Mental disorder prevalence in chronic pain patients using opioid versus non-opioid analgesics: A registry-linkage study

Background Chronic pain and mental disorders are leading causes of disability worldwide. Individuals with chronic pain are more likely to experience mental disorders compared to individuals without chronic pain, but large-scale estimates are lacking. We aimed to calculate overall prevalence of mental health diagnoses from primary and secondary care among individuals treated for chronic pain in 2019 and to compare prevalence among chronic pain patients receiving opioid versus non-opioid analgesics, according to age and gender. Methods It is a population-based cohort study. Linked data from nationwide health registers on dispensed drugs and diagnoses from primary (ICPC-2) and secondary (ICD-10) health care. Chronic pain patients were identified as all patients over 18 years of age filling at least one prescription of an analgesic reimbursed for non-malignant chronic pain in both 2018 and 2019 (N = 139,434, 69.3% women). Results Prevalence of any mental health diagnosis was 35.6% (95% confidence interval: 35.4%–35.9%) when sleep diagnoses were included and 29.0% (28.8%–29.3%) when excluded. The most prevalent diagnostic categories were sleep disorders (14% [13.8%–14.2%]), depressive and related disorders (10.1% [9.9%–10.2%]) and phobia and other anxiety disorders (5.7% [5.5%–5.8%]). Prevalence of most diagnostic categories was higher in the group using opioids compared to non-opioids. The group with the highest overall prevalence was young women (18–44 years) using opioids (50.1% [47.2%–53.0%]). Conclusions Mental health diagnoses are common in chronic pain patients receiving analgesics, particularly among young individuals and opioid users. The combination of opioid use and high psychiatric comorbidity suggests that prescribers should attend to mental health in addition to somatic pain. Significance This large-scale study with nation-wide registry data supports previous findings of high psychiatric burden in chronic pain patients. Opioid users had significantly higher prevalence of mental health diagnoses, regardless of age and gender compared to users of non-opioid analgesics. Opioid users with chronic pain therefore stand out as a particularly vulnerable group and should be followed up closely by their physician to ensure they receive sufficient care for both their mental and somatic symptoms.Mental disorder prevalence in chronic pain patients using opioid versus non-opioid analgesics: A registry-linkage studyacceptedVersionpublishedVersio

Sources of uncertainty in modeled land carbon storage within and across three MIPs: Diagnosis with three new techniques

This is the final version. Available from the American Meteorological Society via the DOI in this recordTerrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land-Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.This paper is financially supported by the Research and Development Special Fund for Public Welfare Industry of the Ministry of Water Research in China (201501028). JBF and CRS were supported in part by NASA’s Carbon Cycle Science program. JBF was also supported in part by NASA’s Terrestrial Ecology and Carbon Monitoring System programs. JT acknowledges RCN funded project EVA (229771) and BCCR-BIGCHANGE

Sources of uncertainty in modeled land carbon storage within and across three MIPs: Diagnosis with three new techniques

This is the final version. Available from the American Meteorological Society via the DOI in this recordTerrestrial carbon cycle models have incorporated increasingly more processes as a means to achieve more-realistic representations of ecosystem carbon cycling. Despite this, there are large across-model variations in the simulation and projection of carbon cycling. Several model intercomparison projects (MIPs), for example, the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (historical simulations), Trends in Net Land-Atmosphere Carbon Exchange (TRENDY), and Multiscale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), have sought to understand intermodel differences. In this study, the authors developed a suite of new techniques to conduct post-MIP analysis to gain insights into uncertainty sources across 25 models in the three MIPs. First, terrestrial carbon storage dynamics were characterized by a three-dimensional (3D) model output space with coordinates of carbon residence time, net primary productivity (NPP), and carbon storage potential. The latter represents the potential of an ecosystem to lose or gain carbon. This space can be used to measure how and why model output differs. Models with a nitrogen cycle generally exhibit lower annual NPP in comparison with other models, and mostly negative carbon storage potential. Second, a transient traceability framework was used to decompose any given carbon cycle model into traceable components and identify the sources of model differences. The carbon residence time (or NPP) was traced to baseline carbon residence time (or baseline NPP related to the maximum carbon input), environmental scalars, and climate forcing. Third, by applying a variance decomposition method, the authors show that the intermodel differences in carbon storage can be mainly attributed to the baseline carbon residence time and baseline NPP (>90% in the three MIPs). The three techniques developed in this study offer a novel approach to gain more insight from existing MIPs and can point out directions for future MIPs. Since this study is conducted at the global scale for an overview on intermodel differences, future studies should focus more on regional analysis to identify the sources of uncertainties and improve models at the specified mechanism level.This paper is financially supported by the Research and Development Special Fund for Public Welfare Industry of the Ministry of Water Research in China (201501028). JBF and CRS were supported in part by NASA’s Carbon Cycle Science program. JBF was also supported in part by NASA’s Terrestrial Ecology and Carbon Monitoring System programs. JT acknowledges RCN funded project EVA (229771) and BCCR-BIGCHANGE

When Is a Principal Charged With an Agent’s Knowledge?

