CD8 positive T cells express IL-17 in patients with chronic obstructive pulmonary disease

<p>Abstract</p> <p>Background</p> <p>Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible chronic inflammatory disease of the lung. The nature of the immune reaction in COPD raises the possibility that IL-17 and related cytokines may contribute to this disorder. This study analyzed the expression of IL-17A and IL-17F as well as the phenotype of cells producing them in bronchial biopsies from COPD patients.</p> <p>Methods</p> <p>Bronchoscopic biopsies of the airway were obtained from 16 COPD subjects (GOLD stage 1-4) and 15 control subjects. Paraffin sections were used for the investigation of IL-17A and IL-17F expression in the airways by immunohistochemistry, and frozen sections were used for the immunofluorescence double staining of IL-17A or IL-17F paired with CD4 or CD8. In order to confirm the expression of IL-17A and IL-17F at the mRNA level, a quantitative RT-PCR was performed on the total mRNA extracted from entire section or CD8 positive cells selected by laser capture microdissection.</p> <p>Results</p> <p>IL-17F immunoreactivity was significantly higher in the bronchial biopsies of COPD patients compared to control subjects (<it>P </it>< 0.0001). In the submucosa, the absolute number of both IL-17A and IL-17F positive cells was higher in COPD patients (<it>P </it>< 0.0001). After adjusting for the total number of cells in the submucosa, we still found that more cells were positive for both IL-17A (<it>P </it>< 0.0001) and IL-17F (<it>P </it>< 0.0001) in COPD patients compared to controls. The mRNA expression of IL-17A and IL-17F in airways of COPD patients was confirmed by RT-PCR. The expression of IL-17A and IL-17F was co-localized with not only CD4 but also CD8, which was further confirmed by RT-PCR on laser capture microdissection selected CD8 positive cells.</p> <p>Conclusion</p> <p>These findings support the notion that Th17 cytokines could play important roles in the pathogenesis of COPD, raising the possibility of using this mechanism as the basis for novel therapeutic approaches.</p

Expression of the T Helper 17-Associated Cytokines IL-17A and IL-17F in Asthma and COPD

BACKGROUND: Asthma and COPD are characterized by airway dysfunction and inflammation. Neutrophilic airway inflammation is a common feature of COPD and is recognized in asthma, particularly in severe disease. The T helper (Th) 17 cytokines IL-17A and IL-17F have been implicated in the development of neutrophilic airway inflammation, but their expression in asthma and COPD is uncertain. METHODS: We assessed IL-17A and IL-17F expression in the bronchial submucosa from 30 subjects with asthma, 10 ex-smokers with mild to moderate COPD, and 27 nonsmoking and 14 smoking control subjects. Sputum IL-17 concentration was measured in 165 subjects with asthma and 27 with COPD. RESULTS: The median (interquartile range) IL-17A cells/mm² submucosa was increased in mild to moderate asthma (2.1 [2.4]) compared with healthy control subjects (0.4 [2.8]) but not in severe asthma (P = .04). In COPD, IL-17A(+) cells/mm² submucosa were increased (0.5 [3.7]) compared with nonsmoking control subjects (0 [0]) but not compared with smoking control subjects (P = .046). IL-17F(+) cells/mm² submucosa were increased in severe asthma (2.7 [3.6]) and mild to moderate asthma (1.6 [1.0]) compared with healthy controls subjects (0.7 [1.4]) (P = .001) but was not increased in subjects with COPD. IL-17A and IL-17F were not associated with increased neutrophilic inflammation, but IL-17F was correlated with the submucosal eosinophil count (rs = 0.5, P = .005). The sputum IL-17 concentration in COPD was increased compared with asthma (2 [0-7] pg/mL vs 0 [0-2] pg/mL, P &lt; .0001) and was correlated with post-bronchodilator FEV₁% predicted (r = -0.5, P = .008) and FEV(1)/FVC (r = -0.4, P = .04). CONCLUSIONS: Our findings support a potential role for the Th17 cytokines IL-17A and IL-17F in asthma and COPD, but do not demonstrate a relationship with neutrophilic inflammation

Stochastic continuous time growth models that allow for closed form solutions

We find a closed form solution that maximises the expected utility of an agent’s inter-temporal consumption subject to a stochastic technology, which is a linear combination of AK and Cobb–Douglas technologies. Additionally, we consider two cases of agent preferences: (i) Constant Relative Risk Aversion (CRRA) preferences, which treat optimal consumption as a linear function of capital, and (ii) Hyperbolic Absolute Risk Aversion (HARA) preferences, which treat optimal consumption as an affine function of capital. By establishing a minimum (subsistence) level of consumption in the HARA model, we are able to create a framework that more accurately represents real-world circumstances than previous studies have done. Furthermore, for both the CRRA and HARA cases we show the suitable, consistent stochastic differential equation which describes the capital dynamics. Finally, we perform a numerical simulation based on the CRRA case and calibrate US data for the HARA case

The revival of the Gini importance?

