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ObjectivesGeneral practitioners (GPs) play a key role in heart failure (HF) management. Despite multiple guidelines, the management of patients with HF in primary care is suboptimal. Therefore, all the qualitative evidence concerning GPs’ perceptions of managing HF in primary care was synthesised to identify barriers and facilitators for optimal care, and ideas for improvement.DesignQualitative evidence synthesis.MethodsSearches of MEDLINE, EMBASE, Web of Science and CINAHL databases up to 20/12/2015 were conducted. The Critical Appraisal Skills Programme's checklist for qualitative research was used for quality assessment. Thematic analysis was used as method of analysis.ResultsOf 5427 articles, 18 qualitative articles were included. Findings were organised in HF-specific factors, patient factors, physician factors and contextual factors. GPs’ uncertainty in all areas of HF management was highlighted. HF management started with an uncertain diagnosis, leading to difficulties with communication, treatment and advance care planning. Lack of access to specialised care and lack of knowledge were identified as important contributors to this uncertainty. In an effort to overcome this, strategies bringing evidence into practice should be promoted. GPs expressed the need for a multidisciplinary chronic care approach for HF. However, mixed experiences were noted with regard to interprofessional collaboration.ConclusionsThe main challenges identified in this synthesis were how to deal with GPs’ uncertainty about clinical practice, how to bring evidence into practice and how to work together as a multiprofessional team. These barriers were situated predominantly on the physician and contextual level. Targets to improve GPs’ HF care were identified.

The pediatric extra-cranial tumor neuroblastoma displays a low mutational burden while recurrent copy number alterations are present in most high-risk cases. Here, we identify SOX11 as a dependency transcription factor in adrenergic neuroblastoma based on recurrent chromosome 2p focal gains and amplifications, specific expression in the normal sympatho-adrenal lineage and adrenergic neuroblastoma, regulation by multiple adrenergic specific (super-)enhancers and strong dependency on high SOX11 expression in adrenergic neuroblastomas. SOX11 regulated direct targets include genes implicated in epigenetic control, cytoskeleton and neurodevelopment. Most notably, SOX11 controls chromatin regulatory complexes, including 10 SWI/SNF core components among which SMARCC1, SMARCA4/BRG1 and ARID1A . Additionally, the histone deacetylase HDAC2 , PRC1 complex component CBX2 , chromatin-modifying enzyme KDM1A/LSD1 and pioneer factor c-MYB are regulated by SOX11. Finally, SOX11 is identified as a core transcription factor of the core regulatory circuitry (CRC) in adrenergic high-risk neuroblastoma with a potential role as epigenetic master regulator upstream of the CRC. The development of neuroblastoma (NB) is regulated by multiple core transcription factors. Here, SOX11 is identified as a potential epigenetic master regulator upstream of the core regulatory circuitry in adrenergic high-risk neuroblastoma.

ObjectiveTo investigate the abundance and the prevalence of Dysosmobacter welbionis J115T, a novel butyrate-producing bacterium isolated from the human gut both in the general population and in subjects with metabolic syndrome. To study the impact of this bacterium on host metabolism using diet-induced obese and diabetic mice.DesignWe analysed the presence and abundance of the bacterium in 11 984 subjects using four human cohorts (ie, Human Microbiome Project, American Gut Project, Flemish Gut Flora Project and Microbes4U). Then, we tested the effects of daily oral gavages with live D. welbionis J115T on metabolism and several hallmarks of obesity, diabetes, inflammation and lipid metabolism in obese/diabetic mice.ResultsThis newly identified bacterium was detected in 62.7%–69.8% of the healthy population. Strikingly, in obese humans with a metabolic syndrome, the abundance of Dysosmobacter genus correlates negatively with body mass index, fasting glucose and glycated haemoglobin. In mice, supplementation with live D. welbionis J115T, but not with the pasteurised bacteria, partially counteracted diet-induced obesity development, fat mass gain, insulin resistance and white adipose tissue hypertrophy and inflammation. In addition, live D. welbionis J115T administration protected the mice from brown adipose tissue inflammation in association with increased mitochondria number and non-shivering thermogenesis. These effects occurred with minor impact on the mouse intestinal microbiota composition.ConclusionsThese results suggest that D. welbionis J115T directly and beneficially influences host metabolism and is a strong candidate for the development of next-generation beneficial bacteria targeting obesity and associated metabolic diseases.

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson’s disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug–gene interactions. We performed automated ML on multimodal data from the Parkinson’s progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson’s Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available.

Globally, health systems have been struggling to cope with the increasing burden of chronic diseases and respond to associated patient needs. Integrated care (IC) for chronic diseases offers solutions, but implementing these new models requires multi-stakeholder action and integrated policies to address social, organisational, and financial barriers. Policy implementation for IC has been little studied, especially through a political lens. This paper examines how IC policies in Belgium were developed over the last decade and how stakeholders have played a role in these policies. We used a case study design. After an exploratory document review, we selected three IC policies. We then interviewed 25 key stakeholders in the field of IC. The stakeholder analysis entailed a detailed mapping of the stakeholders' power, position, and interest related to the three selected policies. Interview participants included policy-makers, civil servants (from ministry of health and health insurance), representatives of health professionals' associations, academics, and patient organisations. Additionally, a processual analysis of IC policy processes (2007-2020) through literature review was used to frame the interviews by means of a chronic care policy timeline. In Belgium, a variety of policy initiatives have been developed in recent years both at central and decentralised levels. The power analysis and policy position maps exposed tensions between federal and federated governments in terms of overlapping competence, as well as the implications of the power shift from federal to federated levels as a consequence of the 2014 state reform. The 2014 partial decentralisation of healthcare has created fragmentation of decisive power which undermines efforts towards IC. This political trend towards fragmentation is at odds with the need for IC. Further research is needed on how public health policy competences and reform durability of IC policies will evolve.

