BackgroundAsthma is characterized by a heterogeneous inflammatory profile and can be subdivided into T(h)2-high and T(h)2-low airway inflammation. Profiling of a broader panel of airway cytokines in large unselected patient cohorts is lacking.MethodsPatients (n = 205) were defined as being “cytokine-low/high” if sputum mRNA expression of a particular cytokine was outside the respective 10th/90th percentile range of the control group (n = 80). Unsupervised hierarchical clustering was used to determine clusters based on sputum cytokine profiles.ResultsHalf of patients (n = 108; 52.6%) had a classical T(h)2-high (“IL-4-, IL-5- and/or IL-13-high”) sputum cytokine profile. Unsupervised cluster analysis revealed 5 clusters. Patients with an “IL-4- and/or IL-13-high” pattern surprisingly did not cluster but were equally distributed among the 5 clusters. Patients with an “IL-5-, IL-17A-/F- and IL-25- high” profile were restricted to cluster 1 (n = 24) with increased sputum eosinophil as well as neutrophil counts and poor lung function parameters at baseline and 2 years later. Four other clusters were identified: “IL-5-high or IL-10-high” (n = 16), “IL-6-high” (n = 8), “IL-22-high” (n = 25). Cluster 5 (n = 132) consists of patients without “cytokine-high” pattern or patients with only high IL-4 and/or IL-13.ConclusionWe identified 5 unique asthma molecular phenotypes by biological clustering. Type 2 cytokines cluster with non-type 2 cytokines in 4 out of 5 clusters. Unsupervised analysis thus not supports a priori type 2 versus non-type 2 molecular phenotypes. www.clinicaltrials.gov NCT01224938. Registered 18 October 2010.Electronic supplementary materialThe online version of this article (doi:10.1186/s12931-017-0524-y) contains supplementary material, which is available to authorized users.
To compare the prognostic value and reproducibility of visual versus AI-assisted analysis of lung involvement on submillisievert low-dose chest CT in COVID-19 patients. Materials and Methods: This was a HIPAA-compliant, institutional review board-approved retrospective study. From March 15 to June 1, 2020, 250 RT-PCR confirmed COVID-19 patients were studied with low-dose chest CT at admission. Visual and AI-assisted analysis of lung involvement was performed by using a semi-quantitative CT score and a quantitative percentage of lung involvement. Adverse outcome was defined as intensive care unit (ICU) admission or death. Cox regression analysis, Kaplan-Meier curves, and cross-validated receiver operating characteristic curve with area under the curve (AUROC) analysis was performed to compare model performance. Intraclass correlation coefficients (ICCs) and Bland-Altman analysis was used to assess intra-and interreader reproducibility. Results: Adverse outcome occurred in 39 patients (11 deaths, 28 ICU admissions). AUC values from AI-assisted analysis were significantly higher than those from visual analysis for both semiquantitative CT scores and percentages of lung involvement (all P<0.001). Intrareader and interreader agreement rates were significantly higher for AI-assisted analysis than visual analysis (all ICC 0.960 versus 0.885). AI-assisted variability for quantitative percentage of lung involvement was 17.2% (coefficient of variation) versus 34.7% for visual analysis. The sample size to detect a 5% change in lung involvement with 90% power and an error of 0.05 was 250 patients with AI-assisted analysis and 1014 patients with visual analysis. Conclusion: AI-assisted analysis of lung involvement on submillisievert low-dose chest CT outperformed conventional visual analysis in predicting outcome in COVID-19 patients while I n p r e s s 3 reducing CT variability. Lung involvement on chest CT could be used as a reliable metric in future clinical trials.
S. pneumoniae is the world’s foremost bacterial pathogen. S. pneumoniae encodes a phasevarion (phase-variable regulon), that results in differential expression of multiple genes. Previous work demonstrated that the pneumococcal SpnIII phasevarion switches between six different expression states, generating six unique phenotypic variants in a pneumococcal population.
A novel approach to human parainfluenza virus 3 (hPIV-3) inhibitor design has been evaluated by targeting an unexplored pocket within the active site region of the hemagglutinin-neuraminidase (HN) of the virus that is normally occluded upon ligand engagement. To explore this opportunity, we developed a highly efficient route to introduce nitrogen-based functionalities at the naturally unsubstituted C-3 position on the neuraminidase inhibitor template N-acyl-2,3-dehydro-2-deoxy-neuraminic acid ( N-acyl-Neu2en), via a regioselective 2,3-bromoazidation. Introduction of triazole substituents at C-3 on this template provided compounds with low micromolar inhibition of hPIV-3 HN neuraminidase activity, with the most potent having 48-fold improved potency over the corresponding C-3 unsubstituted analogue. However, the C-3-triazole N-acyl-Neu2en derivatives were significantly less active against the hemagglutinin function of the virus, with high micromolar IC values determined, and showed insignificant in vitro antiviral activity. Given the different pH optima of the HN protein's neuraminidase (acidic pH) and hemagglutinin (neutral pH) functions, the influence of pH on inhibitor binding was examined using X-ray crystallography and STD NMR spectroscopy, providing novel insights into the multifunctionality of hPIV-3 HN. While the 3-phenyltriazole- N-isobutyryl-Neu2en derivative could bind HN at pH 4.6, suitable for neuraminidase inhibition, at neutral pH binding of the inhibitor was substantially reduced. Importantly, this study clearly demonstrates for the first time that potent inhibition of HN neuraminidase activity is not necessarily directly correlated with a strong antiviral activity, and suggests that strong inhibition of the hemagglutinin function of hPIV HN is crucial for potent antiviral activity. This highlights the importance of designing hPIV inhibitors that primarily target the receptor-binding function of hPIV HN.
Two groups of volunteers were examined with regard to the threshold level of a maxillary incisor for axialIy applied forces. One group showed an advanced periodontilis around the examined tooth, while the other group presented no clinical periodontal pathology. The ascending method of limits was used to determine the psychophysical threshold level. The relationship between the threshold level and the force application rate appeared to be linear on a lin‐log and a log‐log display. Higher rates gave lower threshold levels. For equal rales, the threshold level of the periodontilis group was significantly higher than for the control group. The results indicate that the periodontal mechanoreceptors are sensitive to the rate of force application, whatever the periodontal condition. Advanced periodontitis. however, clearly impairs the tactile function of the tooth supporting tissues.
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