Question: Detecting species presence in vegetation and making visual assessment of abundances involve a certain amount of skill, and therefore subjectivity. We evaluated the magnitude of the error in data, and its consequences for evaluating temporal trends. Location: Swedish forest vegetation. Methods: Vegetation data were collected independently by two observers in 342 permanent 100-m2 plots in mature boreal forests. Each plot was visited by one observer from a group of 36 and one of two quality assessment observers. The cover class of 29 taxa was recorded, and presence/absence for an additional 50. Results: Overall, one third of each occurrence was missed by one of the two observers, but with large differences among species. There were more missed occurrences at low abundances. Species occurring at low abundance when present tended to be frequently overlooked. Variance component analyses indicated that cover data on 5 of 17 species had a significant observer bias. Observer-explained variance was < 10% in 15 of 17 species. Conclusion: The substantial number of missed occurrences suggests poor power in detecting changes based on presence/absence data. The magnitude of observer bias in cover estimates was relatively small, compared with random error, and therefore potentially analytically tractable. Data in this monitoring system could be improved by a more structured working model during field work.Original publication: Milberg, P., Bergstedt, J., Fridman, J., Odell, G & Westerberg, L., Systematic and random variation in vegetation monitoring data, 2008, Journal of Vegetation Science, (19), 633-644. http://dx.doi.org/10.3170/2008-8-18423. Copyright: Opulus Press, http://www.opuluspress.se/index.ph

Genmodifisert maislinje MON810

Source at https://vkm.no/Vurderingen av den insektresistente maislinjen MON 810 er utført av Faggruppe for genmodifiserte organismer under Vitenskapskomiteen for mattrygghet. Vitenskapskomiteen for mattrygghet er blitt bedt av Direktoratet for naturforvalting om å foreta en vurdering av miljørisiko i forbindelse med nasjonal sluttbehandling av søknad om godkjenning av MON 810 for alle bruksområder

Application of affymetrix array and massively parallel signature sequencing for identification of genes involved in prostate cancer progression

BACKGROUND: Affymetrix GeneChip Array and Massively Parallel Signature Sequencing (MPSS) are two high throughput methodologies used to profile transcriptomes. Each method has certain strengths and weaknesses; however, no comparison has been made between the data derived from Affymetrix arrays and MPSS. In this study, two lineage-related prostate cancer cell lines, LNCaP and C4-2, were used for transcriptome analysis with the aim of identifying genes associated with prostate cancer progression. METHODS: Affymetrix GeneChip array and MPSS analyses were performed. Data was analyzed with GeneSpring 6.2 and in-house perl scripts. Expression array results were verified with RT-PCR. RESULTS: Comparison of the data revealed that both technologies detected genes the other did not. In LNCaP, 3,180 genes were only detected by Affymetrix and 1,169 genes were only detected by MPSS. Similarly, in C4-2, 4,121 genes were only detected by Affymetrix and 1,014 genes were only detected by MPSS. Analysis of the combined transcriptomes identified 66 genes unique to LNCaP cells and 33 genes unique to C4-2 cells. Expression analysis of these genes in prostate cancer specimens showed CA1 to be highly expressed in bone metastasis but not expressed in primary tumor and EPHA7 to be expressed in normal prostate and primary tumor but not bone metastasis. CONCLUSION: Our data indicates that transcriptome profiling with a single methodology will not fully assess the expression of all genes in a cell line. A combination of transcription profiling technologies such as DNA array and MPSS provides a more robust means to assess the expression profile of an RNA sample. Finally, genes that were differentially expressed in cell lines were also differentially expressed in primary prostate cancer and its metastases

Integrating the Genetic and Physical Maps of Arabidopsis thaliana: Identification of Mapped Alleles of Cloned Essential (EMB) Genes

The classical genetic map of Arabidopsis includes more than 130 genes with an embryo-defective (emb) mutant phenotype. Many of these essential genes remain to be cloned. Hundreds of additional EMB genes have been cloned and catalogued (www.seedgenes.org) but not mapped. To facilitate EMB gene identification and assess the current level of saturation, we updated the classical map, compared the physical and genetic locations of mapped loci, and performed allelism tests between mapped (but not cloned) and cloned (but not mapped) emb mutants with similar chromosome locations. Two hundred pairwise combinations of genes located on chromosomes 1 and 5 were tested and more than 1100 total crosses were screened. Sixteen of 51 mapped emb mutants examined were found to be disrupted in a known EMB gene. Alleles of a wide range of published EMB genes (YDA, GLA1, TIL1, AtASP38, AtDEK1, EMB506, DG1, OEP80) were discovered. Two EMS mutants isolated 30 years ago, T-DNA mutants with complex insertion sites, and a mutant with an atypical, embryo-specific phenotype were resolved. The frequency of allelism encountered was consistent with past estimates of 500 to 1000 EMB loci. New EMB genes identified among mapped T-DNA insertion mutants included CHC1, which is required for chromatin remodeling, and SHS1/AtBT1, which encodes a plastidial nucleotide transporter similar to the maize Brittle1 protein required for normal endosperm development. Two classical genetic markers (PY, ALB1) were identified based on similar map locations of known genes required for thiamine (THIC) and chlorophyll (PDE166) biosynthesis. The alignment of genetic and physical maps presented here should facilitate the continued analysis of essential genes in Arabidopsis and further characterization of a broad spectrum of mutant phenotypes in a model plant

Quantile pyramids for Bayesian nonparametrics

Polya trees fix partitions and use random probabilities in order to construct random probability measures. With quantile pyramids we instead fix probabilities and use random partitions. For nonparametric Bayesian inference we use a prior which supports piecewise linear quantile functions, based on the need to work with a finite set of partitions, yet we show that the limiting version of the prior exists. We also discuss and investigate an alternative model based on the so-called substitute likelihood, Both approaches factorize in a convenient way leading to relatively straightforward analysis via MCMC, since analytic summaries of posterior distributions are too complicated. We give conditions securing the existence of an absolute continuous quantile process, and discuss consistency and approximate normality for the sequence of posterior distributions. Illustrations are included