Respiratory syncytial virus is the major cause of acute lower respiratory tract infections in young children, causing extensive mortality and morbidity globally, with limited therapeutic or preventative options. Cathelicidins are innate immune antimicrobial host defence peptides and have antiviral activity against RSV. However, upper respiratory tract cathelicidin expression and the relationship with host and environment factors in early life, are unknown. Infant cohorts were analysed to characterise early life nasal cathelicidin levels, revealing low expression levels in the first week of life, with increased levels at 9 months which are comparable to 2-year-olds and healthy adults. No impact of prematurity on nasal cathelicidin expression was observed, nor were there effects of sex or birth mode, however, nasal cathelicidin expression was lower in the first week-of-life in winter births. Nasal cathelicidin levels were positively associated with specific inflammatory markers and demonstrated to be associated with microbial community composition. Importantly, levels of nasal cathelicidin expression were elevated in infants with mild RSV infection, but, in contrast, were not upregulated in infants hospitalised with severe RSV infection. These data suggest important relationships between nasal cathelicidin, upper airway microbiota, inflammation, and immunity against RSV infection, with interventional potential.

Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows. Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. This motivates research into methods that require fewer resources to achieve similar results. This survey synthesizes and relates current methods and findings in efficient NLP. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods.

Unequal access to health promotion resources and early prevention services is a major determinant of health inequity among youth. Initiatives that improve the access to and adoption of health promotion messages are important undertakings, e.g., sport. Sport-for-development (SFD) programs are seen as valuable delivery tools, in which coaches are used as change agents to increase health awareness and behavior among at-risk youth. The delivery of such messages requires specific knowledge and skills that can be attained through training; however, the effectiveness of such training requires assessment. In this study, we evaluated the feasibility of such a training program for SFD coaches using process evaluation from a realist perspective, and views from multiple stakeholders, among other sources. We also clarified the inner workings of the training and investigated how context shaped the training outcomes. Increased health awareness and a sense of responsibility from acting as a role model for at-risk youth were among the perceived training outcomes. Building a safe environment for learning, engagement, and bonds of trust increased the confidence to learn, and resulted in a sense of critical self-reflection and self-development of SFD coaches towards health and prevention messages. Importantly, the unique situations (or context) of SFD coaches and SFD in general presented challenging variables, e.g., a precarious life history or living conditions, mental health issues, or low educational skills, that hampered the impact of the mechanisms put in place by the training. Here, we present a process in which the development of the 'right mind-set,' engagement and bonds of trust, in combination with the right settings are key elements for SFD coaches to learn how to convey health-promoting messages and take responsibility as role models for at-risk youth.

The role of the maintenance operator is evolving with Industry 4.0, leveraging advanced technologies to enhance physical, sensory, and cognitive capabilities. Efficient troubleshooting support is critical to reducing operators' physical and mental stress in high-demand maintenance environments. Augmented reality (AR) presents a powerful solution by integrating real-time data visualisation, contextualised fault diagnostics, and interactive guidance into the troubleshooting workflow. However, existing research lacks structured approaches for AR-assisted troubleshooting that enable operators to rapidly detect, analyse, and resolve system failures. This research investigates the essential artificial intelligence (AI) and AR functionalities needed for effective troubleshooting. It examines how AR can enhance troubleshooting by seamlessly integrating real-time data, adaptive user interfaces (UI), and operator-driven insights. Findings from a railway case study indicate that AR-assisted troubleshooting improves decision-making, reduces task errors, facilitates knowledge capture, and supports both novice and experienced operators in structuring and contextualising system data. Iterative prototype development highlighted the importance of clear visualisation, guided autonomy, contextual information, and operator feedback. Future research will explore adaptive interfaces, real-time feedback loops, and broader deployment to further optimise AR-supported maintenance workflows.

Gender has been proposed to impact the phenotype and prognosis of hypertrophic cardiomyopathy (HC). Our aims were to study gender differences in the clinical presentation, phenotype, genotype, and outcome of HC. This retrospective single-center cohort study included 1,007 patients with HC (62% male, 80% genotyped) evaluated between 1977 and 2017. Hazard ratios (HR) were calculated using multivariable Cox proportional hazard regression models. At first evaluation, female patients presented more often with symptoms (43% vs 35%, p = 0.01), were older than male patients (56 ± 16 vs 49 ± 15 years, p <0.001), and more frequently had hypertension (38% vs 27%, p <0.001), left ventricular outflow tract obstruction (37% vs 27%, p <0.001), and impaired left ventricular systolic (17% vs 11%, p = 0.01) and diastolic (77% vs 62%, p <0.001) function. Overall, the genetic yield was similar between genders (54% vs 51%, p = 0.4); however, in patients ≥70 years, the genetic yield was less in women (15% vs 36%, p = 0.03). During 6.8-year follow-up (interquartile range 3.2 to 10.9), female gender was not independently associated with all-cause mortality (HR 1.25 [0.91 to 1.73]), cardiovascular mortality (HR 1.22 [0.83 to 1.79]), heart failure-related mortality (HR 1.77 [0.95 to 3.27]), or sudden cardiac death (SCD) and/or aborted SCD (HR 0.75 [0.44 to 1.30]). Interventions and nonfatal clinical events did not differ between the genders. In conclusion, female patients with HC present at a more advanced age with a different clinical, phenotypic, and genetic status. There is no independent association between female gender and all-cause mortality, cardiovascular mortality, heart failure-related mortality, or SCD.