Influence of peptidylarginine deiminase type 4 genotype and shared epitope on clinical characteristics and autoantibody profile of rheumatoid arthritis.

Background: Recent evidence suggests that distinction of subsets of rheumatoid arthritis (RA) depending on anticyclic citrullinated peptide antibody (anti-CCP) status may be helpful in distinguishing distinct aetiopathologies and in predicting the course of disease. HLA-DRB1 shared epitope (SE) and peptidylarginine deiminase type 4 (PADI4) genotype, both of which have been implicated in anti-CCP generation, are assumed to be associated with RA. Objectives: To elucidate whether PADI4 affects the clinical characteristics of RA, and whether it would modulate the effect of anti-CCPs on clinical course. The combined effect of SE and PADI4 on autoantibody profile was also analysed. Methods: 373 patients with RA were studied. SE, padi4_94C.T, rheumatoid factor, anti-CCPs and antinuclear antibodies (ANAs) were determined. Disease severity was characterised by cumulative therapy intensity classified into ordinal categories (CTI-1 to CTI-3) and by Steinbrocker score. Results: CTI was significantly associated with disease duration, erosive disease, disease activity score (DAS) 28 and anti-CCPs. The association of anti-CCPs with CTI was considerably influenced by padi4_94C.T genotype (C/C: ORadj=0.93, padj=0.92; C/T: ORadj=2.92, padj=0.093; T/T: ORadj=15.3, padj=0.002). Carriage of padi4_94T exhibited a significant trend towards higher Steinbrocker scores in univariate and multivariate analyses. An association of padi4_94C.T with ANAs was observed, with noteworthy differences depending on SE status (SE2: ORadj=6.20, padj,0.04; SE+: ORadj=0.36, padj=0.02) and significant heterogeneity between the two SE strata (p=0.006). Conclusions: PADI4 genotype in combination with anti- CCPs and SE modulates clinical and serological characteristics of RA

MatricS—A novel tool for monitoring professional role development in surgical disciplines

Introduction: Mentoring is an effective method for human resource development. Monitoring the process is important for individual mentee/mentor pairs as well as for program directors. Due to individual personality differences of both mentees and mentors and their respective interactions, it is challenging to monitor the individual development process of mentees in a structured manner. This study investigates to what extent a novel instrument, the mentee-based assessment tool for role development of interpersonal competencies in surgical professions (MatricS) can adequately monitor the professional role development process of residents during an established mentoring program. Material and methods: In a prospective longitudinal study, the competence development of 31 mentees in two subsequent cohorts was assessed by a modified role matrix based on Canadian Medical Education Directives for Specialists. The evaluation focused on three defined roles (D, developer; N, networker; M, multiplicator) at three levels (private, employer-related, national/international) with four stages of development. For validation of mentee self-assessments, the assessments of the respective mentors were recorded alongside. For correlation analyses, Pearson coefficients were calculated, pre-post-comparisons were done by paired t-tests; significance was assumed at p < 0.05, respectively. Results: Mentee self-assessments overall correlated well with the objective mentor assessments (Pearson's r 0.8, p 75% of all roles and levels. Conclusion: The role development process during mentoring can be reliably monitored by using MatricS. MatricS scores highly correlate between mentees and mentors, indicating that mentee self-assessments are suitable and sufficient for monitoring. These findings help to lessen the work burden on senior surgeons and thus can help to increase the acceptance of mentoring programs in surgical disciplines

Modelling street level PM10 concentrations across Europe: source apportionment and possible futures

Despite increasing emission controls, particulate matter (PM) has remained a critical issue for European air quality in recent years. The various sources of PM, both from primary particulate emissions as well as secondary formation from precursor gases, make this a complex problem to tackle. In order to allow for credible predictions of future concentrations under policy assumptions, a modelling approach is needed that considers all chemical processes and spatial dimensions involved, from long-range transport of pollution to local emissions in street canyons. Here we describe a modelling scheme which has been implemented in the GAINS integrated assessment model to assess compliance with PM10 (PM with aerodynamic diameter <10 um) limit values at individual air quality monitoring stations reporting to the AirBase database. The modelling approach relies on a combination of bottom up modelling of emissions, simplified atmospheric chemistry and dispersion calculations, and a traffic increment calculation wherever applicable. At each monitoring station fulfilling a few data coverage criteria, measured concentrations in the base year 2009 are explained to the extent possible and then modelled for the past and future. More than 1850 monitoring stations are covered, including more than 300 traffic stations and 80% of the stations which exceeded the EU air quality limit values in 2009. As a validation, we compare modelled trends in the period 2000-2008 to observations, which are well reproduced. The modelling scheme is applied here to quantify explicitly source contributions to ambient concentrations at several critical monitoring stations, displaying the differences in spatial origin and chemical composition of urban roadside PM10 across Europe. Furthermore, we analyse the predicted evolution of PM10 concentrations in the European Union until 2030 under different policy scenarios. Significant improvements in ambient PM10 concentrations are expected assuming successful implementation of already agreed legislation; however, these will not be large enough to ensure attainment of PM10 limit values in hot spot locations such as Southern Poland and major European cities. Remaining issues are largely eliminated in a scenario applying the best available emission control technologies to the maximal technically feasible extent

