Educational activities for physicians sponsored by opioid manufacturers are implicated in the over- and mis-prescribing of opioids. However, the implications of promotion to nurses are poorly understood. Nurses play a key role in assessing pain, addressing the determinants of pain, and administering opioid medications. We sought to understand the nature and content of pain-related educational events sponsored by opioid manufacturers and to compare events targeting physicians and nurses.

We conducted a cross sectional, descriptive analysis of pharmaceutical company reports detailing 116,845 sponsored educational events attended by health professionals from 2011 to 2015 in Australia. We included events that were sponsored by manufacturers of prescription opioid analgesics and were pain related. We compared event characteristics across three attendee groups (a) physicians only; (b) at least one nurse in attendance; and (c) nurses only. We coded the unstructured data using iteratively generated keywords for vand geriatrics (25%; p < .001) than physician-only or multidisciplinary events.

Opioid promotion via sponsored educational events extends beyond physicians to multidisciplinary teams and specifically, nurses. Despite lack of evidence that opioids improve outcomes for long-term chronic non-cancer pain, hundreds of sponsored educational events focused on chronic pain. Regulators should consider the validity of distinguishing between pharmaceutical companies’ “promotional” and “non-promotional” activities.

Opioid promotion via sponsored educational events extends beyond physicians to multidisciplinary teams and specifically, nurses. Despite lack of evidence that opioids improve outcomes for long-term chronic non-cancer pain, hundreds of sponsored educational events focused on chronic pain. Regulators should consider the validity of distinguishing between pharmaceutical companies’ “promotional” and “non-promotional” activities.Leptospirosis is a ubiquitous zoonotic disease and a major clinical challenge owing to the multitude of clinical presentations and manifestations that are possibly attributable to the diversity of Leptospira, the understanding of which is key to study the epidemiology of this emerging global disease threat. Sri Lanka is a hotspot for leptospirosis with high levels of endemicity as well as annual epidemics. We carried out a prospective study of Leptospira diversity in Sri Lanka, covering the full range of climatic zones, geography, and clinical severity. Samples were collected for leptospiral culture from 1,192 patients from 15 of 25 districts in Sri Lanka over two and half years. Twenty-five isolates belonging to four pathogenic Leptospira species were identified L. interrogans, L. borgpetersenii, L. weilii, and L. kirschneri. At least six serogroups were identified among the isolates Autumnalis (6), Pyrogenes (4), Icterohaemorrhagiae (2), Celledoni (1), Grippotyphosa (2) and Bataviae (1). Seven isolates did not agglutinate using available antisera panels, suggesting new serogroups. Isolates were sequenced using an Illumina platform. These data add 25 new core genome sequence types and were clustered in 15 clonal groups, including 12 new clonal groups. L. borgpetersenii was found only in the dry zone and L. weilii only in the wet zone. Acute kidney injury and cardiovascular involvement were seen only with L. interrogans infections. Thrombocytopenia and liver impairment were seen in both L. interrogans and L. borgpetersenii infections. The inadequate sensitivity of culture isolation to identify infecting Leptospira species underscores the need for culture-independent typing methods for Leptospira.The ensemble Kalman filter (EnKF) is a data assimilation technique that uses an ensemble of models, updated with data, to track the time evolution of a usually non-linear system. check details It does so by using an empirical approximation to the well-known Kalman filter. However, its performance can suffer when the ensemble size is smaller than the state space, as is often necessary for computationally burdensome models. This scenario means that the empirical estimate of the state covariance is not full rank and possibly quite noisy. To solve this problem in this high dimensional regime, we propose a computationally fast and easy to implement algorithm called the penalized ensemble Kalman filter (PEnKF). Under certain conditions, it can be theoretically proven that the PEnKF will be accurate (the estimation error will converge to zero) despite having fewer ensemble members than state dimensions. Further, as contrasted to localization methods, the proposed approach learns the covariance structure associated with the dynamical system. These theoretical results are supported with simulations of several non-linear and high dimensional systems.In coronary artery disease (CAD) the adipocytokine content in the heart fat depot is altered, but it has not been established whether these changes are associated with the degree of atherosclerotic damage to the coronary artery (CA). Were examined 84 patients with CAD, and according to the degree of atherosclerotic state based on the SYNTAX Score scale, were divided 39 moderate (≤22 points), 20 severe (23-31 points) and 25 extremely severe (≥32 points). Biopsies of subcutaneous (SAT), epicardial (EAT) and perivascular adipose tissue (PVAT) were obtained during elective coronary artery bypass grafting (CABG). The expression of adipocytokine was determined using real-time PCR. The concentration of the studied adipocytokines in adipocyte culture medium was measured by ELISA. Statistical analysis was performed using logistic regression analysis. In the adipocytes of the cardiac depot of patients with CAD, an increase in the expression and secretion of leptin and IL-6 and a decrease in adiponectin, with a maximum manifestation in severe and extremely severe CA lesions, was observed. EAT adipocytes were characterized by minimal expression of the adiponectin gene maximal gene expression leptin and IL-6 compared to SAT and PVAT adipocytes.Scan pattern analysis has been discussed as a promising tool in the context of real-time gaze-based applications. In particular, information-theoretic measures of scan path predictability, such as the gaze transition entropy (GTE), have been proposed for detecting relevant changes in user state or task demand. These measures model scan patterns as first-order Markov chains, assuming that only the location of the previous fixation is predictive of the next fixation in time. However, this assumption may not be sufficient in general, as recent research has shown that scan patterns may also exhibit more long-range temporal correlations. Thus, we here evaluate the active information storage (AIS) as a novel information-theoretic approach to quantifying scan path predictability in a dynamic task. In contrast to the GTE, the AIS provides means to statistically test and account for temporal correlations in scan path data beyond the previous last fixation. We compare AIS to GTE in a driving simulator experiment, in which participants drove in a highway scenario, where trials were defined based on an experimental manipulation that encouraged the driver to start an overtaking maneuver.