Age, physical inactivity, obesity, health conditions, and health-related quality of life among patients receiving conservative management for musculoskeletal disorders

Background Musculoskeletal conditions and insufficient physical activity have substantial personal and economic costs among contemporary aging societies. This study examined the age distribution, comorbid health conditions, body mass index (BMI), self-reported physical activity levels, and health-related quality of life of patients accessing ambulatory hospital clinics for musculoskeletal disorders. The study also investigated whether comorbidity, BMI, and self-reported physical activity were associated with patients’ health-related quality of life after adjusting for age as a potential confounder. Methods A cross-sectional survey was undertaken in three ambulatory hospital clinics for musculoskeletal disorders. Participants (n=224) reported their reason for referral, age, comorbid health conditions, BMI, physical activity levels (Active Australia Survey), and health-related quality of life (EQ-5D). Descriptive statistics and linear modeling were used to examine the associations between age, comorbidity, BMI, intensity and duration of physical activity, and health-related quality of life. Results The majority of patients (n=115, 51.3%) reported two or more comorbidities. In addition to other musculoskeletal conditions, common comorbidities included depression (n=41, 18.3%), hypertension (n=40, 17.9%), and diabetes (n=39, 17.4%). Approximately one-half of participants (n=110, 49.1%) self-reported insufficient physical activity to meet minimum recommended guidelines and 150 (67.0%) were overweight (n=56, 23.2%), obese (n=64, 28.6%), severely obese (n=16, 7.1%), or very severely obese (n=14, 6.3%), with a higher proportion of older patients affected. A generalized linear model indicated that, after adjusting for age, self-reported physical activity was positively associated (z=4.22, P<0.001), and comorbidities were negatively associated (z=-2.67, P<0.01) with patients’ health-related quality of life. Conclusion Older patients were more frequently affected by undesirable clinical attributes of comorbidity, obesity, and physical inactivity. However, findings from this investigation are compelling for the care of patients of all ages. Potential integration of physical activity behavior change or other effective lifestyle interventions into models of care for patients with musculoskeletal disorders is worthy of further investigation.

A non-linear interface element model for thin layer high adhesive mortared masonry

A nonlinear interface element modelling method is formulated for the prediction of deformation and failure of high adhesive thin layer polymer mortared masonry exhibiting failure of units and mortar. Plastic flow vectors are explicitly integrated within the implicit finite element framework instead of relying on predictor–corrector like approaches. The method is calibrated using experimental data from uniaxial compression, shear triplet and flexural beam tests. The model is validated using a thin layer mortared masonry shear wall, whose experimental datasets are reported in the literature and is used to examine the behaviour of thin layer mortared masonry under biaxial loading.

Estrogen deficiency-associated bone loss in the maxilla: A methodology to quantify the changes in the maxillary intra-radicular alveolar bone in an ovariectomized rat osteoporosis model

The effects of estrogen deficiency on bone characteristics are site-dependent, with the most commonly studied sites being appendicular long bones (proximal femur and tibia) and axial bones (vertebra). The effect on the maxillary and mandibular bones is still inconsistent and requires further investigation. This study was designed to evaluate bone quality in the posterior maxilla of ovariectomized rats in order to validate this site as an appropriate model to study the effect of osteoporotic changes. Methods: Forty-eight 3-month-old female Sprague-Dawley rats were randomly divided into two groups: an ovariectomized group (OVX, n=24) and Sham-operated group (SHAM, n=24). Six rats were randomly sacrificed from both groups at time points 8, 12, 16 and 20 weeks. The samples from tibia and maxilla were collected for Micro CT and histological analysis. For the maxilla, the volume of interest (VOI) area focused on the furcation areas of the first and second molar. Trabecular bone volume fraction (BV/TV, %), trabecular thickness (Tb.Th.), trabecular number (Tb.N.), trabecular separation (Tb.Sp.), and connectivity density (Conn.Dens) were analysed after Micro CT scanning. Results: At 8 weeks the indices BV/TV, Tb.Sp, Tb.N and Conn.Dens showed significant differences (P<0.05) between the OVX and SHAM groups in the tibia. Compared with the tibia, the maxilla developed osteoporosis at a later stage, with significant changes in maxillary bone density only occurring after 12 weeks. Compared with the SHAM group, both the first and second molars of the OVX group showed significantly decreased BV/TV values from 12 weeks, and these changes were sustained through 16 and 20 weeks. For Tb.Sp, there were significant increases in bone values for the OVX group compared with the SHAM group at 12, 16 and 20 weeks. Histological changes were highly consistent with Micro CT results. Conclusion: This study established a method to quantify the changes of intra-radicular alveolar bone in the posterior maxilla in an accepted rat osteoporosis model. The degree of the osteoporotic changes to trabecular bone architecture is site-dependent and at least 3 months are required for the osteoporotic effects to be apparent in the posterior maxilla following rat OVX.