Potentials for future reductions of global GHG and air pollutant emissions from circular municipal waste management systems

Recent trajectories of production and consumption patterns have resulted in massively rising quantities of municipal solid waste (MSW). Building on the Shared Socioeconomic Pathways, we build two sets of global scenarios until 2050, namely baseline and mitigation scenarios. We assess trajectories of future MSW generation and the impact of MSW management strategies on methane and air pollutant emissions. In 2050, the adoption of mitigation strategies in the sustainability-oriented scenario yields earlier, and major, co-benefits compared to scenarios in which inequalities are reduced but that are focused solely on technical solutions. In 2050, the GHG emissions in the sustainability-oriented scenario amount to 182 Gg CO2eq/yr of CH4, to be released while particulate matter, and air pollutants from open burning of MSW can be virtually eliminated. We demonstrate that the 6.3 target of the SDG 6 can only be achieved through more ambitious sustainability-oriented scenarios that limit MSW generation and improve management

A system dynamics-based scenario analysis of residential solid waste management in Kisumu, Kenya

The problem of solid waste management presents an issue of increasing importance in many low-income settings, including the progressively urbanised context of Kenya. Kisumu County is one such setting with an estimated 500 t of waste generated per day and with less than half of it regularly collected. The open burning and natural decay of solid waste is an important source of greenhouse gas (GHG) emissions and atmospheric pollutants with adverse health consequences. In this paper, we use system dynamics modelling to investigate the expected impact on GHG and PM_{2.5} emissions of (i) a waste-to-biogas initiative and (ii) a regulatory ban on the open burning of waste in landfill. We use life tables to estimate the impact on mortality of the reduction in PM_{2.5} exposure. Our results indicate that combining these two interventions can generate over 1.1 million tonnes of cumulative savings in GHG emissions by 2035, of which the largest contribution (42%) results from the biogas produced replacing unclean fuels in household cooking. Combining the two interventions is expected to reduce PM_{2.5} emissions from the waste and residential sectors by over 30% compared to our baseline scenario by 2035, resulting in at least around 1150 cumulative life years saved over 2021–2035. The contribution and novelty of this study lies in the quantification of a potential waste-to-biogas scenario and its environmental and health impact in Kisumu for the first time

Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions

© 2019, The Author(s). In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions

Replacing the Draize eye test: impedance spectroscopy as a 3R method to discriminate between all GHS categories for eye irritation

Highly invasive animal based test procedures for risk assessment such as the Draize eye test are under increasing criticism due to poor transferability for the human organism and animal-welfare concerns. However, besides all efforts, the Draize eye test is still not completely replaced by alternative animal-free methods. To develop an in vitro test to identify all categories of eye irritation, we combined organotypic cornea models based on primary human cells with an electrical readout system that measures the impedance of the test models. First, we showed that employing a primary human cornea epithelial cell based model is advantageous in native marker expression to the primary human epidermal keratinocytes derived models. Secondly, by employing a non-destructive measuring system based on impedance spectroscopy, we could increase the sensitivity of the test system. Thereby, all globally harmonized systems categories of eye irritation could be identified by repeated measurements over a period of 7 days. Based on a novel prediction model we achieved an accuracy of 78% with a reproducibility of 88.9% to determine all three categories of eye irritation in one single test. This could pave the way according to the 3R principle to replace the Draize eye test

Quantifying national household air pollution (HAP) exposure to PM2.5 in rural and urban areas