MOTIVATION: Random forests are fast, flexible and represent a robust approach to analyze high dimensional data. A key advantage over alternative machine learning algorithms are variable importance measures, which can be used to identify relevant features or perform variable selection. Measures based on the impurity reduction of splits, such as the Gini importance, are popular because they are simple and fast to compute. However, they are biased in favor of variables with many possible split points and high minor allele frequency. RESULTS: We set up a fast approach to debias impurity-based variable importance measures for classification, regression and survival forests. We show that it creates a variable importance measure which is unbiased with regard to the number of categories and minor allele frequency and almost as fast as the standard impurity importance. As a result, it is now possible to compute reliable importance estimates without the extra computing cost of permutations. Further, we combine the importance measure with a fast testing procedure, producing p-values for variable importance with almost no computational overhead to the creation of the random forest. Applications to gene expression and genome-wide association data show that the proposed method is powerful and computationally efficient

Suitability of Sludge Biotic Index (SBI), Sludge Index (SI) and filamentous bacteria analysis for assessing activated sludge process performance: the case of piggery slaughterhouse wastewater

Piggery slaughterhouse wastewater poses serious issues in terms of disposal feasibility and environmental impact, due to its huge organic load and variability. It is commonly treated by means of activated sludge processes, whose performance, in case of municipal wastewater, can be monitored by means of specific analyses, such as Sludge Biotic Index (SBI), Sludge Index (SI) and floc and filamentous bacteria observation. Therefore, this paper was aimed at assessing the applicability of these techniques to piggery slaughterhouse sewage. A plant located in Northern Italy was monitored for 1 year. Physical, chemical and operation parameters were measured; the activated sludge community (ciliates, flagellates, amoebae and small metazoa) was analysed for calculating SBI and SI. Floc and filamentous bacteria were examined and described accordingly with internationally adopted criteria. The results showed the full applicability of the studied techniques for optimizing the operation of a piggery slaughterhouse wastewater treatment plant

Assessing the gap between a normative and a reality-based model of building LCA

Recognized as a powerful methodology for the evaluation of environmental burdens, life cycle assessment (LCA) must be performed with close-to-reality inputs to be robust and accurate. However, the necessary real-world data is hardly available at the design stage, resulting in current LCA practice being mainly based on standards and norms as hypothesis of building contexts, therefore inducing uncertainties in results. The current paper presents a methodology to collect a subset of such input data in a function of the context more accurately than the standards. It then studies the impact of such uncertainties in the LCA results. Through an academic building case study that measured data concerning the building occupancy (A), i.e. the hourly occupancy rate and density, the hourly appliance consumption rate (B), and the hourly conversion factors of environmental impact of the electricity mix (C), the LCA results for the GHG emissions and primary energy consumption are compared between normative- and measurements-based input parameters. The measured occupancy rate (A) is shown to impact the LCA results the most, especially the embedded impacts, by implying a new occupancy density: the building population increase of +32% leads to a significant increase of the embedded impacts related to furniture. The variability in appliance usage (B) is marginal between measures and standards and therefore does not lead to a significant change in LCA results. The use of hourly conversion factors (C) indicates an underestimation of the GHG emissions and, at the same time, an overestimation of the primary energy when assessed with mean annual values. The combined effect of simultaneously using the three reality-based input parameters (A, B, C) mostly affects the non-renewable part of the cumulative energy demand indicator (-9% reduction of the operational part), followed by the cumulative energy demand (-7%) and GHG emissions indicators (-3%). The research findings affect not only LCA research but also practitioners such as architects or building contractors who need to respect ambitious environmental targets

The revival of the Gini importance?

MOTIVATION: Random forests are fast, flexible and represent a robust approach to analyze high dimensional data. A key advantage over alternative machine learning algorithms are variable importance measures, which can be used to identify relevant features or perform variable selection. Measures based on the impurity reduction of splits, such as the Gini importance, are popular because they are simple and fast to compute. However, they are biased in favor of variables with many possible split points and high minor allele frequency. RESULTS: We set up a fast approach to debias impurity-based variable importance measures for classification, regression and survival forests. We show that it creates a variable importance measure which is unbiased with regard to the number of categories and minor allele frequency and almost as fast as the standard impurity importance. As a result, it is now possible to compute reliable importance estimates without the extra computing cost of permutations. Further, we combine the importance measure with a fast testing procedure, producing p-values for variable importance with almost no computational overhead to the creation of the random forest. Applications to gene expression and genome-wide association data show that the proposed method is powerful and computationally efficient