The role of illness perception and emotions on quality of life in fibromyalgia compared with other chronic pain conditions.

OBJECTIVE: Fibromyalgia syndrome (FMs) is a chronic widespread pain condition that can negatively impact on all aspects of patient's life. The purpose of this study was: first, to evaluate illness perception (IP), quality of life (QoL) and affective-emotive variables (EAV) of patients with FM; and second, to compare these variables to different pain conditions. METHODS: Consecutive 34 women (mean age 47.4±8.3 years) affected by FM were enrolled for the study from December 2009 to May 2011. IP was evaluated by means of the Revised Illness Perception Questionnaire, QoL through Nottigham Health Profile and EAV through the Beck Depression Inventory. Scores were compared with rheumatoid arthritis (RA) (n=20; mean age 53±12.8 years) and low back pain (LBP) (n=20; 51.3±7.8 years) groups. RESULTS: FM patients scored higher than RA and LBP groups on IP (Identity scale mean: FM=8.8±2.3, AR=5.5±3.3, LBP=4.1±2.9; Kruskal-Wallis=24.42). Moreover FM patients show higher EAV (mean FM=21±9.6, AR=8.9±5.6, LBP=14.9±6.5; Kolmogorov-Smirnov Z=2.17) and QoL (Pain scale mean: FM=74.2±24.1; AR=35.7±19.9; LBP=56.5±20.4; Kolmogorov-Smirnov Z=2.27; Energy scale mean: FM=86.2±28.5; AR=46.8±35.4; LBP=61.6 ±63.7; Kolmogorov-Smirnov Z=1.98) than RA group. CONCLUSIONS: Our study highlighted dysfunctional IP, low QoL, high EAV scores in FM patients and the significant relations between these variables. Research results provided support for relevance of a multidisciplinary approach to the management of FM, including psychological interventions, according to a biopsychosocial perspective.

Introducing an integrated climate change perspective in POPs modelling, monitoring and regulation.

This paper presents a review on the implications of climate change on the monitoring, modelling and regulation of persistent organic pollutants (POPs). Current research gaps are also identified and discussed. Long-term data sets are essential to identify relationships between climate fluctuations and changes in chemical species distribution. Reconstructing the influence of climatic changes on POPs environmental behaviour is very challenging in some local studies, and some insights can be obtained by the few available dated sediment cores or by studying POPs response to inter-annual climate fluctuations. Knowledge gaps and future projections can be studied by developing and applying various modelling tools, identifying compounds susceptibility to climate change, local and global effects, orienting international policies. Long-term monitoring strategies and modelling exercises taking into account climate change should be considered when devising new regulatory plans in chemicals management.

Climate change influence on POPs distribution and fate: a case study.

Climate change has the potential of affecting the behaviour and distribution of organic pollutants, including POPs. Direct effects of climate change, like temperature increase, modification of wind and precipitation patterns, sea level rise, snow and ice cover, may be very effective in altering the partitioning of POPs among the environmental compartments. Other consequences of future climate scenarios may imply the alteration of degradation rates, soil properties (and hence land use), air-particle partitioning of chemicals and so forth. A case study is here presented to illustrate the major implications of climate change on the long term at the local scale. A dynamic multimedia model was applied to selected PCB and PCDD/F congeners to simulate the effects of climate change on their distribution and fluxes over the next 50 y in the Venice Lagoon (Italy). Different climate change scenarios were tested, finding noticeable variations in POPs concentration even for minor environmental changes. PCBs and PCDFs environmental concentrations may differ by a factor two in a moderate climate change scenario, compared to a situation with stable climate over the next 50 y. However, model results also suggest that if global warming may have the potential of reducing the environmental levels of these chemicals, it would probably enhance their mobility and hence their potential for long range atmospheric transport.

The maximum reservoir capacity of soils for persistent organic pollutants: implications for global cycling.

The concept of maximum reservoir capacity (MRC), the ratio of the capacities of the surface soil and of the atmospheric mixed layer (AML) to hold chemical under equilibrium conditions, is applied to selected persistent organic pollutants (POPs) in the surface 'skin' (1 mm) of soils. MRC is calculated as a function of soil organic matter (SOM) content and temperature-dependent K(OA) and mapped globally for selected PCB congeners (PCB-28; -153; -180) and HCB, to identify regions with a higher tendency to retain POPs. It is shown to vary over many orders of magnitude, between compounds, locations and time (seasonally/diurnally). The MRC approach emphasises the very large capacity of soils as a storage compartment for POPs. The theoretical MRC concept is compared to reality and its implications for the global cycling of POPs are discussed. Sharp gradients in soil MRC can exist in mountainous areas and between the land and ocean. Exchanges between oceans and land masses via the atmosphere is likely to be an important driver to the global cycling of these compounds, and net ocean-land transfers could occur in some areas.

Regression models to predict water-soil heavy metals partition coefficients in risk assessment studies.

Risk assessment studies apply fate and transport models to predict the behaviour of chemicals in the environment. The definition of physico-chemical properties is crucial to predict the mobility of pollutants and heavy metals in particular within the environmental compartments. The conservative approach normally adopted at a screening level in attributing a value to the K(d) value, results in an extremely variable mobility in soil. In this paper a regression model to estimate rapidly the K(d) for heavy metals is proposed and applied to Pb, allowing a considerable reduction (3-4 orders of magnitude) of the estimation uncertainty. The application of a stepwise forward multiple regression to literature data provided a pH-dependent regression equation of the soil-water distribution coefficient (K(d)) for Pb: log K(d)=1.99+0.42 pH.