According to WHO (World Health Organization), in 2020, 14% of people in global urban areas relied on polluting solid fuels and technologies, compared with 52% of the rural population. The health impacts of such inequality are massive. It was estimated that 3.2 million premature deaths per year (2020), particularly in low-income and middle-income countries due to household air pollution (HAP). Several studies provide estimates of the exposure to fine particulate matter (PM2.5) from household air pollution (HAP-PM2.5) for users of different fuel/cookstove types in rural and urban areas. However, hardly any studies estimate the population-weighted exposure to HAP-PM2.5 at the global scale. A Bayesian hierarchical model was developed to estimate PM2.5 exposure coefficients and their uncertainties for an annual average of HAP-PM2.5 personal exposure. The predicted HAP-PM2.5 exposure at the user level was used to estimate the national-level exposure for the population living in urban and rural areas. The results suggest that switching from polluting solid fuels (biomass, charcoal, coal) to cleaner fuels (gas and electricity) for heating and cooking can potentially reduce the national-level HAP-PM2.5 personal exposure on average by 53%. However, there exists a significant disparity between rural and urban areas, partly reflecting inequality in energy access. More specifically, switching from polluting solid fuels for heating and cooking to cleaner fuels can reduce the personal exposure to HAP-PM2.5 in rural areas by 54% and in urban areas by 38%. The study indicates that increased access to clean fuels and improved stove interventions are needed to achieve the goals of universal energy access and equality between urban and rural areas

Urban–rural disparity in global estimation of PM2·5 household air pollution and its attributable health burden

Background: Polluting fuels and inefficient stove technologies are still a leading cause of premature deaths worldwide, particularly in low-income and middle-income countries. Previous studies of global household air pollution (HAP) have neither considered the estimation of PM2·5 at national level nor the corresponding attributable mortality burden. Additionally, the effects of climate and ambient air pollution on the global estimation of HAP-PM2·5 exposure for different urban and rural settings remain largely unknown. In this study, we include climatic effects to estimate the HAP-PM2·5 exposure from different fuel types and stove technologies in rural and urban settings separately and the related attributable global mortality burden. Methods: Bayesian hierarchical models were developed to estimate an annual average HAP-PM2·5 personal exposure and HAP-PM2·5 indoor concentration (including both outdoor and indoor sources). Model variables were selected from sample data in 282 peer-reviewed studies drawn and updated from the WHO Global HAP dataset. The PM2·5 exposure coefficients from the developed model were applied to the external datasets to predict the HAP-PM2·5 exposure globally (personal exposure in 62 countries and indoor concentration in 69 countries). Attributable mortality rate was estimated using a comparative risk assessment approach. Using weighted averages, the national level 24 h average HAP-PM2·5 exposure due to polluting and clean fuels and related death rate per 100 000 population were estimated. Findings: In 2020, household use of polluting solid fuels for cooking and heating led to a national-level average personal exposure of 151 μg/m3 (95% CI 133-169), with rural households having an average of 171 μg/m3 (153-189) and urban households an average of 92 μg/m3 (77-106). Use of clean fuels gave rise to a national-level average personal exposure of 69 μg/m3 (62-76), with a rural average of 76 μg/m3 (69-83) and an urban average of 49 μg/m3 (46-53). Personal exposure-attributable premature mortality (per 100 000 population) from the use of polluting solid fuels at national level was on average 78 (95% CI 69-87), with a rural average of 82 (73-90) and an urban average of 66 (57-75). The average attributable premature mortality (per 100 000 population) from the use of clean fuels at the national level is 62 (54-70), with a rural average of 66 (58-74) and an urban average of 52 (47-57). The estimated HAP-PM2·5 indoor concentration shows that the use of polluting solid fuels resulted in a national-level average of 412 μg/m3 (95% CI 353-471), with a rural average of 514 μg/m3 (446-582) and an urban average of 149 μg/m3 (126-173). The use of clean fuels (gas and electricity) led to an average PM2·5 indoor concentration of 135 μg/m3 (117-153), with a rural average of 174 μg/m3 (154-195) and an urban average of 71 μg/m3 (63-80). Using time-weighted HAP-PM2·5 indoor concentrations, the attributable premature death rate (per 100 000 population) from the use of polluting solid fuels at the national level is on average 78 (95% CI 72-84), the rural average being 84 (78-91) and the urban average 60 (54-66). From the use of clean fuels, the average attributable premature death rate (per 100 000 population) at the national level is 59 (53-64), the rural average being 68 (62-74) and the urban average 45 (41-50). Interpretation: A shift from polluting to clean fuels can reduce the average PM2·5 personal exposure by 53% and thereby lower the death rate. For all fuel types, the estimated average HAP-PM2·5 personal exposure and indoor concentrations exceed the WHO's Interim Target-1 average annual threshold. Policy interventions are urgently needed to greatly increase the use of clean fuels and stove technologies by 2030 to achieve the goal of affordable clean energy access, as set by the UN in 2015, and address health inequities in urban-rural settings. Funding: Wellcome Trust, The Lancet Countdown, the Eng