Care as usual primarily consists of pharmacotherapy and supportive treatment. However, a substantial number of patients show a suboptimal response to treatment and still suffer from frequent episodes, persistent interepisodic symptoms and poor social functioning. Both psychiatric and somatic comorbid disorders are frequent, especially personality disorders, substance abuse, cardiovascular diseases and diabetes. Multidisciplinary collaboration of professionals is needed to combine all expertise in order to achieve high-quality integrated treatment. 'Collaborative Care' is a treatment method that could meet these needs. Several studies have shown promising effects of these integrated treatment programs for patients with bipolar disorder. In this article we describe a research protocol concerning a study on the effects of Collaborative Care for patients with bipolar disorder in the Netherlands.
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Accessible Summary What is known on the subject? • Mentalizing is the capacity to understand both one‘s own and other people‘s behaviour in terms of mental states, such as, for example, desires, feelings and beliefs. • The mentalizing capacities of healthcare professionals help to establish effective therapeutic relationships and, in turn, lead to better patient outcomes. What this paper adds to existing knowledge? • The personal factors positively associated with the mentalizing capacities of healthcare professionals are being female, greater work experience and having a more secure attachment style. Psychosocial factors are having personal experience with psychotherapy, burnout, and in the case of female students, being able to identify with the female psychotherapist role model during training. There is limited evidence that training programmes can improve mentalizing capacities. • Although the mentalization field is gaining importance and research is expanding, the implications for mental health nursing have not been previously reviewed. Mental health nurses are underrepresented in research on the mentalizing capacities of healthcare professionals. This is significant given that mental health nurses work closest to patients and thus are more often confronted with patients‘ behaviour compared to other health care professionals, and constitute a large part of the workforce in mental healthcare for patients with mental illness. What are the implications for practice? • Given the importance of mentalizing capacity of both the patient and the nurse for a constructive working relationship, it is important that mental health nurses are trained in the basic principles of mentalization. Mental health nurses should be able to recognize situations where patients‘ lack of ability to mentalize creates difficulties in the interaction. They should also be able to recognize their own difficulties with mentalizing and be sensitive to the communicative implications this may have.
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Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
-Chatbots are being used at an increasing rate, for instance, for simple Q&A conversations, flight reservations, online shopping and news aggregation. However, users expect to be served as effective and reliable as they were with human-based systems and are unforgiving once the system fails to understand them, engage them or show them human empathy. This problem is more prominent when the technology is used in domains such as health care, where empathy and the ability to give emotional support are most essential during interaction with the person. Empathy, however, is a unique human skill, and conversational agents such as chatbots cannot yet express empathy in nuanced ways to account for its complex nature and quality. This project focuses on designing emotionally supportive conversational agents within the mental health domain. We take a user-centered co-creation approach to focus on the mental health problems of sexual assault victims. This group is chosen specifically, because of the high rate of the sexual assault incidents and its lifetime destructive effects on the victim and the fact that although early intervention and treatment is necessary to prevent future mental health problems, these incidents largely go unreported due to the stigma attached to sexual assault. On the other hand, research shows that people feel more comfortable talking to chatbots about intimate topics since they feel no fear of judgment. We think an emotionally supportive and empathic chatbot specifically designed to encourage self-disclosure among sexual assault victims could help those who remain silent in fear of negative evaluation and empower them to process their experience better and take the necessary steps towards treatment early on.
Structural colour (SC) is created by light interacting with regular nanostructures in angle-dependent ways resulting in vivid hues. This form of intense colouration offers commercial and industrial benefits over dyes and other pigments. Advantages include durability, efficient use of light, anti-fade properties and the potential to be created from low cost materials (e.g. cellulose fibres). SC is widely found in nature, examples include butterflies, squid, beetles, plants and even bacteria. Flavobacterium IR1 is a Gram-negative, gliding bacterium isolated from Rotterdam harbour. IR1 is able to rapidly self-assemble into a 2D photonic crystal (a form of SC) on hydrated surfaces. Colonies of IR1 are able to display intense, angle-dependent colours when illuminated with white light. The process of assembly from a disordered structure to intense hues, that reflect the ordering of the cells, is possible within 10-20 minutes. This bacterium can be stored long-term by freeze drying and then rapidly activated by hydration. We see these properties as suiting a cellular reporter system quite distinct from those on the market, SC is intended to be “the new Green Fluorescent Protein”. The ability to understand the genomics and genetics of SC is the unique selling point to be exploited in product development. We propose exploiting SC in IR1 to create microbial biosensors to detect, in the first instance, volatile compounds that are damaging to health and the environment over the long term. Examples include petroleum or plastic derivatives that cause cancer, birth defects and allergies, indicate explosives or other insidious hazards. Hoekmine, working with staff and students within the Hogeschool Utrecht and iLab, has developed the tools to do these tasks. We intend to create a freeze-dried disposable product (disposables) that, when rehydrated, allow IR1 strains to sense and report multiple hazardous vapours alerting industries and individuals to threats. The data, visible as brightly coloured patches of bacteria, will be captured and quantified by mobile phone creating a system that can be used in any location by any user without prior training. Access to advice, assay results and other information will be via a custom designed APP. This work will be performed in parallel with the creation of a business plan and market/IP investigation to prepare the ground for seed investment. The vision is to make a widely usable series of tests to allow robust environmental monitoring for all to improve the quality of life. In the future, this technology will be applied to other areas of diagnostics.
