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Background: Autoimmune involvement in the pathogenesis of asthma and chronic obstructive pulmonary disease (COPD) has been proposed, and autoantibodies are a hallmark of autoimmunity. This study aimed to compare the autoantibody profiles of asthma and COPD, and the relationship between autoantibodies and features of these diseases. Methods: We recruited 110 asthma patients and 92 COPD patients for a prospective study. Six autoantibody types were evaluated: antinuclear antibody, anti-cytoplasmic antibodies, rheumatoid factor, anti-cyclic citrullinated peptide antibody, myeloperoxidase–anti-neutrophil cytoplasmic autoantibody (MPO-ANCA) and proteinase 3-ANCA. Other clinical data were also recorded concurrently. Results: An antinuclear antibody titre of ≥1:160 presented only in asthma but not in COPD (10% vs. 0%, p = 0.0002). Eosinophil counts in blood were negative predictors of antinuclear antibody in asthma. Conversely, eosinophil counts in blood and immunoglobulin-E levels of ≥100 IU/mL were positively associated with rheumatoid factor in asthma but not in COPD. There was no relationship between antinuclear antibody or rheumatoid factor and disease severity. Conclusions: It is possible that asthma tends to involve autoimmunity associated with antinuclear antibody more frequently than COPD because asthma is the more robust factor for antinuclear antibody positivity. Antinuclear antibody and rheumatoid factor are associated with eosinophilic responses, but they do not work as biomarkers for disease severity.

Objectives: Multi‑b‑value diffusion‑weighted imaging (DWI) is useful for the detection of pathological tissues. The signal decay curve of each voxel is generally analyzed using an exponential model; however, the most appropriate curve for an analysis does not necessarily follow a single function. We used model‑free analysis to classify data from the inferior alveolar nerve (IAN) into clusters according to the pattern of decay curves. This clustering should be able to classify groups of voxels representing different tissue properties in the neurovascular bundle of the IAN. Materials and Methods: DWI with eight b‑values was acquired from the IANs of 13 normal volunteers. K‑means cluster analysis was used to classify the data. Silhouette analysis was performed to define the optimum number of clusters. The suitability of single and double exponential functions was evaluated for each cluster. The fitting and spatial distributions of parameters associated with diffusion and perfusion using the double exponential model were tested. Results: The optimum number of clusters was three. The cluster that exhibited the steepest decay curve showed a higher apparent diffusion coefficient than the others, and was affected by the perfusion component. The cluster with the most gradual decay curve showed the best fit to the double exponential function, and contained the highest volume fraction of the slow diffusion component, indicating a different distribution to the other clusters. Conclusions: Clustering of DWI was accomplished without model fitting and was presumably based on the diffusivity properties of IAN, which may be influenced by microcirculation and fascicles. [ABSTRACT FROM AUTHOR]