Stretch-Induced Down-Regulation of HCN2 Suppresses Contractile Activity.

Although hyperpolarization-activated and cyclic nucleotide-gated 2 channels (HCN2) are expressed in multiple cell types in the gut, the role of HCN2 in intestinal motility is poorly understood. HCN2 is down-regulated in intestinal smooth muscle in a rodent model of ileus. Thus, the purpose of this study was to determine the effects of HCN inhibition on intestinal motility. HCN inhibition with ZD7288 or zatebradine significantly suppressed both spontaneous and agonist-induced contractile activity in the small intestine in a dose-dependent and tetrodotoxin-independent manner. HCN inhibition significantly suppressed intestinal tone but not contractile amplitude. The calcium sensitivity of contractile activity was significantly suppressed by HCN inhibition. Inflammatory mediators did not affect the suppression of intestinal contractile activity by HCN inhibition but increased stretch of the intestinal tissue partially attenuated the effects of HCN inhibition on agonist-induced intestinal contractile activity. HCN2 protein and mRNA levels in intestinal smooth muscle tissue were significantly down-regulated by increased mechanical stretch compared to unstretched tissue. Increased cyclical stretch down-regulated HCN2 protein and mRNA levels in primary human intestinal smooth muscle cells and macrophages. Overall, our results suggest that decreased HCN2 expression induced by mechanical signals, such as intestinal wall distension or edema development, may contribute to the development of ileus.

[A study of the psychosocial characteristics of psychiatric teams in Hungary].

BACKGROUND: In this study, we examined psychiatric teams in a Hungarian sample before the COVID-19 pandemic. The aim of our research is to examine Hungarian psychiatric teams along different workplace psychosocial factors (collaboration management, organization and job, workplace requirements, work-life balance, trust, health and well-being) in order to assess them in terms of hierarchy and competence. Moreover we examine the mental and somatic effects of these factors. METHODS: We conducted a cross-sectional survey of Hungarian mental health care workers (N =79). The main question of the study was how perceived hierarchy and competence boundary violations are associated to psychosocial factors of the COPSOQ-II questionnaire (Copenhagen Psychosocial Questionnaire II) in general, and among employees in different job groups. RESULTS: Based on bootstrapped binary logistic regression performed on the full sample, we found that perceived hierarchy is associated with control, social support of co-workers and stress, while violation of competence boundaries is associated with job recognition, job clarity and fairness. DISCUSSION: Our results show that where mental health care workers experienced less perceived hierarchy within the team, they were more likely to feel that they had control over their work. Furthermore, in hierarchies perceived as superior-subordinate relations, the quality of the relationships within the team seems to be much more important. CONCLUSIONS: This research can be the starting point for a comprehensive study of other psychiatric teams with the aid of a widely used measure, providing an opportunity for comparison with other health teams at an international level.

How to Use Model-Based Cluster Analysis Efficiently in Person-Oriented Research.

Model-based cluster analysis (MBCA) was created to automatize the often subjective model-selection procedure of traditional explorative clustering methods. It is a type of finite mixture modelling, assuming that the data come from a mixture of different subpopulations following given distributions, typically multivariate normal. In that case cluster analysis is the exploration of the underlying mixture structure. In MBCA finding the possible number of clusters and the best clustering model is a statistical model-selection problem, where the models with differing number and type of component distributions are compared. For fitting a certain model MBCA uses a likelihood based Bayesian Information Criterion (BIC) to evaluate its appropriateness and the model with the highest BIC value is accepted as the final solution. The aim of the present study is to investigate the adequacy of automatic model selection in MBCA using BIC, and suggested alternative methods, like the Integrated Completed Likelihood Criterion (ICL), or Baudry's method. An additional aim is to refine these procedures by using so called quality coefficients (QCs), borrowed from methodological advances within the field of exploratory cluster analysis, to help in the choice of an appropriate cluster structure (CLS), and also to compare the efficiency of MBCA in identifying a theoretical CLS with those of various other clustering methods. The analyses are restricted to studying the performance of various procedures of the type described above for two classification situations, typical in person-oriented studies: (1) an example data set characterized by a perfect theoretical CLS with seven types (seven completely homogeneous clusters) was used to generate three data sets with varying degrees of measurement error added to the original values, and (2) three additional data sets based on another perfect theoretical CLS with four types. It was found that the automatic decision rarely led to an optimal solution. However, dropping solutions with irregular BIC curves, and using different QCs as an aid in choosing between different solutions generated by MBCA and by fusing close clusters, optimal solutions were achieved for the two classification situations studied. With this refined procedure the revealed cluster solutions of MBCA often proved to be at least as good as those of different hierarchical and k-center clustering methods. MBCA was definitely superior in identifying four-type CLS models. In identifying seven-type CLS models MBCA performed at a similar level as the best of other clustering methods (such as k-means) only when the reliability level of the input variables was high or moderate, otherwise it was slightly less efficient.

Digital health is a cultural transformation of traditional healthcare.

Under the term "digital health", advanced medical technologies, disruptive innovations and digital communication have gradually become inseparable from providing best practice healthcare. While the cost of treating chronic conditions is increasing and doctor shortages are imminent worldwide, the needed transformation in the structure of healthcare and medicine fails to catch up with the rapid progress of the medical technology industry. This transition is slowed down by strict regulations; the reluctance of stakeholders in healthcare to change; and ignoring the importance of cultural changes and the human factor in an increasingly technological world. With access and adoption of technology getting higher, the risk of patients primarily turning to an accessible, but unregulated technological solution for their health problem is likely to increase. In this paper, we discuss how the old paradigm of the paternalistic model of medicine is transforming into an equal level partnership between patients and professionals and how it is aided and augmented by disruptive technologies. We attempt to define what digital health means and how it affects the status quo of care and also the study design in implementing technological innovations into the practice of medicine.