Background Patients with chronic obstructive pulmonary disease (COPD) frequently suffer from chronic bronchitis (CB) and display steroid-resistant inflammation with increased sputum neutrophils and macrophages. Recently, a causal link between mucus hyper-concentration and disease progression of CB has been suggested. Methods In this study, we have evaluated the steroid sensitivity of purified, patient-derived sputum and alveolar macrophages and used a novel mechanistic cross-talk assay to examine how macrophages and bronchial epithelial cells cross-talk to regulate MUC5B production. Results We demonstrate that sputum plug macrophages isolated from COPD patients with chronic bronchitis (COPD/CB) are chronically activated and only partially respond to ex vivo corticosteroid treatment compared to alveolar macrophages isolated from lung resections. Further, we show that pseudo-stratified bronchial epithelial cells grown in air–liquid-interface are inert to direct bacterial lipopolysaccharide stimulation and that macrophages are able to relay this signal and activate the CREB/AP-1 transcription factor complex and subsequent MUC5B expression in epithelial cells through a soluble mediator. Using recombinant protein and neutralizing antibodies, we identified a key role for TNFα in this cross-talk. Conclusions For the first time, we describe ex vivo pharmacology in purified human sputum macrophages isolated from chronic bronchitis COPD patients and identify a possible basis for the steroid resistance frequently seen in this population. Our data pinpoint a critical role for chronically activated sputum macrophages in perpetuating TNFα-dependent signals driving mucus hyper-production. Targeting the chronically activated mucus plug macrophage phenotype and interfering with aberrant macrophage-epithelial cross-talk may provide a novel strategy to resolve chronic inflammatory lung disease.

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Adaptive cluster sampling is a design specifically developed for rare and clustered populations. Using this sampling design, we consider the case when an auxiliary variable is available in addition to the variable of interest. The use of auxiliary information has been shown to improve the efficiency of estimators although this results in asymptotically design‐unbiased estimators. Consider wildlife population in a protected area. Its distribution and abundance can partly be influenced by such factors as disease and pollution where the presence of wildlife diseases or higher environmental pollution decreases population totals and the distribution of wildlife. This paper proposes two product estimators and their associated variance estimators for the adaptive cluster sampling design to be used when the study and auxiliary variables are negatively correlated. The exact expression of the bias together with the mean square error to the first degree of approximation has been obtained. We derived the conditions under which the proposed estimators provided a more accurate estimation than the Horvitz–Thompson and Hansen–Hurwitz estimators with adaptive cluster sampling and the product estimator with simple random sampling. A simulation study was carried out to show the performance of the proposed estimators. Moreover, theoretical findings were supported by a numerical example using real data. Copyright © 2016 John Wiley & Sons, Ltd.

Plant pathologists need to manage plant diseases at low incidence levels. This needs to be performed efficiently in terms of precision, cost and time because most plant infections spread rapidly to other plants. Adaptive cluster sampling with a data-driven stopping rule (ACS*) was proposed to control the final sample size and improve efficiency of the ordinary adaptive cluster sampling (ACS) when prior knowledge of population structure is not known. This study seeks to apply the ACS* design to plant diseases at various levels of clustering and incidences levels. Results from simulation study show that the ACS* is as efficient as the ordinary ACS design at low levels of disease incidence with highly clustered diseased plants and is an efficient design compared with simple random sampling (SRS) and ordinary ACS for some highly to less clustered diseased plants with moderate to higher levels of disease incidence.