The significance of the sense of coherence for various coping resources in stress situations used by police officers in on-the-beat service

Background: Police officers meet many stressors as part of their occupation. The psychological resource "sense of coherence" (SOC) protects against ill-health, but its impact on coping resources for stress situations has not been studied in the population of police officers. Different approaches to investigate the significance of SOC for different outcomes have been identified in literature, leading to some difficulties in the interpretation and generalization of results. The aim was therefore to explore SOC and the coping resources, and to examine the significance of SOC for various coping resources for stress using different models in a sample of Swedish police officers providing on-the-beat service. Materials and Methods: One hundred and one police officers (age: mean = 33 years, SD = 8; 29 females) were included, and the Orientation to Life Questionnaire (SOC-29) and the Coping Resources Inventory (CRI) were used. The dependent variable in each regression analysis was one of the coping resources: cognitive, social, emotional, spiritual/philosophical, physical, and a global resource. Global SOC-29 and/or its components (comprehensibility, manageability, and meaningfulness) were investigated as independent variables. Results: All CRI and SOC-29 scores except for that of spiritual/philosophical resources were higher than those of reference groups. Manageability was the most important component of SOC for various coping resources in stress situations used by police officers. Conclusion: A deeper study of manageability will give useful information, because this component of SOC is particularly significant in the variation in resources used by police officers to cope with stress. Salutogenesis, the origin of well-being, should be more in focus of future research on workplaces with a high level of occupational stress

Health service utilization patterns of primary care patients with osteoarthritis

Contains fulltext : 53455.pdf ( ) (Open Access)BACKGROUND: To assess factors associated with visits to GPs, orthopaedists, and non-physician practitioners of complementary medicine (alternative practitioners) by primary care patients with osteoarthritis (OA). METHODS: Cross-sectional survey among 1250 consecutively addressed patients from 75 primary care practices in Germany. All patients suffered from OA of the knee or hip according to ACR criteria. They received questionnaires collecting sociodemographic data, data about health service utilisation, prescriptions, comorbidities. They also included established instruments as the Arthritis Impact Measurement Scale (AIMS2-SF) to assess disease-specific quality of life and the Patient Health Questionnaire (PHQ-9) to assess depression. Hierarchical stepwise multiple linear regression models were used to reveal significant factors influencing health service utilization. RESULTS: 1021 of 1250 (81.6%) questionnaires were returned. Nonrespondents did not differ from participants. Factors associated with health service use (HSU) varied between providers of care. Not being in a partnership, achieving a high score on the PHQ-9, increased pain severity reflected in the "symptom" scale of the AIMS2-SF, and an increased number of drug prescriptions predicted a high frequency of GP visits. The PHQ-9 score was also a predictor for visits to orthopaedists, as were previous GP contacts, a high score in the "symptom" scale as well as a high score in the "lower limb scale" of the AIMS2-SF. Regarding visits to alternative practitioners, a high score in the AIMS -"social" scale was a positive predictor as older people were less likely to visit them. CONCLUSION: Our results emphasize the need for awareness of psychological factors contributing to the use of health care providers. Addressing the revealed factors associated with HSU appropriately may lead to decreased health care utilization. But further research is needed to assess how this can be done successfully

Machines vs. Ensembles: Effective MAPK Signaling through Heterogeneous Sets of Protein Complexes

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author’s publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively will ultimately shape how we conceptualize the function, evolution and engineering of signaling networks

Multi-state Modeling of Biomolecules

Multi-state modeling of biomolecules refers to a series of techniques used to represent and compute the behavior of biological molecules or complexes that can adopt a large number of possible functional states. Biological signaling systems often rely on complexes of biological macromolecules that can undergo several functionally significant modifications that are mutually compatible. Thus, they can exist in a very large number of functionally different states. Modeling such multi-state systems poses two problems: the problem of how to describe and specify a multi-state system (the “specification problem”) and the problem of how to use a computer to simulate the progress of the system over time (the “computation problem”). To address the specification problem, modelers have in recent years moved away from explicit specification of all possible states and towards rule-based formalisms that allow for implicit model specification, including the κ-calculus [1], BioNetGen [2]–[5], the Allosteric Network Compiler [6], and others [7], [8]. To tackle the computation problem, they have turned to particle-based methods that have in many cases proved more computationally efficient than population-based methods based on ordinary differential equations, partial differential equations, or the Gillespie stochastic simulation algorithm [9], [10]. Given current computing technology, particle-based methods are sometimes the only possible option. Particle-based simulators fall into two further categories: nonspatial simulators, such as StochSim [11], DYNSTOC [12], RuleMonkey [9], [13], and the Network-Free Stochastic Simulator (NFSim) [14], and spatial simulators, including Meredys [15], SRSim [16], [17], and MCell [18]–[20]. Modelers can thus choose from a variety of tools, the best choice depending on the particular problem. Development of faster and more powerful methods is ongoing, promising the ability to simulate ever more complex signaling processes in the future

Unstable neurons underlie a stable learned behavior

Motor skills can be maintained for decades, but the biological basis of this memory persistence remains largely unknown. The zebra finch, for example, sings a highly stereotyped song that is stable for years, but it is not known whether the precise neural patterns underlying song are stable or shift from day to day. Here we demonstrate that the population of projection neurons coding for song in the premotor nucleus, HVC, change from day to day. The most dramatic shifts occur over intervals of sleep. In contrast to the transient participation of excitatory neurons, ensemble measurements dominated by inhibition persist unchanged even after damage to downstream motor nerves. These observations offer a principle of motor stability: spatiotemporal patterns of inhibition can maintain a stable scaffold for motor dynamics while the population of principal neurons that directly drive behavior shift from one day to the next