The prescribing behaviour of doctors is influenced by the pharmaceutical industry. This study investigated the extent to which contacts with pharmaceutical sales representatives (PSR) and the ...perception of these contacts influence prescribing habits.
An online questionnaire regarding contact with PSRs and perceptions of this contact was sent to 1,388 doctors, 11.5% (n = 160) of whom completed the survey. Individual prescribing data over a year (number of prescriptions, expenditure, and daily doses) for all on-patent branded, off-patent branded, and generic drugs were obtained from the Bavarian Association of Statutory Health Insurance Physicians.
84% of the doctors saw PSR at least once a week, and 14% daily. 69% accepted drug samples, 39% accepted stationery and 37% took part in sponsored continuing medical education (CME) frequently. 5 physicians (3%) accepted no benefits at all. 43% of doctors believed that they received adequate and accurate information from PSRs frequently or always and 42% believed that their prescribing habits were influenced by PSR visits occasionally or frequently. Practices that saw PSRs frequently had significantly higher total prescriptions and total daily doses (but not expenditure) than practices that were less frequently visited. Doctors who believed that they received accurate information from PSRs showed higher expenditures on off-patent branded drugs (thus available as generics) and a lower proportion of generics. The eschewal of sponsored CME was associated with a lower proportion of on patent-branded drug prescriptions, lower expenditure on off-patent branded drug prescriptions and a higher proportion of generics. Acceptance of office stationery was associated with higher daily doses.
Avoidance of industry-sponsored CME is associated with more rational prescribing habits. Furthermore, gift acceptance and the belief that one is receiving adequate information from a PSR are associated with changed prescribing habits. Further studies with larger sample sizes are needed.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Exposure to ambient air pollution is a well-established determinant of health and disease. The Lancet Commission on pollution and health concludes that air pollution is the leading environmental ...cause of global disease and premature death. Indeed, there is a growing body of evidence that links air pollution not only to adverse cardiorespiratory effects but also to increased risk of cerebrovascular and neuropsychiatric disorders. Despite being a relatively new area of investigation, overall, there is mounting recent evidence showing that exposure to multiple air pollutants, in particular to fine particles, may affect the central nervous system (CNS) and brain health, thereby contributing to increased risk of stroke, dementia, Parkinson's disease, cognitive dysfunction, neurodevelopmental disorders, depression and other related conditions. The underlying molecular mechanisms of susceptibility and disease remain largely elusive. However, emerging evidence suggests inflammation and oxidative stress to be crucial factors in the pathogenesis of air pollution-induced disorders, driven by the enhanced production of proinflammatory mediators and reactive oxygen species in response to exposure to various air pollutants. From a public health perspective, mitigation measures are urgent to reduce the burden of disease and premature mortality from ambient air pollution.
Modeling flow and transport in porous media using pore‐scale modeling is reliant on rock properties derived from digital rock images using segmentation techniques. These digital rock images obtained ...using computed tomography incorporate the variation in the intensity of phases depending on the attenuation of X‐rays. A standard technique is the segmentation of tomographic images based on user‐selected grayscale thresholding, allowing the identification of different phases. This threshold is subjective based on the operator and results in loss of essential information about the grayscale variation after segmentation. This paper implements the gray‐level co‐occurrence matrix (GLCM) incorporating the full range of grayscale information. The GLCM captures the relative occurrence of grayscale values in a spatial map. These maps show visually connected/disconnected populations of different phases such as pore space, quartz grains, minerals, and other features. We show that each rock has its own GLCM signature depending on the variations in gray‐level intensities. Several statistical measures are calculated: (1) GLCM contrast describing local variation in the gray‐level intensities, (2) GLCM angular second moment, describing the rock homogeneity; (3) GLCM mean, describing weighted average of the probability of occurrence of features based on their location on the GLCM map; and (4) GLCM correlation, measuring the linear dependencies of grayscale values and the degree of (an) isotropy in the micro–computed tomographic images of each of the rock types. The GLCM method provides a pathway to alleviate user biases and allow automation of micro–computed tomography analyses.