A national satellite-based land-use regression model for air pollution exposure assessment in Australia

Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006–2011. We are making our model predictions freely available for research.

Pharmaceutical company influences on medication prescribing and their potential impact on quality use of medicines

Background Pharmaceuticals are big business, reporting strong market growth year after year. The ‘gatekeepers’ of this market are prescribers of medicines, who are the major target of pharmaceutical companies, utilizing direct and indirect influences. Methods This paper draws on previous research investigating pharmaceutical company prescribing influences to develop a qualitative model demonstrating the synergism between commercial influences on prescribing. The generic model was used to explore a realistic but hypothetical scenario to ascertain the applicability of the model. Results and Discussion A generic influence model was developed. The model was readily able to be adapted to reflect a realistic practice scenario. Conclusion Prescriber awareness of the linkages between various seemingly separate marketing techniques could potentially improve medicines usage in an evidence-based practice paradigm.

Intravenous injection of leconotide, an omega conotoxin : synergistic antihyperalgesic effects with morphine in a rat model of bone cancer pain

Objective.  Leconotide (CVID, AM336, CNSB004) is an omega conopeptide similar to ziconotide, which blocks voltage sensitive calcium channels. However, unlike ziconotide, which must be administered intrathecally, leconotide can be given intravenously because it is less toxic. This study investigated the antihyperalgesic potency of leconotide given intravenously alone and in combinations with morphine-administered intraperitoneally, in a rat model of bone cancer pain. Design.  Syngeneic rat prostate cancer cells AT3B-1 were injected into one tibia of male Wistar rats. The tumor expanded within the bone causing hyperalgesia to heat applied to the ipsilateral hind paw. Measurements were made of the maximum dose (MD) of morphine and leconotide given alone and in combinations that caused no effect in an open-field activity monitor, rotarod, and blood pressure and heart rate measurements. Paw withdrawal thresholds from noxious heat were measured. Dose response curves for morphine (0.312–5.0 mg/kg intraperitoneal) and leconotide (0.002–200 µg/kg intravenous) given alone were plotted and responses compared with those caused by morphine and leconotide in combinations. Results.  Leconotide caused minimal antihyperalgesic effects when administered alone. Morphine given alone intraperitoneally caused dose-related antihyperalgesic effects (ED50 = 2.40 ± 1.24 mg/kg), which were increased by coadministration of leconotide 20 µg/kg (morphine ED50 = 0.16 ± 1.30 mg/kg); 0.2 µg/kg (morphine ED50 = 0.39 ± 1.27 mg/kg); and 0.02 µg/kg (morphine ED50 = 1.24 ± 1.30 mg/kg). Conclusions.  Leconotide caused a significant increase in reversal by morphine of the bone cancer-induced hyperalgesia without increasing the side effect profile of either drug. Clinical Implication.  Translation into clinical practice of the method of analgesia described here will improve the quantity and quality of analgesia in patients with bone metastases. The use of an ordinary parenteral route for administration of the calcium channel blocker (leconotide) at low dose opens up the technique to large numbers of patients who could not have an intrathecal catheter for drug administration. Furthermore, the potentiating synergistic effect with morphine on hyperalgesia without increased side effects will lead to greater analgesia with improved quality of life.

Detecting approximate clones in business process model repositories

Empirical evidence shows that repositories of business process models used in industrial practice contain significant amounts of duplication. This duplication arises for example when the repository covers multiple variants of the same processes or due to copy-pasting. Previous work has addressed the problem of efficiently retrieving exact clones that can be refactored into shared subprocess models. This article studies the broader problem of approximate clone detection in process models. The article proposes techniques for detecting clusters of approximate clones based on two well-known clustering algorithms: DBSCAN and Hi- erarchical Agglomerative Clustering (HAC). The article also defines a measure of standardizability of an approximate clone cluster, meaning the potential benefit of replacing the approximate clones with a single standardized subprocess. Experiments show that both techniques, in conjunction with the proposed standardizability measure, accurately retrieve clusters of approximate clones that originate from copy-pasting followed by independent modifications to the copied fragments. Additional experiments show that both techniques produce clusters that match those produced by human subjects and that are perceived to be standardizable.

