Purpose Eosinophilic inflammation is a key component of severe asthma (SA). However, there has been no reliable serum biomarker for the eosinophilic inflammation of SA. We hypothesized that serum eosinophil-derived neurotoxin (EDN) could predict the eosinophilic inflammation of SA in adult asthmatics. Methods Severe asthmatics (n = 235), nonsevere asthmatics (n = 898), and healthy controls (n = 125) were enrolled from Ajou University Hospital, South Korea. The serum levels of EDN and periostin were measured by enzyme-linked immunosorbent assay and compared between severe and nonsevere asthmatics. Their associations with total eosinophil count (TEC) and clinical parameters were evaluated; clinical validation of the K-EDN kit for the measurement of serum EDN was evaluated. Results Severe asthmatics were older and had longer disease duration with significantly lower levels of forced expiratory volume in 1 second and methacholine PC20 than nonsevere asthmatics. Significant differences were found in TEC or sputum eosinophil count (%) between the groups. The serum levels of EDN and periostin were significantly higher in severe asthmatics than in nonsevere asthmatics and in healthy controls (all P < 0.05). Although significant correlations were found between serum EDN levels measured by the 2 kits ( ρ = 0.545, P < 0.0001), higher correlation coefficients between serum EDN levels measured by the K-EDN kit and TEC were higher ( ρ = 0.358, P < 0.0001) than those between serum EDN levels measured by the MBL kit and TEC ( ρ = 0.319, P < 0.0001) or serum periostin level ( ρ = 0.222, P < 0.0001). Multivariate regression analysis demonstrated that serum EDN levels measured by the K-EDN kit predicted the phenotype of SA ( P = 0.003), while 2 other biomarkers did not. Conclusions The serum EDN level may be a useful biomarker for assessing asthma severity in adult asthmatics.

The BioTracer™ K EDN ELISA Kit was accurate and useful in measuring EDN levels in young asthma patient serum. Because of our kit's distinct advantages and utility, we suggest this kit can be used for the timely diagnosis, treatment, and monitoring of asthma in asthma patients of all ages, especially those too young to perform pulmonary function tests.

BackgroundHuman metapneumovirus (hMPV) and respiratory syncytial virus (RSV) share some epidemiological and clinical characteristics; however, few studies have examined the mechanisms by which these viruses induce airway inflammation.ObjectiveThis study was undertaken to compare cytokine profiles in hMPV and RSV patients to investigate possible differences in inflammatory pathways.MethodsNasopharyngeal aspirate specimens were collected from 1,008 pediatric patients hospitalized for acute lower respiratory tract infection with wheezing and 20 normal healthy controls. Patients were tested for 7 common respiratory viruses then divided into hMPV (n = 35) and RSV groups (n = 67). T helper (Th) 1 (interferon [IFN]-γ), Th2 (interleukin [IL]-4, eotaxin) and Th17 (IL-1β, IL-6) cytokine profiles were analyzed in the 3 groups.ResultsIFN-γ and IL-2 levels were significantly increased in the hMPV and RSV groups compared to the control group (p < 0.0001 and p < 0.0001, respectively). IL-4 levels were significantly higher in the RSV group compared to the hMPV and control groups (p = 0.0003 and p < 0.0001, respectively). Eotaxin levels showed a tendency to be higher in the RSV group compared to the hMPV group (p = 0.0580), and significantly higher compared to the control group (p < 0.0001). IL-1β levels were significantly higher in the hMPV compared to the RSV group (p < 0.0001), and IL-6 levels were significantly higher in the hMPV group compared to the control group (p < 0.0001).ConclusionOur results suggest that hMPV and RSV have different inflammatory mechanisms. hMPV induces airway inflammation by the Th17 pathway through release of IL-1β and IL-6, whereas RSV acts through the Th2 pathway.

Background: The host tropism determinants of influenza virus, which cause changes in the host range and increase the likelihood of interaction with specific hosts, are critical for understanding the infection and propagation of the virus in diverse host species. Methods: Six types of protein sequences of influenza viral strains isolated from three classes of hosts (avian, human, and swine) were obtained. Random forest, naïve Bayes classification, and knearest neighbor algorithms were used for host classification. The Java language was used for sequence analysis programming and identifying host-specific position markers. Results: A machine learning technique was explored to derive the physicochemical properties of amino acids used in host classification and prediction. HA protein was found to play the most important role in determining host tropism of the influenza virus, and the random forest method yielded the highest accuracy in host prediction. Conserved amino acids that exhibited host-specific differences were also selected and verified, and they were found to be useful position markers for host classification. Finally, ANOVA analysis and post-hoc testing revealed that the physicochemical properties of amino acids, comprising protein sequences combined with position markers, differed significantly among hosts. Conclusion: The host tropism determinants and position markers described in this study can be used in related research to classify, identify, and predict the hosts of influenza viruses that are currently susceptible or likely to be infected in the future.

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