Plain Language Summary
The flow of fluids through a porous rock is heavily dependent on the geometrical structure of the rock. Modern techniques allow us to study the rock structure using high‐resolution images obtained from computed tomography scanning. These high‐resolution images represent different features of rocks by a range of grayscale values. The current interpretation method is to select a given grayscale value as a threshold to distinguish features from one another. This leads to user‐biased outcomes, and also, important information about minerals is lost. Herein, we show that this problem can be alleviated by applying computer vision techniques directly to the original X‐ray images and extracting directional, spatial, and frequency based information. This new perspective provides a fully automated analysis of rock characteristics and allows feature identification directly from the grayscale statistics. This contribution shows the descriptive power of the technique by characterizing rock structure using automated pattern recognition techniques.
Key Points
An unbiased automated second‐order statistical approach for characterizing porous media using gray‐level co‐occurrence matrix is developed
Rock characterization is carried out by correlating statistical measures to grain size, phase noise, the degree of (an) isotropy, and feature identification
Several advantages of computer vision techniques for rapid and reliable analyses of X‐ray images is discussed
Background
Resilience can be defined as maintaining or regaining mental health during or after significant adversities such as a potentially traumatising event, challenging life circumstances, a ...critical life transition or physical illness. Healthcare students, such as medical, nursing, psychology and social work students, are exposed to various study‐ and work‐related stressors, the latter particularly during later phases of health professional education. They are at increased risk of developing symptoms of burnout or mental disorders. This population may benefit from resilience‐promoting training programmes.
Objectives
To assess the effects of interventions to foster resilience in healthcare students, that is, students in training for health professions delivering direct medical care (e.g. medical, nursing, midwifery or paramedic students), and those in training for allied health professions, as distinct from medical care (e.g. psychology, physical therapy or social work students).
Search methods
We searched CENTRAL, MEDLINE, Embase, 11 other databases and three trial registries from 1990 to June 2019. We checked reference lists and contacted researchers in the field. We updated this search in four key databases in June 2020, but we have not yet incorporated these results.
Selection criteria
Randomised controlled trials (RCTs) comparing any form of psychological intervention to foster resilience, hardiness or post‐traumatic growth versus no intervention, waiting list, usual care, and active or attention control, in adults (18 years and older), who are healthcare students. Primary outcomes were resilience, anxiety, depression, stress or stress perception, and well‐being or quality of life. Secondary outcomes were resilience factors.
Data collection and analysis
Two review authors independently selected studies, extracted data, assessed risks of bias, and rated the certainty of the evidence using the GRADE approach (at post‐test only).
Main results
We included 30 RCTs, of which 24 were set in high‐income countries and six in (upper‐ to lower‐) middle‐income countries. Twenty‐two studies focused solely on healthcare students (1315 participants; number randomised not specified for two studies), including both students in health professions delivering direct medical care and those in allied health professions, such as psychology and physical therapy. Half of the studies were conducted in a university or school setting, including nursing/midwifery students or medical students. Eight studies investigated mixed samples (1365 participants), with healthcare students and participants outside of a health professional study field.
Participants mainly included women (63.3% to 67.3% in mixed samples) from young adulthood (mean age range, if reported: 19.5 to 26.83 years; 19.35 to 38.14 years in mixed samples). Seventeen of the studies investigated group interventions of high training intensity (11 studies; > 12 hours/sessions), that were delivered face‐to‐face (17 studies). Of the included studies, eight compared a resilience training based on mindfulness versus unspecific comparators (e.g. wait‐list).
The studies were funded by different sources (e.g. universities, foundations), or a combination of various sources (four studies). Seven studies did not specify a potential funder, and three studies received no funding support.
Risk of bias was high or unclear, with main flaws in performance, detection, attrition and reporting bias domains.