A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model

In a tag-based recommender system, the multi-dimensional correlation should be modeled effectively for finding quality recommendations. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based on the maximum values of tensor elements. In order to improve the accuracy and scalability, we propose an implementation of the -mode block-striped (matrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

A high and low fear phenotype mouse model

Fear-related illnesses such as post-traumatic stress disorder (PTSD) impose a tremendous burden on individual quality of life, families, and the national economy. In the military population, 17-20% of services members returning from deployment are diagnosed with PTSD. While treatments have improved for PTSD and are helpful for some, many people continue to suffer despite therapy. The aim of this research is to examine fear memory behaviourally and at the cellular level in the amygdala by using a unique inter-cross strain of high and low fear phenotype mice. An extended outcross C57BL/6J x DBA/2J (F8) are selected for high or low Pavlovian fear memory to context and cue. On presentation of either the original learning context or the cue (tone) mice display high or low levels of freezing as a behavioural measure of fear. In order to identify key aspects of the cellular basis of this difference in fear memory behaviour we are making measurements of protein levels and neuron numbers of a known pathway involved in the consolidation of a long term fear memory (pMAPK). Ongoing studies aim to determine if high fear behaviour is associated with differential signalling in the lateral amygdala compared to low fear behaviour. Additionally, by blocking this pathway in the lateral amygdala (LA), we aim to reduce fear behaviour following Pavlovian fear conditioning. This research will help to unravel the cellular mechanisms underlying high fear behaviour and advance the field toward targeted treatment and improved outcomes, ultimately improving human quality of life.

A constitutive model for mechanical response characterization of pumpkin peel and flesh tissues under tensile and compressive loadings

Enhancing quality of food products and reducing volume of waste during mechanical operations of food industry requires a comprehensive knowledge of material response under loadings. While research has focused on mechanical response of food material, the volume of waste after harvesting and during processing stages is still considerably high in both developing and developed countries. This research aims to develop and evaluate a constitutive model of mechanical response of tough skinned vegetables under postharvest and processing operations. The model focuses on both tensile and compressive properties of pumpkin flesh and peel tissues where the behaviours of these tissues vary depending on various factors such as rheological response and cellular structure. Both elastic and plastic response of tissue were considered in the modelling process and finite elasticity combined with pseudo elasticity theory was applied to generate the model. The outcomes were then validated using the published results of experimental work on pumpkin flesh and peel under uniaxial tensile and compression. The constitutive coefficients for peel under tensile test was α = 25.66 and β = −18.48 Mpa and for flesh α = −5.29 and β = 5.27 Mpa. under compression the constitutive coefficients were α = 4.74 and β = −1.71 Mpa for peel and α = 0.76 and β = −1.86 Mpa for flesh samples. Constitutive curves predicted the values of force precisely and close to the experimental values. The curves were fit for whole stress versus strain curve as well as a section of curve up to bio yield point. The modelling outputs had presented good agreement with the empirical values and the constructive curves exhibited a very similar pattern to the experimental curves. The presented constitutive model can be applied next to other agricultural materials under loading in future.

The elaboration likelihood model : review, critique and research agenda

Purpose: The purpose of this paper is to review, critique and develop a research agenda for the Elaboration Likelihood Model (ELM). The model was introduced by Petty and Cacioppo over three decades ago and has been modified, revised and extended. Given modern communication contexts, it is appropriate to question the model’s validity and relevance. Design/methodology/approach: The authors develop a conceptual approach, based on a fully comprehensive and extensive review and critique of ELM and its development since its inception. Findings: This paper focuses on major issues concerning the ELM. These include model assumptions and its descriptive nature; continuum questions, multi-channel processing and mediating variables before turning to the need to replicate the ELM and to offer recommendations for its future development. Research limitations/implications: This paper offers a series of questions in terms of research implications. These include whether ELM could or should be replicated, its extension, a greater conceptualization of argument quality, an explanation of movement along the continuum and between central and peripheral routes to persuasion, or to use new methodologies and technologies to help better understanding consume thinking and behaviour? All these relate to the current need to explore the relevance of ELM in a more modern context. Practical implications: It is time to question the validity and relevance of the ELM. The diversity of on- and off-line media options and the variants of consumer choice raise significant issues. Originality/value: While the ELM model continues to be widely cited and taught as one of the major cornerstones of persuasion, questions are raised concerning its relevance and validity in 21st century communication contexts.

Emotion as a boost to metacognition : how worry enhances the quality of confidence

Emotion and cognition are known to interact during human decision processes. In this study we focus on a specific kind of cognition, namely metacognition. Our experiment induces a negative emotion, worry, during a perceptual task. In a numerosity task subjects have to make a two alternative forced choice and then reveal their confidence in this decision. We measure metacognition in terms of discrimination and calibration abilities. Our results show that metacognition, but not choice, is affected by the level of worry anticipatedbefore the decision. Under worry individuals tend to have better metacognition in terms of the two measures. Furthermore understanding the formation of confidence is better explained with taking into account the level of worry in the model. This study shows the importance of an emotional component in the formation and the quality of the subjective probabilities.