BackgroundInterleukin 13 (IL13) is a T-helper type 2 (Th2) cytokine associated with inflammation and pathology in allergic diseases such as bronchial asthma. We have shown that treatment with lebrikizumab, an anti-IL13 monoclonal antibody, significantly improves prebronchodilator forced expiratory volume in 1 s (FEV1) in a subset of subjects with uncontrolled asthma.ObjectiveTo evaluate efficacy and safety of lebrikizumab in subjects with mild asthma who underwent bronchial allergen challenge.MethodsTwenty-nine subjects were randomized 1: 1–5 mg/kg lebrikizumab (n = 13) or placebo (n = 16) administered subcutaneously every 4 weeks over 12 weeks, a total of four doses. Primary efficacy outcome was late asthmatic response (LAR) at Week 13, defined as area under the curve of FEV1 measured 2–8 h following inhaled allergen challenge. Serum biomarkers were measured to verify IL13 pathway inhibition and identify patients with an increased response to lebrikizumab.ResultsAt Week 13, the LAR in lebrikizumab subjects was reduced by 48% compared with placebo subjects, although this was not statistically significant (95% confidence interval, −19%, 90%). Exploratory analysis indicated that lebrikizumab-treated subjects with elevated baseline levels of peripheral blood eosinophils, serum IgE, or periostin exhibited a greater reduction in LAR compared with subjects with lower baseline levels of these biomarkers. Lebrikizumab exerted systemic effects on markers of Th2 inflammation, reducing serum immunoglobulin E (IgE), chemokine ligands 13 and 17 by approximately 25% (P < 0.01). Lebrikizumab was well tolerated.Conclusion and Clinical RelevanceLebrikizumab reduced the LAR in subjects with mild asthma. Clinical trial number NCT00781443.
Objective: We aim to study to what extent conventional and deep-learning-based T 2 relaxometry patterns are able to distinguish between knees with and without radiographic osteoarthritis (OA). Methods: T 2 relaxation time maps were analyzed for 4,384 subjects from the baseline Osteoarthritis Initiative (OAI) Dataset. Voxel Based Relaxometry (VBR) was used for automatic quantification and voxelbased analysis of the differences in T 2 between subjects with and without radiographic OA. A Densely Connected Convolutional Neural Network (DenseNet) was trained to diagnose OA from T 2 data. For comparison, more classical feature extraction techniques and shallow classifiers were used to benchmark the performance of our algorithm's results. Deep and shallow models were evaluated with and without the inclusion of risk factors. Sensitivity and Specificity values and McNemar test were used to compare the performance of the different classifiers. Results: The best shallow model was obtained when the first ten Principal Components, demographics and pain score were included as features (AUC ¼ 77.77%, Sensitivity ¼ 67.01%, Specificity ¼ 71.79%). In comparison, DenseNet trained on raw T 2 data obtained AUC ¼ 83.44%, Sensitivity ¼ 76.99%, Specificity ¼ 77.94%. McNemar test on two misclassified proportions form the shallow and deep model showed that the boost in performance was statistically significant (McNemar's chi-squared ¼ 10.33, degree of freedom (DF) ¼ 1, P-value ¼ 0.0013). Conclusion: In this study, we presented a Magnetic Resonance Imaging (MRI)-based data-driven platform using T 2 measurements to characterize radiographic OA. Our results showed that feature learning from T 2 maps has potential in uncovering information that can potentially better diagnose OA than simple averages or linear patterns decomposition.
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