At post‐intervention, very‐low certainty evidence indicated that, compared to controls, healthcare students receiving resilience training may report higher levels of resilience (standardised mean difference (SMD) 0.43, 95% confidence interval (CI) 0.07 to 0.78; 9 studies, 561 participants), lower levels of anxiety (SMD −0.45, 95% CI −0.84 to −0.06; 7 studies, 362 participants), and lower levels of stress or stress perception (SMD −0.28, 95% CI −0.48 to −0.09; 7 studies, 420 participants). Effect sizes varied between small and moderate. There was little or no evidence of any effect of resilience training on depression (SMD −0.20, 95% CI −0.52 to 0.11; 6 studies, 332 participants; very‐low certainty evidence) or well‐being or quality of life (SMD 0.15, 95% CI −0.14 to 0.43; 4 studies, 251 participants; very‐low certainty evidence).
Adverse effects were measured in four studies, but data were only reported for three of them. None of the three studies reported any adverse events occurring during the study (very‐low certainty of evidence).
Authors' conclusions
For healthcare students, there is very‐low certainty evidence for the effect of resilience training on resilience, anxiety, and stress or stress perception at post‐intervention.
The heterogeneous interventions, the paucity of short‐, medium‐ or long‐term data, and the geographical distribution restricted to high‐income countries limit the generalisability of results. Conclusions should therefore be drawn cautiously. Since the findings suggest positive effects of resilience training for healthcare students with very‐low certainty evidence, high‐quality replications and improved study designs (e.g. a consensus on the definition of resilience, the assessment of individual stressor exposure, more attention controls, and longer follow‐up periods) are clearly needed.
Smith and colleagues developed the Brief Resilience Scale (BRS) to assess the individual ability to recover from stress despite significant adversity. This study aimed to validate the German version ...of the BRS. We used data from a population-based (sample 1: n = 1.481) and a representative (sample 2: n = 1.128) sample of participants from the German general population (age ≥ 18) to assess reliability and validity. Confirmatory factor analyses (CFA) were conducted to compare one- and two-factorial models from previous studies with a method-factor model which especially accounts for the wording of the items. Reliability was analyzed. Convergent validity was measured by correlating BRS scores with mental health measures, coping, social support, and optimism. Reliability was good (α = .85, ω = .85 for both samples). The method-factor model showed excellent model fit (sample 1: χ2/df = 7.544; RMSEA = .07; CFI = .99; SRMR = .02; sample 2: χ2/df = 1.166; RMSEA = .01; CFI = 1.00; SRMR = .01) which was significantly better than the one-factor model (Δχ2(4) = 172.71, p < .001) or the two-factor model (Δχ2(3) = 31.16, p < .001). The BRS was positively correlated with well-being, social support, optimism, and the coping strategies active coping, positive reframing, acceptance, and humor. It was negatively correlated with somatic symptoms, anxiety and insomnia, social dysfunction, depression, and the coping strategies religion, denial, venting, substance use, and self-blame. To conclude, our results provide evidence for the reliability and validity of the German adaptation of the BRS as well as the unidimensional structure of the scale once method effects are accounted for.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The concept of linking pore‐scale data to continuum‐scale characteristics of porous media relies on the existence of a representative elementary volume (REV). The current techniques for estimating ...REVs require access to segmented micro‐computed tomographic (micro‐CT) images and computations of petrophysical properties which are computationally intensive and time‐consuming. Herein, a texture characterization method called the Gray‐Level Size Zone Matrix (GLSZM) is applied directly to raw grayscale micro‐CT images. GLSZM representations of 3D micro‐CT images capture information regarding the connectivity of gray‐level intensities, termed as “size‐zones.” Statistical descriptors of pore space are calculated based on GLSZM to understand the connectivity of low gray‐level intensities. These GLSZM statistics capture microstructural fluctuations and offer insights into the impact of grayscale heterogeneity on REV size. This approach allows REV sizes to be estimated directly using grayscale micro‐CT images, in a reproducible, less time‐consuming and computationally efficient manner.
Plain Language Summary
Representative elementary volumes or REVs are defined as the smallest volume of the rock sample that encompasses the region of local heterogeneities for the length scale and property being investigated. X‐ray micro‐computed tomographic (micro‐CT) images capture the rock structure as different gray‐level intensities. Traditionally, raw or grayscale micro‐CT images undergo a series of image processing steps to obtain segmented micro‐CT images wherein a label is assigned to pore space and minerals. REVs are then estimated based on properties calculated from these segmented images. While it is preferred that information‐rich raw micro‐CT images be used for such an analysis, there are limitations on properties that can be calculated. To tackle this challenge, we introduce a novel texture characterization technique that can be directly applied to raw micro‐CT images. This approach captures valuable information about gray‐level intensities and their connectivity in a 3D space. The statistics then allow us to describe important aspects of the pore spaces that can otherwise only be inferred from their binary equivalent. In addition to this, using this texture characterization technique would allow us to infer REV sizes in a robust and computationally efficient manner.
Key Points
Texture characterization is used to estimate the representative elementary volume from X‐ray tomographic images of rocks
Gray‐Level Size Zone Matrix describes the connectivity of low gray‐level intensities using grayscale information of rock images
This approach allows direct use of raw micro‐CT images to estimate REV sizes in a robust and computationally efficient manner
The processing of verbal fluency tasks relies on the coordinated activity of a number of brain areas, particularly in the frontal and temporal lobes of the left hemisphere. Recent studies using ...functional magnetic resonance imaging (fMRI) to study the neural networks subserving verbal fluency functions have yielded divergent results especially with respect to a parcellation of the inferior frontal gyrus for phonemic and semantic verbal fluency. We conducted a coordinate-based activation likelihood estimation (ALE) meta-analysis on brain activation during the processing of phonemic and semantic verbal fluency tasks involving 28 individual studies with 490 healthy volunteers.
For phonemic as well as for semantic verbal fluency, the most prominent clusters of brain activation were found in the left inferior/middle frontal gyrus (LIFG/MIFG) and the anterior cingulate gyrus. BA 44 was only involved in the processing of phonemic verbal fluency tasks, BA 45 and 47 in the processing of phonemic and semantic fluency tasks.
Our comparison of brain activation during the execution of either phonemic or semantic verbal fluency tasks revealed evidence for spatially different activation in BA 44, but not other regions of the LIFG/LMFG (BA 9, 45, 47) during phonemic and semantic verbal fluency processing.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Equilibrium thermodynamics has been of fundamental importance to many branches of engineering including cyclical mechanical applications. However, in geomechanics and geological applications it has ...not yet reached a consensus in the community. Reason for the failure of establishing thermodynamic laws as a ground principle is the far from equilibrium nature of geomechanical problems which prevent the local equilibrium assumption. Problems including rate-dependence and poromechanical complexity, where deformation often occurs in a highly localized manner, were therefore thought to be not amenable to a thermodynamic approach. Here we show that the theory of thermomechanics, originally proposed for quasi-static hyperplastic deformation problems can be extended to include rate-dependent critical state-line models for porous rocks. The development therefore makes thermodynamic-consistent modeling available for civil engineering, geological and even geodynamic problems. In this two-part contribution, we present extensions of the thermomechanics theory to account for the poromechanics of path- and rate-dependent critical state line models and we cover the relevance of this thermodynamic-consistent model for civil engineering, geological and geodynamic applications. In this first part, we review the concepts behind the thermomechanics theory and present a thermodynamic extension of generic critical state line models for visco-plasticity and damage mechanics and analyze the model prediction for strain localization.
Compaction bands are a type of localized deformation that can occur as diffuse or discrete bands in porous rocks. While modeling of shear bands can replicate discrete and diffusive bands, numerical ...models of compaction have so far only been able to describe the formation of discrete compaction bands. In this study, we present a new thermodynamic approach to model compaction bands that is able to capture both discrete and diffuse compaction band growth. The approach is based on a reaction‐diffusion formalism that includes an additional entropy flux. This entropic velocity regularizes the solution, by introducing a characteristic diffusion length scale and controlling the mode change from discrete to diffusive post‐localisation growth. The approach is used to model compaction band growth in highly porous carbonates. The model can replicate the areas of material damage exhibiting reduced porosity which are often observed as nuclei for the growth of compaction bands in experiments. The model also has the versatility to predict the formation of diffuse compaction bands, which is a significant advance in the field of compaction band modeling. The method can potentially be used for investigating the effect of material heterogeneities on compaction band growth and is heuristic for developing new methodologies for forecasting compaction band formation.
Plain Language Summary
Compaction bands are areas of localized deformation in materials with multiple phases, such as porous rocks. They form when one of the phases localizes and forms bands perpendicular to the direction of the maximum principal effective stress. In this study, we present a new thermodynamically consistent model for compaction bands in porous materials. The model is based on the modified Cam‐Clay plasticity model, but it includes a number of additions to make it more realistic and to account for the mesh sensitivity of numerical solutions. We test the new model against experimental results for compaction bands in highly porous carbonate (Mt Gambier limestone). We find that the model can accurately match the experimental results. This new model is a significant advance in the modeling of compaction bands. It has the potential to be used to investigate the effect of material properties, heterogeneity and loading conditions on compaction band formation, and to develop new methods for predicting compaction bands.
Key Points
A new thermodynamically consistent Cam‐Clay model for deformation bands in porous rocks is presented, alleviating the numerical problem of mesh sensitivity
The model is based on the modified Cam‐Clay plasticity model augmented by an entropic regularization technique, accurately capturing the experimental results for compaction bands in highly porous carbonate
The mode change from discrete to diffuse post‐localisation evolution is found to be related to the reaction‐diffusion processes of microstructure interactions
Mental burden due to the SARS-CoV-2 pandemic has been widely reported for the general public and specific risk groups like healthcare workers and different patient populations. We aimed to assess its ...impact on mental health during the early phase by comparing pandemic with prepandemic data and to identify potential risk and protective factors.
For this systematic review and meta-analyses, we systematically searched PubMed, PsycINFO, and Web of Science from January 1, 2019 to May 29, 2020, and screened reference lists of included studies. In addition, we searched PubMed and PsycINFO for prepandemic comparative data. Survey studies assessing mental burden by the SARS-CoV-2 pandemic in the general population, healthcare workers, or any patients (eg, COVID-19 patients), with a broad range of eligible mental health outcomes, and matching studies evaluating prepandemic comparative data in the same population (if available) were included. We used multilevel meta-analyses for main, subgroup, and sensitivity analyses, focusing on (perceived) stress, symptoms of anxiety and depression, and sleep-related symptoms as primary outcomes.
Of 2429 records retrieved, 104 were included in the review (n = 208,261 participants), 43 in the meta-analysis (n = 71,613 participants). While symptoms of anxiety (standardized mean difference SMD 0.40; 95% CI 0.15-0.65) and depression (SMD 0.67; 95% CI 0.07-1.27) were increased in the general population during the early phase of the pandemic compared with prepandemic conditions, mental burden was not increased in patients as well as healthcare workers, irrespective of COVID-19 patient contact. Specific outcome measures (eg, Patient Health Questionnaire) and older comparative data (published ≥5 years ago) were associated with increased mental burden. Across the three population groups, existing mental disorders, female sex, and concerns about getting infected were repeatedly reported as risk factors, while older age, a good economic situation, and education were protective.
This meta-analysis paints a more differentiated picture of the mental health consequences in pandemic situations than previous reviews. High-quality, representative surveys, high granular longitudinal studies, and more research on protective factors are required to better understand the psychological impacts of the SARS-CoV-2 pandemic and to help design effective preventive measures and interventions that are tailored to the needs of specific population groups.
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